Cardiovascular Imaging Through the Prism of Modern Metrics

Social media use by cardiovascular healthcare professionals in Portugal

INTRODUCTION AND OBJECTIVES

Social media (SoMe) has a flourishing role in cardiovascular (CV) medicine as a facilitator of academic communication not only during conferences and congresses, but also by scientific societies and journals. However, there is no solid data illustrating the use of SoMe by CV healthcare professionals (CVHP) in Portugal. Hence, the main goal of this national cross-sectional survey was to accurately characterize SoMe use by Portuguese CVHPs.

METHODS

A 35-item questionnaire was specifically developed for this study, approved by the Digital Health Study Group of the Portuguese Society of Cardiology (SPC), and sent, by e-mail, to the mailing list of the SPC (including 1293 potential recipients).

RESULTS AND CONCLUSION

There were 206 valid answers. Fifty-two percent of respondents were female and 58% were younger than 44 years of age with almost two out of three participants being physicians. Ninety-two percent of the survey participants reported that they are currently using SoMe; LinkedIn was the most common platform used for professional purposes. Sixty-four percent believed SoMe had had a positive impact on their clinical practice; 77% and 49% had used SoMe for acquiring and sharing information related to COVID-19, respectively. In conclusion, the majority of Portuguese CVHPs that participated in this survey are actively using SoMe, with a greater participation of those <45 years of age; its clinical impact is positive, with a leading role in the dissemination of evidence during the COVID pandemic.

AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy

BACKGROUND

Clinical reads of coronary computed tomography angiography (CTA), especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. Artificial intelligence (AI)-based solutions applied to coronary CTA may overcome these limitations.

OBJECTIVES

This study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography (AI-QCT) angiography analyses to core lab-interpreted coronary CTA, core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR).

METHODS

Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination.

RESULTS

Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8.

CONCLUSIONS

A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).

Social media to enhance engagement and science dissemination during in-person and virtual medical conferences: the SCMR 2020 and 2021 experiences: a report of the SCMR social media task force

Most cardiac imaging conferences have adopted social media as a means of disseminating conference highlights to a global audience well beyond the confines of the conference location. A deliberate and thoughtful social media campaign has the potential to increase the reach of the conference and allow for augmented engagement. The coronavirus disease 2019 (COVID-19) pandemic triggered a radical transformation in not just the delivery of healthcare but also the dissemination of science within the medical community. In the past, in-person medical conferences were an integral annual tradition for most medical professionals to stay up to date with the latest in the field. Social distancing requirements of the COVID-19 pandemic resulted in either cancelling medical conferences or shifting to a virtual format. Following suit, for the first time in its history, the 2021 Society for Cardiovascular Magnetic Resonance (SCMR) annual meeting was an all-virtual event. This called for a modified social media strategy which aimed to re-create the sociability of an in-person conference whilst also promoting global dissemination of the science being presented. This paper describes the employment of social media as well as the evolution through the SCMR scientific sessions for 2020 and 2021 that serves as a model for future cardiovascular conferences.

CT ​EvaLuation ​by ​ARtificial ​Intelligence ​For ​Atherosclerosis, Stenosis and Vascular ​MorphologY ​(CLARIFY): ​A ​Multi-center, international study

BACKGROUND

Atherosclerosis evaluation by coronary computed tomography angiography (CCTA) is promising for coronary artery disease (CAD) risk stratification, but time consuming and requires high expertise. Artificial Intelligence (AI) applied to CCTA for comprehensive CAD assessment may overcome these limitations. We hypothesized AI aided analysis allows for rapid, accurate evaluation of vessel morphology and stenosis.

METHODS

This was a multi-site study of 232 patients undergoing CCTA. Studies were analyzed by FDA-cleared software service that performs AI-driven coronary artery segmentation and labeling, lumen and vessel wall determination, plaque quantification and characterization with comparison to ground truth of consensus by three L3 readers. CCTAs were analyzed for: % maximal diameter stenosis, plaque volume and composition, presence of high-risk plaque and Coronary Artery Disease Reporting & Data System (CAD-RADS) category.

RESULTS

AI performance was excellent for accuracy, sensitivity, specificity, positive predictive value and negative predictive value as follows: >70% stenosis: 99.7%, 90.9%, 99.8%, 93.3%, 99.9%, respectively; >50% stenosis: 94.8%, 80.0%, 97.0, 80.0%, 97.0%, respectively. Bland-Altman plots depict agreement between expert reader and AI determined maximal diameter stenosis for per-vessel (mean difference -0.8%; 95% CI 13.8% to -15.3%) and per-patient (mean difference -2.3%; 95% CI 15.8% to -20.4%). L3 and AI agreed within one CAD-RADS category in 228/232 (98.3%) exams per-patient and 923/924 (99.9%) vessels on a per-vessel basis. There was a wide range of atherosclerosis in the coronary artery territories assessed by AI when stratified by CAD-RADS distribution.

CONCLUSIONS

AI-aided approach to CCTA interpretation determines coronary stenosis and CAD-RADS category in close agreement with consensus of L3 expert readers. There was a wide range of atherosclerosis identified through AI.

Impact of Social Media and Virtual Learning on Cardiology During the COVID-19 Pandemic Era and Beyond

Over the past decade, advances in digital trends and technology have greatly impacted the medical field with rapid delivery of and access to information. The field of cardiovascular medicine in particular has seen major technological advances and is well versed in the use of digital platforms and social media. In these unprecedented times of the COVID-19 pandemic, social media and other digital platforms are essential tools for communication, education, and delivery of information. In this review, we discuss the ways virtual learning and social media are changing medical education and research.