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Background: AI-CAC provides more actionable information than the Agatston coronary artery calcium (CAC) score. We have recently shown in the MESA (Multi-Ethnic Study of Atherosclerosis) that AI-CAC automated left atrial (LA) volumetry enabled prediction of atrial fibrillation (AF) as early as 1 year., Objectives: In this study, the authors evaluated the performance of AI-CAC LA volumetry versus LA measured by human experts using cardiac magnetic resonance imaging (CMRI) for predicting incident AF and stroke and compared them with Cohorts for Heart and Aging Research in Genomic Epidemiology model for atrial fibrillation (CHARGE-AF) risk score, Agatston score, and N-terminal pro b-type natriuretic peptide (NT-proBNP)., Methods: We used 15-year outcomes data from 3,552 asymptomatic individuals (52.2% women, age 61.7 ± 10.2 years) who underwent both CAC scans and CMRI in the MESA baseline examination. CMRI LA volume was previously measured by human experts. Data on NT-proBNP, CHARGE-AF risk score, and the Agatston score were obtained from MESA. Discrimination was assessed using the time-dependent area under the curve., Results: Over 15 years follow-up, 562 cases of AF and 140 cases of stroke accrued. The area under the curve for AI-CAC versus CMRI volumetry for AF (0.802 vs 0.798) and stroke (0.762 vs 0.751) were not significantly different. AI-CAC LA significantly improved the continuous net reclassification index for prediction of 5-year AF when added to CHARGE-AF risk score (0.23), NT-proBNP (0.37, 0.37), and Agatston score (0.44) ( P for all <0.0001)., Conclusions: AI-CAC automated LA volumetry and CMRI LA volume measured by human experts similarly predicted incident AF and stroke over 15 years. Further studies to investigate the clinical utility of AI-CAC for AF and stroke prediction are warranted., Competing Interests: Several members of the writing group are inventors of the AI tool mentioned in this paper. Dr Naghavi is the founder of HeartLung.AI. Dr Reeves, Dr Atlas, Dr Yankelevitz, Dr Li, and Dr Wong are on the advisory board for HeartLung.AI. Dr Zhang is a software engineer for HeartLung.AI. Dr Atlas is a graduate research associate of HeartLung.AI. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. This research was supported by 2R42AR070713 and R01HL146666 and MESA was supported by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the 10.13039/100000050National Heart, Lung, and Blood Institute, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the 10.13039/100006108National Center for Advancing Translational Sciences (NCATS)., (© 2024 The Authors.)

Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) applied to CAC scans can predict non-CHD events, including heart failure, atrial fibrillation, and stroke. We applied AI-enabled automated cardiac chambers volumetry and calcified plaque characterization to CAC scans (AI-CAC) of 5830 asymptomatic individuals (52.2% women, age 61.7 ± 10.2 years) in the multi-ethnic study of atherosclerosis during 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow-up for AI-CAC vs. Agatston score was (0.784 vs. 0.701), (0.771 vs. 0.709), (0.789 vs. 0.712) and (0.816 vs. 0.729) (p < 0.0001 for all), respectively. AI-CAC plaque characteristics, including number, location, density, plus number of vessels, significantly improved CHD prediction in the CAC 1-100 cohort vs. Agatston Score. AI-CAC significantly improved the Agatston score for predicting all CVD events., (© 2024. The Author(s).)

Introduction: Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF)., Methods: We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years., Results: Over 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p ​< ​0.0001), and comparable to clinical risk factors (0.85, p ​= ​0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p ​< ​0.0001)., Conclusion: AI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths., Methods: We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score., Results: During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342)., Conclusion: In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.

Rationale and Objectives: We previously reported a novel manual method for measuring bone mineral density (BMD) in coronary artery calcium (CAC) scans and validated our method against Dual X-Ray Absorptiometry (DEXA). Furthermore, we have developed and validated an artificial intelligence (AI) based automated BMD (AutoBMD) measurement as an opportunistic add-on to CAC scans that recently received FDA approval. In this report, we present evidence of equivalency between AutoBMD measurements in cardiac vs lung CT scans., Materials and Methods: AI models were trained using 132 cases with 7649 (3 mm) slices for CAC, and 37 cases with 21918 (0.5 mm) slices for lung scans. To validate AutoBMD against manual measurements, we used 6776 cases of BMD measured manually on CAC scans in the Multi-Ethnic Study of Atherosclerosis (MESA). We then used 165 additional cases from Harbor UCLA Lundquist Institute to compare AutoBMD in patients who underwent both cardiac and lung scans on the same day., Results: Mean±SD for age was 69 ± 9.4 years with 52.4% male. AutoBMD in lung and cardiac scans, and manual BMD in cardiac scans were 153.7 ± 43.9, 155.1 ± 44.4, and 163.6 ± 45.3 g/cm 3 , respectively (p = 0.09). Bland-Altman agreement analysis between AutoBMD lung and cardiac scans resulted in 1.37 g/cm 3 mean differences. Pearson correlation coefficient between lung and cardiac AutoBMD was R 2 = 0.95 (p < 0.0001)., Conclusion: Opportunistic BMD measurement using AutoBMD in CAC and lung cancer screening scans is promising and yields similar results. No extra radiation plus the high prevalence of asymptomatic osteoporosis makes AutoBMD an ideal screening tool for osteopenia and osteoporosis in CT scans done for other reasons., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Morteza Naghavi reports financial support was provided by American Technologies LLC and HeartLung.AI. Morteza Naghavi reports a relationship with American Technologies LLC and HeartLung.AI that includes: board membership, consulting or advisory, employment, and equity or stocks. Song Shou Mao reports a relationship with The Lundquist Institute that includes: consulting or advisory. Venkat Manubolu reports a relationship with HeartLung.AI that includes: consulting or advisory. Dong Li reports a relationship with HeartLung.AI that includes: consulting or advisory. Thomas Atlas reports a relationship with HeartLung.AI that includes: consulting or advisory. Anthony Reeves reports a relationship with HeartLung.AI that includes: consulting or advisory. Claudia Henschke reports no financial relationship with HeartLung.AI or American Heart Technologies LLC. David Yankelevitz reports a relationship with HeartLung.AI that includes: consulting or advisory. Matthew Budoff reports a relationship with HeartLung.AI that includes: consulting or advisory. Amirhossein Jaberzadeh reports a relationship with HeartLung.AI that includes: employment. Chenyu Zhang reports a relationship with HeartLung.AI that includes: employment. Marlon Montes reports a relationship with HeartLung.AI that includes: employment. Mathew Budoff has patent #US 9119,590 B2 issued to Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA. Mathew Budoff has patent #US 10,695,022,B2 issued to Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA., (© 2023 Published by Elsevier Ltd.)

Introduction: Guidelines recommend individualized breast cancer screening and prevention interventions for women in their 40s. Yet, few primary care clinicians assess breast cancer risk., Study Design: Pretest-Posttest trial., Setting/participants: Women aged 40-49 years were recruited from one large Boston-based academic primary care practice between July 2017 and April 2019., Intervention: Participants completed a pretest, received a personalized breast cancer risk report, saw their primary care clinician, and completed a posttest., Main Outcome Measures: Using mixed effects models, changes in screening intentions (0-100 scale [0=will not screen to 100=will screen]), mammography knowledge, decisional conflict, and receipt of screening were examined. Analyses were conducted from June 2019 to February 2020., Results: Patient (n=337) mean age was 44.1 (SD=2.9) years, 61.4% were non-Hispanic white, and 76.6% were college graduates; 306 (90.5%) completed follow-up (203 with 5-year breast cancer risk <1.1%). Screening intentions declined from pre- to post-visit (79.3 to 68.0, p<0.0001), especially for women with 5-year risk <1.1% (77.2 to 63.3, p<0.0001), but still favored screening. In the 2 years prior, 37.6% had screening mammography compared with 41.8% over a mean 16 months follow-up (p=0.17). Mammography knowledge increased and decisional conflict declined. Eleven (3.3%) women met criteria for breast cancer prevention medications (ten discussed medications with their clinicians), 22 (6.5%) for MRI (19 discussed MRI with their clinician), and 67 (19.8%) for genetic counseling (47 discussed with the clinician)., Conclusions: Receipt of a personalized breast cancer report was associated with women in their 40s making more-informed and less-conflicted mammography screening decisions and with high-risk women discussing breast cancer prevention interventions with clinicians., Trial Registration: This study is registered at www.clinicaltrials.govNCT03180086., (Copyright © 2020 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Influenza viruses evade human adaptive immune responses due to continuing antigenic changes. This makes it necessary to re-formulate and re-administer current seasonal influenza vaccines on an annual basis. Our pan-influenza vaccination approach attempts to redirect antibody responses from the variable, immuno-dominant hemagglutinin head towards the conserved-but immuno-subdominant-hemagglutinin stalk. The strategy utilizes sequential immunization with chimeric hemagglutinin-based vaccines expressing exotic head domains, and a conserved hemagglutinin stalk. We compared a live-attenuated influenza virus prime followed by an inactivated split-virus boost to two doses of split-virus vaccines and assessed the impact of adjuvant on protection against challenge with pandemic H1N1 virus in ferrets. All tested immunization regimens successfully induced broadly cross-reactive antibody responses. The combined live-attenuated/split virus vaccination conferred superior protection against pandemic H1N1 infection compared to two doses of split-virus vaccination. Our data support advancement of this chimeric hemagglutinin-based vaccine approach to clinical trials in humans., Competing Interests: The Icahn School of Medicine at Mount Sinai has filed patent applications regarding influenza virus vaccines.