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The interaction between humans and computers is one of the most pressing challenges in contemporary society. Subtleties of this communication require that human-computer interfaces are conceptualized and tested by highly specialized experts. Here we describe a novel academic course for human-oriented designers and project managers, with the emphasis being put on the communication between the human brain and computer. Outcomes of the course were validated on 12 course participants both: qualitatively -- educational experience, and quantitatively -- outcomes of a project and final exam. T-test comparison revealed that students who participated in the course gained specialized knowledge, as compared to students who did not take part in the course. Moreover, students generally agreed that the course was helpful and interesting, however, about one-third of them considered the course hard. All in all, we find our brain-computer interfaces course useful in instructing specialist oriented on the human-factor of the communication between human and the machine.

Aims: Proximal coronary artery calcium (CAC) may improve prediction of major adverse cardiac events (MACE) beyond the CAC score, particularly in patients with low CAC burden. We investigated whether the proximal CAC can be detected on gated cardiac computer tomography (CT) and whether it provides prognostic significance with artificial intelligence (AI)., Methods and Results: A total of 2016 asymptomatic adults with baseline CAC CT scans from a single site were followed up for MACE for 14 years. An AI algorithm to classify CAC into proximal or not was created using expert annotations of total and proximal CAC and AI-derived cardiac structures. The algorithm was evaluated for prognostic significance on AI-derived CAC segmentation. In 303 subjects with expert annotations, the classification of proximal versus not proximal CAC reached an area under receiver operating curve of 0.93 (95% confidence interval [CI] 0.91-0.95). For prognostic evaluation, in an additional 588 subjects with mild AI-derived CAC scores, the AI proximal involvement was associated with worse MACE-free survival (P=0.008) and higher risk of MACE when adjusting for CAC score alone (hazard ratio [HR] 2.28, 95% CI 1.16-4.48, P=0.02) or CAC score and clinical risk factors (HR 2.12, 95% CI 1.03-4.36, P=0.04)., Conclusion: The AI algorithm could identify proximal CAC on CAC CT. The proximal location had modest prognostic significance in subjects with mild CAC scores. The AI identification of proximal CAC can be integrated into automatic CAC scoring and improves the risk prediction of CAC CT., (© The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Aims: Transthoracic echocardiography is recommended in all patients with acute coronary syndrome but is time-consuming and lacks an evidence base. We aimed to assess the feasibility, diagnostic accuracy, and time efficiency of hand-held echocardiography in patients with acute coronary syndrome and describe the impact of echocardiography on clinical management in this setting., Methods and Results: Patients with acute coronary syndrome underwent both hand-held and transthoracic echocardiographies with agreement between key imaging parameters assessed using kappa statistics. The immediate clinical impact of hand-held echocardiography in this population was systematically evaluated. Overall, 262 patients (65 ± 12 years, 71% male) participated. Agreement between hand-held and transthoracic echocardiographies was good-to-excellent (kappa 0.60-1.00) with hand-held echocardiography having an overall negative predictive value of 95%. Hand-held echocardiography was performed rapidly (7.7 ± 1.6 min) and completed a median of 5 (interquartile range 3-20) h earlier than transthoracic echocardiography. Systematic hand-held echocardiography in all patients with acute coronary syndrome identified an important cardiac abnormality in 50%, and the clinical management plan was changed by echocardiography in 42%. In 85% of cases, hand-held echocardiography was sufficient for patient decision-making, and transthoracic echocardiography was no longer deemed necessary., Conclusion: In patients with acute coronary syndrome, hand-held echocardiography provides comparable results to transthoracic echocardiography, can be more rapidly applied, and gives sufficient imaging information for decision-making in the vast majority of patients. Systematic echocardiography has clinical impact in half of patients, supporting the clinical utility of echocardiography in this population and providing an evidence base for current guidelines., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Background: Computed tomography attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only utilized for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and evaluate these measures for all-cause mortality (ACM) risk stratification., Methods: We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry (four sites), to define chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle (SM), subcutaneous, intramuscular (IMAT), visceral (VAT), and epicardial (EAT) adipose tissues were quantified between automatically-identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation, and indexed volumes were evaluated for predicting ACM, adjusting for established risk factors and 18 other body compositions measures via Cox regression models and Kaplan-Meier curves., Findings: End-to-end processing time was <2 minutes/scan with no user interaction. Of 9918 patients studied, 5451(55%) were male. During median 2.5 years follow-up, 610 (6.2%) patients died. High VAT, EAT and IMAT attenuation were associated with increased ACM risk (adjusted hazard ratio (HR) [95% confidence interval] for VAT: 2.39 [1.92, 2.96], p<0.0001; EAT: 1.55 [1.26, 1.90], p<0.0001; IMAT: 1.30 [1.06, 1.60], p=0.0124). Patients with high bone attenuation were at lower risk of death as compared to subjects with lower bone attenuation (adjusted HR 0.77 [0.62, 0.95], p=0.0159). Likewise, high SM volume index was associated with a lower risk of death (adjusted HR 0.56 [0.44, 0.71], p<0.0001)., Interpretations: CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers which can be automatically measured and offer important additional prognostic value., Competing Interests: Declaration of interests RJM received grant support and consulting fees from Pfizer. TDR received research grant support from GE Healthcare and Advanced Accelerator Applications. AJE received speaker fees from Ionetix, consulting fees from W. L. Gore & Associates, authorship fees from Wolters Kluwer Healthcare. AJE served on a scientific advisory board for Canon Medical Systems and received grants from Attralus, Bruker, Canon Medical Systems, Eidos Therapeutics, Intellia Therapeutics, Ionis Pharmaceuticals, Neovasc, Pfizer, Roche Medical Systems, and W. L. Gore & Associates. EJM received grant support from and is a consultant for GE Healthcare. MB was supported by a research award from the Kosciuszko Foundation – The American Centre of Polish Culture. DB and PS participate in software royalties for QPS software at Cedars-Sinai Medical Center. DB served as a consultant for GE Healthcare. PS received research grant support from Siemens Medical Systems, and consulting fees from Synektik S.A. Other Authors declared no competing interests.

Background: While low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI)-driven image framework for image assessment., Methods: Patients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs)., Results: Of 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), which was higher than that of an AI CTAC model (0.78 [0.71-0.85]), and AI hybrid model (0.79 [0.72-0.86]) incorporating CTAC and MPI data (p<0.001 for all)., Conclusion: In patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.