Search

Abstract Objective One of the important proteins involved in lipid metabolism is the ATP-binding cassette transporter A1 (ABCA1) encoding by ABCA1 gene. In this study we evaluated the single nucleotide polymorphisms (SNPs) of ABCA1 gene. We analyzed SNPs in chromosome 9 such as rs2230806 (R219K) in the position 107620867, rs2230808 (R1587K) in the position 106602625 and rs4149313 (I883M) in the position 106626574 according to gender and lipid profile of Greek nurses. Methods The study population consisted of 447 (87 men) unrelated nurses who were genotyped for ABCA1 gene polymorphisms. Additionally, lipid profile [total cholesterol, triglycerides, high density lipoprotein cholesterol, low density lipoprotein cholesterol (LDL-C) and apolipoprotein A1] was evaluated. Results The distribution of all three studied ABCA1 gene polymorphisms did not differ according to gender. However, only R219K genotype distribution bared borderline statistical significance (p = 0.08) between the two studied groups. Moreover, allele frequencies of R219K, R1587K and I88M polymorphisms did not differ according to gender. In general, blood lipid levels did not seem to vary according to ABCA1 gene polymorphisms, when testing all subjects or when testing only men or only women. However, a significant difference of LDL-C distribution was detected in all subjects according to R1587K genotype, indicating lower LDL-C levels with KK polymorphism (p = 0.0025). The above difference was solely detected on female population (p = 0.0053). Conclusions The ABCA1 gene polymorphisms frequency, distribution and lipid profile did not differ according to gender. However, in the female population the KK genotype of R1587K gene indicated lower LDL-C levels. Further studies, involving a higher number of individuals, are required to clarify genes and gender contribution.

Abstract Background The cholesteryl ester transfer protein (CETP) has a central role in the lipid metabolism and therefore may alter the susceptibility to atherosclerosis. Methods The DNA of 471 subjects [133 subjects with angiographically documented left main coronary artery disease (LMCAD), 241 subjects with more peripheral coronary artery disease (MPCAD) and 97 subjects self reported healthy (Controls)] was analyzed for the frequency of TaqIB and I405V polymorphisms in the gene coding CETP. Results There is no significant difference in CETP allele frequency or genotype distribution among LMCAD and MPCAD patients although there is statistical difference between LMCAD and Controls (p = 0.001). Specifically, patients with LMCAD and B1B1 genotype of TaqIB polymorphism were more frequent present compared to Controls (33.8% vs 22.9%, respectively). The frequency of B2B2 genotype was 3 times lower in the LMCAD group compared to Controls (10.5% vs 30.2%, respectively). In the LMCAD group the frequency of B1 allele compared to Controls was higher (62% vs 46%, respectively, p = 0.001). The relationship between TaqIB gene polymorphism and the LMCAD was independent of lipid profile, with the exception of apolipoprotein A. Conclusions These findings indicate that the TaqIB polymorphism may have potential importance in screening individuals at high risk for developing CAD. However, this polymorphism cannot distinguish between LMCAD and MPCAD. Further prospective investigations in larger populations are required to confirm these findings.

Abstract Background In patients with Duchenne Muscular Dystrophy (DMD), the absent or diminished dystrophin leads to progressive skeletal muscle and heart failure. We evaluated the role of myocardial inflammation as a precipitating factor in the development of heart failure in DMD. Methods 20 DMD patients (aged 15-18 yrs) and 20 age-matched healthy volunteers were studied and followed-up for 2 years. Evaluation of myocarditis with cardiovascular magnetic resonance imaging (CMR) was performed using STIR T2-weighted (T2W), T1-weighted (T1W) before and after contrast media and late enhanced images (LGE). Left ventricular volumes and ejection fraction were also calculated. Myocardial biopsy was performed in patients with positive CMR and immunohistologic and polymerase chain reaction (PCR) analysis was employed. Results In DMD patients, left ventricular end-diastolic volume (LVEDV) was not different compared to controls. Left ventricular end-systolic volume (LVESV) was higher (45.1 ± 6.6 vs. 37.3 ± 3.8 ml, p < 0.001) and left ventricular ejection fraction (LVEF) was lower (53.9 ± 2.1 vs. 63 ± 2.4%, p < 0.001). T2 heart/skeletal muscle ratio and early T1 ratio values in DMD patients presented no difference compared to controls. LGE areas were identified in six DMD patients. In four of them with CMR evidence of myocarditis, myocardial biopsy was performed. Active myocarditis was identified in one and healing myocarditis in three using immunohistology. All six patients with CMR evidence of myocarditis had a rapid deterioration of left ventricular function during the next year. Conclusions DMD patients with myocardial inflammation documented by CMR had a rigorous progression to heart failure.

Purpose: The difference between rest and peak stress end-systolic pressure-volume relation (ΔESPVR) is an afterload-independent index of left ventricular (LV) contractility. We assessed the independent prognostic value of ΔESPVR index by dipyridamole stress-cardiovascular magnetic resonance (CMR) in patients with known/suspected coronary artery disease (CAD)., Methods: We considered 196 consecutive patients (62.74 ± 10.66 years, 49 females). Wall motion and perfusion abnormalities at rest and peak stress were analysed. Replacement myocardial fibrosis was detected by late gadolinium enhancement (LGE) technique. The ESPVR was evaluated at rest and peak stress from raw measurement of systolic arterial pressure and end-systolic volume by biplane Simpson's method., Results: A reduced ΔESPVR index (≤ 0.02 mmHg/mL/m2) was found in 88 (44.9%) patients and it was associated with a lower LV ejection fraction (EF) and with a higher frequency of abnormal stress CMR and myocardial fibrosis. During a mean follow-up of 53.17 ± 28.21 months, 50 (25.5%) cardiac events were recorded: 5 cardiac deaths, 17 revascularizations, one myocardial infarction, 23 hospitalisations for heart failure or unstable angina, and 4 ventricular arrhythmias. According to Cox regression analysis, diabetes, family history, LVEF, abnormal stress CMR, myocardial fibrosis, and reduced ΔESPVR were significant univariate prognosticators. In the multivariate analysis the independent predictors were ΔESPVR index ≤ 0.02 mmHg/mL/m2 (hazard ratio-HR = 2.58, P = 0.007), myocardial fibrosis (HR = 2.13, P = 0.036), and diabetes (HR = 2.33, P = 0.012)., Conclusion: ΔESPVR index by stress-CMR was independently associated with cardiac outcomes in patients with known/suspected CAD, in addition to replacement myocardial fibrosis and diabetes. Thus, the assessment of ΔESPVR index may be included into the standard stress-CMR exam to further stratify the patients., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Cardiac involvement in sickle beta thalassemia (Sβ-thal) patients has been poorly investigated. We aimed to evaluate cardiac function and myocardial iron overload by cardiovascular magnetic resonance (CMR) in patients with Sβ-thal. One-hundred and eleven Sβ-thal patients consecutively enrolled in the Myocardial Iron Overload in Thalassemia (MIOT) network were studied and compared with 46 sickle cell anemia (SCA) patients and with 111 gender- and age- matched healthy volunteers. Cine images were acquired to quantify biventricular function. Myocardial iron overload (MIO) was assessed by the T2* technique, while macroscopic myocardial fibrosis was evaluated by the late gadolinium enhancement (LGE) technique. In Sβ-thal and SCA patients, the morphological and functional CMR parameters were not significantly different, except for the left atrial area and left ventricular (LV) stroke volume, indexed by body surface area ( p = 0.023 and p = 0.048, respectively), which were significantly higher in SCA patients. No significant differences between the two groups were found in terms of myocardial iron overload and macroscopic myocardial fibrosis. When compared to healthy subjects, Sβ-thal patients showed significantly higher bi-atrial and biventricular parameters, except for LV ejection fraction, which was significantly lower. The CMR analysis confirmed that Sβ-thal and SCA patients are phenotypically similar. Since Sβ-thal patients showed markedly different morphological and functional indices from healthy subjects, it would be useful to identify Sβ-thal/SCA-specific bi-atrial and biventricular reference values.

Background: Cushing's Syndrome (CS) is associated with increased cardiovascular morbidity and mortality. In endogenous CS, cardiovascular mortality remains increased for up to 15 years post remission of hypercortisolism. Similarly, patients with exogenous CS have 4-fold increased incidence of cardiovascular events, regardless of pre-existing cardiovascular disease (CVD)., Objective: To present the pathophysiology, prognosis, clinical and imaging phenotype of cardiac disease in CS., Methods: A Pubmed search for cardiac disease in CS over the last 20 years was conducted using combinations of relevant terms. Preclinical and clinical studies, as well as review papers reporting on subclinical heart failure (HF), cardiomyopathy, coronary heart disease (CHD), and cardiovascular imaging were selected., Results: Cardiac disease in CS is associated with direct mineralocorticoid and glucocorticoid receptor activation, increased responsiveness to angiotensin II, ectopic epicardial adiposity, arterial stiffness and endothelial dysfunction, as well as with diabetes mellitus, hypertension, hyperlipidemia, obesity and prothrombotic diathesis. Subclinical HF and cardiomyopathy are principally related to direct glucocorticoid (GC) effects and markedly improve or regress post hypercortisolism remission. In contrast, CHD is related to both direct GC effects and CS comorbidities and persists post cure. In patients without clinical evidence of CVD, echocardiography and cardiac magnetic resonance (CMR) imaging reveal left ventricular hypertrophy, fibrosis, diastolic and systolic dysfunction, with the latter being underestimated by echocardiography. Finally, coronary microvascular disease is encountered in one third of cases., Conclusion: Cardiovascular imaging is crucial in evaluation of cardiac involvement in CS. CMR superiority in terms of reproducibility, operator independency, unrestricted field of view and capability of tissue characterisation makes this modality ideal for future studies., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP 3 ) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP 3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Introduction: Heart involvement is a common problem in systemic sclerosis. Recently, a definition of systemic sclerosis primary heart involvement had been proposed. Our aim was to establish consensus guidance on the screening, diagnosis and follow-up of systemic sclerosis primary heart involvement patients., Methods: A systematic literature review was performed to investigate the tests used to evaluate heart involvement in systemic sclerosis. The extracted data were categorized into relevant domains (conventional radiology, electrocardiography, echocardiography, cardiac magnetic resonance imaging, laboratory, and others) and presented to experts and one patient research partner, who discussed the data and added their opinion. This led to the formulation of overarching principles and guidance statements, then reviewed and voted on for agreement. Consensus was attained when the mean agreement was ⩾7/10 and of ⩾70% of voters., Results: Among 2650 publications, 168 met eligibility criteria; the data extracted were discussed over three meetings. Seven overarching principles and 10 guidance points were created, revised and voted on. The consensus highlighted the importance of patient counseling, differential diagnosis and multidisciplinary team management, as well as defining screening and diagnostic approaches. The initial core evaluation should integrate history, physical examination, rest electrocardiography, trans-thoracic echocardiography and standard serum cardiac biomarkers. Further investigations should be individually tailored and decided through a multidisciplinary management. The overall mean agreement was 9.1/10, with mean 93% of experts voting above 7/10., Conclusion: This consensus-based guidance on screening, diagnosis and follow-up of systemic sclerosis primary heart involvement provides a foundation for standard of care and future feasibility studies that are ongoing to support its application in clinical practice., Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Aleksandra Djokovic: no conflicts of interest to declare. Alessandro Giollo received consulting fees and/or honoraria from Novartis, Lilly, and Galapagos. Alessia Pepe: no conflicts of interest to declare. Alexia Steelandt: no conflicts of interest to declare. Alida L.P. Caforio: no conflicts of interest to declare. Anastasia Xintarakou: no conflicts of interest to declare. Anna Baritussio: no conflicts of interest to declare. Arsen Ristic: no conflicts of interest to declare. Carsten Tschöpe: no conflicts of interest to declare. Christopher P. Denton has received consultancy fees and/or research grant funding from Actelion, GlaxoSmithKline, Bayer, Sanofi-Aventis, Inventiva, Boehringer Ingelheim, Roche, CSL Behring, UCB Pharma, Leadiant Biosciences, Corbus, and Acceleron. Cosimo Bruni received consulting fees and/or honoraria from Actelion, Eli-Lilly, Boehringer Ingelheim; research grants from Gruppo Italiano Lotta alla Sclerodermia (GILS), European Scleroderma Trials and Research Group (EUSTAR), and Scleroderma Research Foundation (SRF), Scleroderma Clinical Trials Consortium (SCTC); and educational grants from AbbVie. Cosimo Bruni was supported by an Italian Ministry of University and Research PhD Scholarship, and this study is part of his PhD thesis. Daniel E. Furst reports grant/research support from Corbus, Galapagos GSK, Pfizer, Talaris, CSL Behring, Mitsubishi; Consultant fees from Actelion, Amgen, Corbus, Galapagos, Novartis, Pfizer, Roche/Genentech, Talaris, CSL Behring, and Boehringer Ingelheim. Dinesh Khanna reports grant support from NIH, Immune Tolerance Network, Bayer, BMS, Horizon, and Pfizer; consultant for Acceleron, Actelion, AbbVie, Amgen, Bayer, Boehringer Ingelheim, Chemomab, CSL Behring, Genentech/Roche, Horizon, Merck, Mitsubishi Tanabe Pharma, Prometheus Leadership/Equity position—Chief Medical Officer, and Eicos Sciences, Inc. Elijah R Behr: no conflicts of interest to declare. George Markousis-Mavrogenis: no conflicts of interest to declare. Ghadeer Hasan: no conflicts of interest to declare. Giacomo De Luca received honoraria from SOBI, Novartis, Pfizer, MSD, and Celgene. Ivan Milinkovic received honoraria/support from Boehringer Ingelheim and Hemofarm-Stada. Kostantinos Bratis: no conflicts of interest to declare. Luna Gargani received consultancy fees from GE Healthcare, Philips Healthcare, and Caption Health outside the submitted work. Marco Matucci-Cerinic received consultancies from Actelion, Janssen, Inventiva, Bayer, Biogen, Boehringer, CSL Behring, Corbus, Galapagos, Mitsubishi, Samsung, Regeneron, Acceleron, MSD, Chemomab, Lilly, Pfizer, and Roche. Masataka Kuwana received consultancy fees and/or research grant funding from AbbVie, Astellas, Bayer, Boehringer Ingelheim, Chugai, Corbus, Eisai, Horizon, Janssen, Mochida, Nippon Shinyaku, Ono Pharmaceuticals, Pfizer, and Mitsubishi Tanabe. Maya H. Buch: no conflicts of interest to declare. Oliver Distler has/had consultancy relationship with and/or has received research funding from and/or has served as a speaker for the following companies in the area of potential treatments for systemic sclerosis and its complications in the last three calendar years: 4P-Pharma, AbbVie, Acceleron, Alcimed, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, Corbus, CSL Behring, 4P Science, Galapagos, Glenmark, Horizon, Inventiva, Janssen, Kymera, Lupin, Medscape, Miltenyi Biotec, Mitsubishi Tanabe, MSD, Novartis, Prometheus, Redxpharma, Roivant, Sanofi, and Topadur; patent issued “mir-29 for the treatment of systemic sclerosis” (US8247389, EP2331143); research grants from Kymera, Mitsubishi Tanabe. Petar Seferovic: no conflicts of interest to declare. Petros Sfikakis reports consultancy fee from Actelion, Pfizer, Genesis, MSD, UCB, Boehringer Ingelheim, Enorasis, Farmaserv, Lilly, Gliead, AbbVie, and Novartis; grants/research support from AbbVie, Roche, Pfizer, Faran, Amgen, Jannsen, Boehringer Ingelheim, and Gilead. Raluca B. Dumitru: no conflicts of interest to declare. Renzo Marcolongo: no conflicts of interest to declare. Sophie Mavrogeni: no conflicts of interest to declare. Sven Plein: no conflicts of interest to declare. Thomas Krieg reports consultancy fee and grant funding from Actelion. Yannick Allanore reports personal fees from Actelion, Bayer, BMS, Boehringer, and Curzion and grants and personal fees from Inventiva, Roche, and Sanofi. Yohei Isomura: no conflicts of interest to declare. Yossra Atef Suliman: no conflicts of interest to declare., (© The Author(s) 2023.)

Background: The E-MIOT (Extension-Myocardial Iron Overload in Thalassemia) project is an Italian Network assuring high-quality quantification of tissue iron overload by magnetic resonance imaging (MRI). We evaluated the impact of the COVID-19 pandemic on E-MIOT services., Methods: The activity of the E-MIOT Network MRI centers in the year 2020 was compared with that of 2019. A survey evaluated whether the availability of MRI slots for patients with hemoglobinopathies was reduced and why., Results: The total number of MRI scans was 656 in 2019 and 350 in 2020, with an overall decline of 46.4% (first MRI: 71.7%, follow-up MRI: 36.9%), a marked decline (86.9%) in the period March-June 2020, and a reduction in the gap between the two years in the period July-September. A new drop (41.4%) was recorded in the period October-December for two centers, due to the general reduction in the total amount of MRIs/day for sanitization procedures. In some centers, patients refused MRI scans for fear of getting COVID. Drops in the MRI services >80% were found for patients coming from a region without an active MRI site., Conclusions: The COVID-19 pandemic had a strong negative impact on MRI multi-organ iron quantification, with a worsening in the management of patients with hemoglobinopathies.

Objectives: Cardiac magnetic resonance imaging (CMRI) is increasingly used to evaluate cardiac involvement in SSc. We assessed changes, including inflammatory and/or fibrotic myocardial lesions detected by CMRI, following therapeutic interventions for SSc-associated symptomatic myocarditis., Methods: In this retrospective study, myocarditis was diagnosed by CMRI (2018 revised Lake Louise criteria) in 14 diffuse and 4 limited SSc patients [16/18 women, age 56 years (s.d. 11), disease duration 8 years (s.d. 11), 17/18 with lung involvement] with cardiac symptoms and abnormal findings on echocardiography (4/18) and/or in 24-hour Holter monitoring (12/14). CMRI was repeated after 8 months (s.d. 3) following administration of cyclophosphamide (n = 11, combined with corticosteroids in 3 and rituximab in 1), mycophenolate (n = 1), tocilizumab (n = 1), methotrexate/corticosteroids (n = 2), corticosteroids (n = 1) or autologous stem cell transplantation (n = 2)., Results: Functional cardiac improvement was evident by increases in left [by 5.8% (s.d. 7.8), P = 0.006] and right ventricular ejection fraction [by 4.5% (s.d. 11.4), P = 0.085] in the second CMRI compared with the first. Notably, late gadolinium enhancement, currently considered to denote replacement fibrosis, decreased by 3.1% (s.d. 3.8; P = 0.003), resolving in six patients. Markers of myocardial oedema, namely T2 ratio and T2 mapping, decreased by 0.27 (s.d. 0.40; P = 0.013) and 6.0 (s.d. 7; P = 0.025), respectively. Conversely, both T1 mapping, considered to reflect acute oedema and diffuse fibrosis, and extracellular volume fraction, reflecting diffuse fibrosis, remained unchanged., Conclusions: CMRI may distinguish between reversible inflammatory/fibrotic and irreversible fibrotic lesions in SSc patients with active myocarditis, confirming the unique nature of primary cardiac involvement in SSc. Whether, and how, CMRI should be used to monitor treatment effects in SSc-associated myocarditis warrants further study., (© The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: We evaluated the prevalence of myocardial involvement by native T1 and T2 mapping, the diagnostic performance of mapping in addition to conventional Lake Louise Criteria (LLC), as well as correlations between mapping findings and clinical or conventional cardiovascular magnetic resonance (CMR) parameters in systemic sclerosis (SSc) patients., Methods: Fifty-five SSc patients (52.31 ± 13.24 years, 81.8% female) and 55 age- and sex-matched healthy subjects underwent clinical, bio-humoral assessment, and CMR. The imaging protocol included: T2-weighted, early post-contrast cine sequences, native T1 and T2 mapping by a segmental approach, and late gadolinium enhancement (LGE) technique., Results: Global myocardial T1 and T2 values were significantly higher in SSc patients than in healthy subjects. An increase in native T1 and/or T2 was present in the 62.1% of patients with normal conventional CMR techniques (negative LGE and T2-weighted images). Respectively, 13.5% and 59.6% of patients fulfilled original and updated LLC (overall agreement = 53.9%). Compared with patients with normal native T1, patients with increased T1 (40.0%) featured significantly higher left ventricular end-diastolic volume index and cardiac index, biventricular stroke volume indexes, and global heart T2 values, and more frequently had a history of digital ulcers. Biochemical and functional CMR parameters were comparable between patients with normal and increased T2 (61.8%)., Conclusion: T1 and T2 mapping are sensitive parameters that should be included in the routine clinical assessment of SSc patients for detecting early/subclinical myocardial involvement., Competing Interests: Declaration of Competing Interest The authors report no relationships that could be construed as a conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Cardiovascular morbidity and mortality are more prevalent in inflammatory arthritis (IA) compared to the general population. Recognizing the importance of addressing this issue, the European League Against Rheumatism (EULAR) published guidelines on cardiovascular disease (CVD) risk management in IA in 2016, with plans to update going forward based on the latest emerging evidence. Herein we review the latest evidence on cardiovascular disease in IA, taking a focus on rheumatoid arthritis, psoriatic arthritis, and axial spondylarthritis, reflecting on the scale of the problem and imaging modalities to identify disease. Evidence demonstrates that both traditional CVD factors and inflammation contribute to the higher CVD burden. Whereas CVD has decreased with the newer anti-rheumatic treatments currently available, CVD continues to remain an important comorbidity in IA patients calling for prompt screening and management of CVD and related risk factors. Non-invasive cardiovascular imaging has been attracting much attention in view of the possibility of detecting cardiovascular lesions in IA accurately and promptly, even at the pre-clinical stage. We reflect on imaging modalities to screen for CVD in IA and on the important role of rheumatologists and cardiologists working closely together.

Systemic sclerosis (SSc) presents high morbidity/mortality, due to internal organ fibrosis, including the heart. Cardiac magnetic resonance (CMR) can perform myocardial function and tissue characterization in the same examination. The Lake Louise criteria (LLC) can identify recent myocardial inflammation using CMR. Abnormal values include: (a) myocardial over skeletal muscle ratio in STIRT2-W images >2, (b) early gadolinium enhancement values >4, (c) epicardial/intramyocardial late gadolinium enhancement (LGE). The diagnosis of myocarditis using LLC is considered if 2/3 criteria are positive. Parametric imaging including T2, native T1 mapping and extracellular volume fraction (ECV) has been recently used to diagnose inflammatory cardiomyopathy. According to expert recommendations, myocarditis should be considered if at least 2 indices, one T2 and one T1 parameter are positive, whereas native T1 mapping and ECV assess diffuse fibrosis or oedema, even in the absence of LGE. Moreover, transmural/subendocardial fibrosis following the distribution of coronary arteries and diffuse subendocardial fibrosis not related with epicardial coronary arteries are indicative of epicardial and micro-vascular coronary artery disease, respectively. To conclude, CMR can identify acute/active myocardial inflammation and myocardial infarction using classic and parametric indices in parallel with ventricular function evaluation., Competing Interests: Declaration of Competing Interest None, (Copyright © 2022. Published by Elsevier Inc.)

Women's engagement in medicine, and more specifically cardiovascular imaging and cardiovascular MRI (CMR), has undergone a slow evolution over the past several decades. As a result, an increasing number of women have joined the cardiovascular imaging community to contribute their expertise. This collaborative work summarizes the barriers that women in cardiovascular imaging have overcome over the past several years, the positive interventions that have been implemented to better support women in the field of CMR, and the challenges that still remain, with a special emphasis on women physicians., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sierra-Galan, Aggarwal, Stojanovska, Raman, Han, Ferreira, Thomas, Seiberlich, Parwani, Bucciarelli-Ducci, Baldassarre, Mavrogeni, Ordovas, Schulz-Menger and Bandettini.)

In cardiorenal syndrome (CRS), heart failure and renal failure are pathophysiologically closely intertwined by the reciprocal relationship between cardiac and renal injury. Type 1 CRS is most common and associated with acute heart failure. A preexistent chronic kidney disease (CKD) is common and contributes to acute kidney injury (AKI) in CRS type 1 patients (acute cardiorenal syndrome). The remaining CRS types are found in patients with chronic heart failure (type 2), acute and chronic kidney diseases (types 3 and 4), and systemic diseases that affect both the heart and the kidney (type 5). Establishing the diagnosis of CRS requires various tools based on the type of CRS, including non-invasive imaging modalities such as TTE, CT, and MRI, adjuvant volume measurement techniques, invasive hemodynamic monitoring, and biomarkers. Albuminuria and Cystatin C (CysC) are biomarkers of glomerular filtration and integrity in CRS and have a prognostic impact. Comprehensive "all-in-one" magnetic resonance imaging (MRI) approaches, including cardiac magnetic resonance imaging (CMR) combined with functional MRI of the kidneys and with brain MRI are proposed for CRS. Hospitalizations due to CRS and mortality are high. Timely diagnosis and initiation of effective adequate therapy, as well as multidisciplinary care, are pertinent for the improvement of quality of life and survival. In addition to the standard pharmacological heart failure medication, including SGLT2 inhibitors (SGLT2i), renal aspects must be strongly considered in the context of CRS, including control of the volume overload (diuretics) with special caution on diuretic resistance. Devices involved in the improvement of myocardial function (e.g., cardiac resynchronization treatment in left bundle branch block, mechanical circulatory support in advanced heart failure) have also shown beneficial effects on renal function.

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

Stress is associated with unhealthy habits and non-communicable diseases. It is also linked to communicable diseases due to its impact on immune function. These can be prevented through intervention programs in schools. The aim of this study was to examine the effect of the simplified Pythagorean Self-Awareness Intervention on heart rate variability (HRV) parameters, perceived stress and behaviors of preschool children. The sample of the study consisted of 45 preschool students. A “one group (double) pretest—posttest design” was used, to allow for comparisons of the measurements before and after the intervention. Students were assessed via two questionnaires (“Perceived Stress Scale for Children” (PSS-C) and “Checklist for Screening Behavioral Problems in Preschool Children”) and a photoplethysmographic (PPG) device. The intervention lasted 9 weeks and included practicing of the Pythagorean Self-awareness techniques and the adoption of healthy behaviors. The results showed no statistically significant differences between the two pretests (p > 0.05 for all comparisons) and statistically significant differences between the second pretest and posttest (“Perceived Stress Scale for Children”, (PSS-C) p < 0.0001, “Checklist for Screening Behavioral Problems in Preschool Children” p < 0.0001 and two indices of PPG device: heart rate mean, p < 0.0001, low frequency/very low frequency, p = 0.034). In conclusion, the Pythagorean Self-Awareness Intervention had a beneficial effect on the sample of preschool students examined, as the results showed an improvement in the perceived stress and the HRV parameters tested, and in engaging healthier behaviors, findings that indicate a relaxed psychologic state and a healthier lifestyle.

Autoimmune rheumatic diseases (ARDs) involve multiple organs including the heart and vasculature. Despite novel treatments, patients with ARDs still experience a reduced life expectancy, partly caused by the higher prevalence of cardiovascular disease (CVD). This includes CV inflammation, rhythm disturbances, perfusion abnormalities (ischaemia/infarction), dysregulation of vasoreactivity, myocardial fibrosis, coagulation abnormalities, pulmonary hypertension, valvular disease, and side-effects of immunomodulatory therapy. Currently, the evaluation of CV involvement in patients with ARDs is based on the assessment of cardiac symptoms, coupled with electrocardiography, blood testing, and echocardiography. However, CVD may not become overt until late in the course of the disease, thus potentially limiting the therapeutic window for intervention. More recently, cardiovascular magnetic resonance (CMR) has allowed for the early identification of pathophysiologic structural/functional alterations that take place before the onset of clinically overt CVD. CMR allows for detailed evaluation of biventricular function together with tissue characterization of vessels/myocardium in the same examination, yielding a reliable assessment of disease activity that might not be mirrored by blood biomarkers and other imaging modalities. Therefore, CMR provides diagnostic information that enables timely clinical decision-making and facilitates the tailoring of treatment to individual patients. Here we review the role of CMR in the early and accurate diagnosis of CVD in patients with ARDs compared with other non-invasive imaging modalities. Furthermore, we present a consensus-based decision algorithm for when a CMR study could be considered in patients with ARDs, together with a standardized study protocol. Lastly, we discuss the clinical implications of findings from a CMR examination., Competing Interests: Conflict of interest: E.D. has received speaker fees or honoraria from Astra Zeneca, Pfizer, Bristol Myers Squibb, General Electric, and Abbott Vascular, and research funding from GE Healthcare. L.G. has received speaker fees or honoraria from GE Healthcare, Caption Health, Philips Healthcare, and EchoNous. J.A.C. Lima has received honoraria from Astra Zeneca. N.A.-M. has received speaker fees or honoraria from GE Healthcare and Abbott Vascular. M.M.-C. has received research funding from MSD. M.M. has received research funding from the National Scleroderma Foundation. R.N. has received speaker fees from Sanofi Genzyme, Bayer, and Boehringer-Ingelheim, and unrestricted research funding from Philips Volcano and Biotronik. N.A.B.N. has received speaker fees or honoraria from Novo Nordisk and Servier, research funding from GlaxoSmithKline, and royalties for intellectual property from UpToDate. S.E.P. provides consultancy to and is stockowner of Circle Cardiovascular Imaging. L.E.S. has received speaker fees or honoraria from Pfizer and Janssen. J.S. has received unrestricted research grant from Bayer. P.S. has received speaker fees or honoraria from Astra Zeneca, Boehringer-Ingelheim, Medtronic, Novartis, Servier, and Respicardia. L.M.S.-G. has received speaker fees or honoraria from Amgen. J.W. has received speaker fees or honoraria from Abbott Vascular and research funding form GE Healthcare., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Heart failure (HF) patients frequently develop brain deficits that lead to cognitive dysfunction (CD), which may ultimately also affect survival. There is an important interaction between brain and heart that becomes crucial for survival in patients with HF. Our aim was to review the brain/heart interactions in HF and discuss the emerging role of combined brain/heart magnetic resonance imaging (MRI) evaluation. A scoping review of published literature was conducted in the PubMed EMBASE (OVID), Web of Science, Scopus and PsycInfo databases. Keywords for searches included heart failure, brain lesion, brain, cognitive, cognitive dysfunction, magnetic resonance imaging cardiovascular magnetic resonance imaging electroencephalogram, positron emission tomography and echocardiography. CD testing, the most commonly used diagnostic approach, can identify neither subclinical cases nor the pathophysiologic background of CD. A combined brain/heart MRI has the capability of diagnosing brain/heart lesions at an early stage and potentially facilitates treatment. Additionally, valuable information about edema, fibrosis and cardiac remodeling, provided with the use of cardiovascular magnetic resonance, can improve HF risk stratification and treatment modification. However, availability, familiarity with this modality and cost should be taken under consideration before final conclusions can be drawn. Abnormal CD testing in HF patients is a strong motivating factor for applying a combined brain/heart MRI to identify early brain/heart lesions and modify risk stratification accordingly.

Background: COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization., Method: ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted., Results: Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions., Conclusions: Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background and Motivation : Parkinson's disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method : The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results : We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion : The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann−Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients., Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification., Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

Hypertension is the most common causative factor of cardiac remodeling, which, in turn, has been associated with changes in brain and kidney function. Currently, the role of blood biomarkers as indices of cardiac remodeling remains unclear. In contrast, cardiac imaging, including echocardiography and cardiovascular magnetic resonance (CMR), has been a valuable noninvasive tool to assess cardiac remodeling. Cardiac remodeling during the course of systemic hypertension is not the sole effect of the latter. "Remodeling" of other vital organs, such as brain and kidney, also takes place. Therefore, it will be more accurate if we discuss about "hypertensive remodeling" involving the heart, the brain, and the kidneys, rather than isolated cardiac remodeling. This supports the idea of their simultaneous assessment to identify the early, silent lesions of total "hypertensive remodeling". In this context, magnetic resonance imaging is the ideal modality to provide useful information about these organs in a noninvasive fashion and without radiation. For this purpose, we propose a combined protocol to employ MRI in the simultaneous assessment of the heart, brain and kidneys. This protocol should include all necessary indices for the evaluation of "hypertensive remodeling" in these 3 organs, and could be performed within a reasonable time, not exceeding one hour, so that it remains patient-friendly. Furthermore, a combined protocol may offer "all in one examination" and save time. Finally, the amount of contrast agent used will be limited granted that post-contrast evaluations of the three organs will be performed after 1 injection., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Background: Human immunodeficiency virus (HIV) is mainly detected in young, otherwise healthy, individuals. Cardiomyopathy and peripheral artery disease affecting these patients appears to be multifactorial. Prompt and potentially more effective implementation of therapeutic measures could be enabled by pre-symptomatic diagnosis of myocardial dysfunction and peripheral artery damage. However, limited data is available to date on this specific topic. Μethods: We investigated the association between global longitudinal strain (GLS), an established index of subclinical left ventricular systolic dysfunction (LVSD) assessed by two-dimensional speckle-tracking echocardiography, and: (a) patient history; (b) demographic and clinical baseline characteristics; (c) carotid intima-media thickness (IMT) and the presence of carotid atherosclerotic plaque(s), measured by ultrasonography; (d) temperature difference (ΔT) along each carotid artery, measured by microwave radiometry; and (e) basic blood panel measurements, including high-sensitivity troponin-T (hsTnT) and NT-proBNP in people living with HIV (PLWH) and no history of cardiovascular disease., Results: We prospectively enrolled 103 consecutive PLWH (95% male, age 47 ± 11 years, anti-retroviral therapy 100%) and 52 age- and sex-matched controls. PLWH had a significantly higher relative wall thickness (0.38 ± 0.08 vs. 0.36 ± 0.04, p = 0.048), and higher rate of LVSD (34% vs. 15.4%, p = 0.015), and carotid artery atherosclerosis (28% vs. 6%, p = 0.001) compared with controls. Among PLWH, LVSD was independently associated with the presence of carotid atherosclerosis (adj. OR:3.09; 95%CI:1.10-8.67, p = 0.032) and BMI (1.15; 1.03-1.29, p = 0.017), while a trend for association between LVSD and left ventricular hypertrophy was also noted (3.12; 0.73-13.33, p = 0.124). No differences were seen in microwave radiometry parameters, NT-proBNP, hs-TnT and c-reactive protein between PLWH with and without LVSD., Conclusions: Subclinical LVSD and carotid atherosclerosis were significantly more frequent in PLWH compared to a group of healthy individuals, implying a possible link between HIV infection and these two pathological processes. Carotid atherosclerosis and increased adiposity were independently associated with impaired GLS in HIV-infected individuals.

Microvascular wall abnormalities demonstrated by nailfold capillaroscopy in systemic sclerosis (SSc) may result in microhemorrhagic deposition of erythrocyte-derived iron. Such abnormalities precede fibrosis, which is orchestrated by myofibroblasts. Iron induces endothelial-to-mesenchymal transition in vitro, which is reversed by reactive oxygen species (ROS) scavengers. The conversion of quiescent fibroblasts into profibrotic myofibroblasts has also been associated with ROS-mediated activation of TGF-β1. Given that iron overload predisposes to ROS formation, we hypothesized that the uptake of erythrocyte-derived iron by resident cells promotes fibrosis. Firstly, we show that iron induces oxidative stress in skin-derived and synovial fibroblasts in vitro, as well as in blood mononuclear cells ex vivo. The biological relevance of increased oxidative stress was confirmed by showing the concomitant induction of DNA damage in these cell types. Similar results were obtained in vivo, following intravenous iron administration. Secondly, using magnetic resonance imaging we show an increased iron deposition in the fingers of a patient with early SSc and nailfold microhemorrhages. While a systematic magnetic resonance study to examine tissue iron levels in SSc, including internal organs, is underway, herein we propose that iron may be a pathogenetic link between microvasculopathy and fibrosis and an additional mechanism responsible for increased oxidative stress in SSc.

A 30-year-old man with a history of an in-situ melanoma of the forehead was referred for cardiac evaluation because of tachycardia and elevated levels of serum troponin. The transthoracic echocardiogram revealed multiple masses attached to the walls of both ventricles and the right atrium (RA). A large mass was occupying almost one third of the right ventricle (RV), resulting in reduction of the end-diastolic RV volume and tachycardia. A cardiac magnetic resonance imaging confirmed multifocal myocardial infiltration and intracavitary masses and excluded the presence of thrombus in any of the cardiac chambers. Diffuse metastatic involvement in the liver, the spleen, and the brain by computed tomography precluded surgical management. Being BRAF-unmutated, the patient was initially treated with a combination of nivolumab and ipilimumab. One month later, the cardiac metastases in RA and left ventricle were unchanged on echocardiogram, while the tumor in RV was enlarged occupying the majority of the chamber, resulting in further reduction of the cardiac output and tachycardia. The treatment was changed to a combination of dacarbazine and carboplatin, but the patient eventually died two months later. Heart is not a common metastatic site of melanoma and cardiac involvement is usually clinically silent making ante mortem diagnosis difficult. Multimodalidy imaging plays a pivotal role in the diagnostic work up. Cardiac melanoma metastases indicate an advance stage disease with poor prognosis.

Introduction: Primary heart involvement in systemic sclerosis may cause morpho-functional and electrical cardiac abnormalities and is a common cause of death. The absence of a clear definition of primary heart involvement in systemic sclerosis limits our understanding and ability to focus on clinical research. We aimed to create an expert consensus definition for primary heart involvement in systemic sclerosis., Methods: A systematic literature review of cardiac involvement and manifestations in systemic sclerosis was conducted to inform an international and multi-disciplinary task force. In addition, the nominal group technique was used to derive a definition that was then subject to voting. A total of 16 clinical cases were evaluated to test face validity, feasibility, reliability and criterion validity of the newly created definition., Results: In total, 171 publications met eligibility criteria. Using the nominal group technique, experts added their opinion, provided statements to consider and ranked them to create the consensus definition, which received 100% agreement on face validity. A median 60(5-300) seconds was taken for the feasibility on a single case. Inter-rater agreement was moderate (mKappa (95% CI) = 0.56 (0.46-1.00) for the first round and 0.55 (0.44-1.00) for the second round) and intra-rater agreement was good (mKappa (95% CI) = 0.77 (0.47-1.00)). Criterion validity showed a 78 (73-84)% correctness versus gold standard., Conclusion: A preliminary primary heart involvement in systemic sclerosis consensus-based definition was created and partially validated, for use in future clinical research., Competing Interests: Declaration of conflicting interests: The author(s) declared the following potential conflicts of interest with respect to research, authorship and/or publication of this article: C.B. reports consultancy fee from Actelion, Eli Lilly; grants from Gruppo Italiano Lotta alla Sclerodermia (GILS), Fondazione Italiana Ricerca sull’Artrite (FIRA), European Scleroderma Trial and D.E.F. reports grant/research support from Corbus, Galapagos GSK, Pfizer, Talaris, CSL Behring, Mitsubishi; Consultant fees from Actelion, Amgen, Corbus, Galapagos, Novartis, Pfizer, Roche/Genentech, Talaris, CSL Behring, Boehringer Ingelheim. G.D.L. received honoraria from SOBI, Novartis, Pfizer, MSD, Celgene. L.G. has received consultancy fees from GE Healthcare outside the submitted work. Y.A. reports personal fees from Actelion, Bayer, BMS, Boehringer and Curzion, and grants and personal fees from Inventiva, Roche, and Sanofi. M.K. has received consultancy fees and/or research grant funding from Abbvie, Actelion Pharmaceuticals, Astellas, Bayer, Boehringer Ingelheim, Chugai, Corbus, CSL Behring, Eisai, Mochida, Nippon Shinyaku, Novartis, Ono, Pfizer, Reata, and Tanabe-Mitsubishi. C.P.D. has received consultancy fees and/or research grant funding from Actelion, GlaxoSmithKline, Bayer, Sanofi-Aventis, Inventiva, Boehringer Ingelheim, Roche, CSL Behring, UCB Pharma, Leadiant Biosciences, Corbus, Acceleron. D.K. reports personal fees from: Actelion, Abbvie, Bayer, Boehringer-Ingelheim, Chemomab, Corbus, CSL Behring, Genentech/Roche, Gilead, GSK, Mitsubishi Tanabi, Sanofi-Aventis, UCB Pharma. He reports grants from Bayer, Boehringer-Ingelheim, Genentech/Roche, Pfizer, Sanofi-Aventis and has stock options in Eicos Sciences, Inc. T.K. reports consultancy fee and grant funding from Actelion. E.Z. reports consultancy fee from GlaxoSmithKline. M.M.-C. reports grant and personal fees from Actelion, personal fees from Biogen, personal fees from Bayer, personal fees from Boehringer Ingelheim, personal fees from CSL Behring, personal fees from Eli-Lilly, outside the submitted work. M.H.B., A.D., R.B.D., A.G., M.P., Y.A.S., K.B., A.S., I.M., A.B., G.H., A.X., Y.I., G.M.-M., L.T., S.M., A.L.P.C., C.T., A.R., K.K., S.P., E.R.B., R.M., I.G., F.T., P.S.: no conflicts of interest to declare., (© The Author(s) 2021.)

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020-2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPoint TM , Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland-Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

Background : Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout : The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions : By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment., (© 2021 The Author(s). Published by BRI.)

Background: With the number of people living with human immunodeficiency virus (HIV) steadily increasing, cardiovascular disease has emerged as a leading cause of non-HIV related mortality. People living with HIV (PLWH) appear to be at increased risk of coronary artery disease and heart failure (HF), while the underlying mechanism appears to be multifactorial. In the general population, ectopic cardiac adiposity has been highlighted as an important modulator of accelerated coronary artery atherosclerosis, arrhythmogenesis and HF with preserved ejection fraction (HFpEF). Cardiac adiposity is also strongly linked with obesity, especially with visceral adipose tissue accumulation., Aims: This review aims to summarize the possible role of cardiac fat depositions, assessed by imaging modalities,as potential contributors to the increased cardiac morbidity and mortality seen in PLWH, as well as therapeutic targets in the current ART era., Materials & Methods: Review of contemporary literature on this topic., Discussion: Despite antiretroviral therapy (ART), PLWH have evidence of persistent, HIV-related systemic inflammation and body fat alterations. Cardiac adiposity can play an additional role in the pathogenesis of cardiovascular disease in the HIV setting. Imaging modalities such as echocardiography, cardiac multidetector computed tomography and cardiac magnetic resonance have demonstrated increased adipose tissue. Studies show that high cardiac fat depots play an additive role in promoting coronary artery atherosclerosis and HFpEF in PLWH. Systemic inflammation due to HIV infection, metabolic adverse effects of ART, adipose alterations in the ageing HIV population, inflammation and immune activation are likely important mechanisms for adipose dysfunction and disproportionately occurrence of ectopic fat depots in the heart among PLWH., Conclusions: High cardiac adiposity seems to plays an additive role in promoting coronary artery atherosclerosis and HFpEF in PLWH. The underlying mechanisms are multiple and warrant further investigation. Improved understanding of the regulating mechanisms that increase cardiovascular risk in HIV infection may give rise to more tailored therapeutic strategies targeting cardiac fat depots., (© 2021 British HIV Association.)

Background : For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology : The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results : The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions : The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation., Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT., Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98 , and ~0.96 ( p -value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94% . On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet . The HDL runs in <1 s on test data per image., Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-20-7676). The series “Carotid Artery Stenosis and Stroke – Prevention and Treatment Part II” was commissioned by the editorial office without any funding or sponsorship. The authors have no other conflicts of interest to declare., (2021 Annals of Translational Medicine. All rights reserved.)

Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound., (© 2021. Society for Imaging Informatics in Medicine.)

This document is a position statement from the Society for Cardiovascular Magnetic Resonance (SCMR) on recommendations for clinical utilization of cardiovascular magnetic resonance (CMR) in women with cardiovascular disease. The document was prepared by the SCMR Consensus Group on CMR Imaging for Female Patients with Cardiovascular Disease and endorsed by the SCMR Publications Committee and SCMR Executive Committee. The goals of this document are to (1) guide the informed selection of cardiovascular imaging methods, (2) inform clinical decision-making, (3) educate stakeholders on the advantages of CMR in specific clinical scenarios, and (4) empower patients with clinical evidence to participate in their clinical care. The statements of clinical utility presented in the current document pertain to the following clinical scenarios: acute coronary syndrome, stable ischemic heart disease, peripartum cardiomyopathy, cancer therapy-related cardiac dysfunction, aortic syndrome and congenital heart disease in pregnancy, bicuspid aortic valve and aortopathies, systemic rheumatic diseases and collagen vascular disorders, and cardiomyopathy-causing mutations. The authors cite published evidence when available and provide expert consensus otherwise. Most of the evidence available pertains to translational studies involving subjects of both sexes. However, the authors have prioritized review of data obtained from female patients, and direct comparison of CMR between women and men. This position statement does not consider CMR accessibility or availability of local expertise, but instead highlights the optimal utilization of CMR in women with known or suspected cardiovascular disease. Finally, the ultimate goal of this position statement is to improve the health of female patients with cardiovascular disease by providing specific recommendations on the use of CMR.

Background: Heterozygous familial hypercholesterolemia (heFH) is a genetic disorder leading to premature coronary artery disease (CAD). We hypothesized that the subclinical pathophysiologic consequences of hypercholesterolemia may be detected before the occurrence of clinically overt CAD by stress testing and myocardial strain imaging., Patients-Methods: We evaluated the treadmill tests (ETTs) of 46 heFH men without known arterial hypertension/diabetes mellitus/vasculopathy like CAD and of 39 healthy men matched for age, baseline systolic/diastolic blood pressure (BP) and heart rate (HR), using Bruce protocol. Global longitudinal strain (GLS) of the left ventricle (LV) additionally to ejection fraction was obtained., Results: heFH men reached a significantly higher peak systolic and diastolic BP compared to controls (p = 0.002 and p < 0.001, respectively). Mean rate pressure product was significantly higher in heFH patients (p = 0.038). Both duration of the ETT and workload in metabolic equivalents was lower in the heFH group (p < 0.001 and p < 0.001, respectively). Baseline to peak rise of systolic and diastolic BP in heFH men was higher (p = 0.008 and p < 0.001 for systolic and diastolic BP, respectively). Furthermore, heFH men had higher rise of HR from baseline to peak, compared to controls; (p = 0.047). GLS in heHF men was slightly decreased (p = 0.014), although the ejection fraction was similar in both groups., Conclusion: heFH men have a higher rise in systolic/diastolic BP during ETT, which may reflect early, preclinical hypertension. Furthermore, slight impairment of LV GLS is present, despite the absence of apparent myocardial dysfunction in conventional 2D echocardiography., Competing Interests: All the authors declare that they have no competing interests regarding the present work., (© 2021 The Authors.)

Chronic kidney disease (CKD) and cardiovascular disease (CVD) together result in an enormous burden on global healthcare. The estimated glomerular filtration rate (eGFR) is a well-established biomarker of CKD and is associated with adverse cardiac events. This review highlights the link between eGFR reduction and that of atherosclerosis progression, which increases the risk of adverse cardiovascular events. In general, CVD risk assessments are performed using conventional risk prediction models. However, since these conventional models were developed for a specific cohort with a unique risk profile and further these models do not consider atherosclerotic plaque-based phenotypes, therefore, such models can either underestimate or overestimate the risk of CVD events. This review examined the approaches used for CVD risk assessments in CKD patients using the concept of integrated risk factors. An integrated risk factor approach is one that combines the effect of conventional risk predictors and non-invasive carotid ultrasound image-based phenotypes. Furthermore, this review provided insights into novel artificial intelligence methods, such as machine learning and deep learning algorithms, to carry out accurate and automated CVD risk assessments and survival analyses in patients with CKD.

Wilson's disease (WD) is caused by copper accumulation in the brain and liver, and if not treated early, can lead to severe disability and death. WD has shown white matter hyperintensity (WMH) in the brain magnetic resonance scans (MRI) scans, but the diagnosis is challenging due to (i) subtle intensity changes and (ii) weak training MRI when using artificial intelligence (AI). Design and validate seven types of high-performing AI-based computer-aided design (CADx) systems consisting of 3D optimized classification, and characterization of WD against controls. We propose a "conventional deep convolution neural network" (cDCNN) and an "improved DCNN" (iDCNN) where rectified linear unit (ReLU) activation function was modified ensuring "differentiable at zero." Three-dimensional optimization was achieved by recording accuracy while changing the CNN layers and augmentation by several folds. WD was characterized using (i) CNN-based feature map strength and (ii) Bispectrum strengths of pixels having higher probabilities of WD. We further computed the (a) area under the curve (AUC), (b) diagnostic odds ratio (DOR), (c) reliability, and (d) stability and (e) benchmarking. Optimal results were achieved using 9 layers of CNN, with 4-fold augmentation. iDCNN yields superior performance compared to cDCNN with accuracy and AUC of 98.28 ± 1.55, 0.99 (p < 0.0001), and 97.19 ± 2.53%, 0.984 (p < 0.0001), respectively. DOR of iDCNN outperformed cDCNN fourfold. iDCNN also outperformed (a) transfer learning-based "Inception V3" paradigm by 11.92% and (b) four types of "conventional machine learning-based systems": k-NN, decision tree, support vector machine, and random forest by 55.13%, 28.36%, 15.35%, and 14.11%, respectively. The AI-based systems can potentially be useful in the early WD diagnosis. Graphical Abstract.

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Increased cardiac fat depots are metabolically active tissues that have a pronounced pro-inflammatory nature. Increasing evidence supports a potential role of cardiac adiposity as a determinant of the substrate of atrial fibrillation and ventricular arrhythmias. The underlying mechanism appears to be multifactorial with local inflammation, fibrosis, adipocyte infiltration, electrical remodeling, autonomic nervous system modulation, oxidative stress and gene expression playing interrelating roles. Current imaging modalities, such as echocardiography, computed tomography and cardiac magnetic resonance, have provided valuable insight into the relationship between cardiac adiposity and arrhythmogenesis, in order to better understand the pathophysiology and improve risk prediction of the patients, over the presence of obesity and traditional risk factors. However, at present, given the insufficient data for the additive value of imaging biomarkers on commonly used risk algorithms, the use of different screening modalities currently is indicated for personalized risk stratification and prognostication in this setting.