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Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved. OBJECTIVES: The study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCT) analyses to core lab-interpreted coronary computed tomography angiography (CTA), core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). BACKGROUND: Clinical reads of coronary CTA, especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. AI-based solutions applied to coronary CTA may overcome these limitations. 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

The difference between expert level (L3) reader and artificial intelligence (AI) performance for quantifying coronary plaque and plaque components is unknown.This study evaluates the interobserver variability among expert readers for quantifying the volume of coronary plaque and plaque components on coronary computed tomographic angiography (CCTA) using an artificial intelligence enabled quantitative CCTA analysis software as a reference (AI-QCT).This study uses CCTA imaging obtained from 232 patients enrolled in the CLARIFY (CT EvaLuation by ARtificial Intelligence For Atherosclerosis, Stenosis and Vascular MorphologY) study. Readers quantified overall plaque volume and the % breakdown of noncalcified plaque (NCP) and calcified plaque (CP) on a per vessel basis. Readers categorized high risk plaque (HRP) based on the presence of low-attenuation-noncalcified plaque (LA-NCP) and positive remodeling (PR; ≥1.10). All CCTAs were analyzed by an FDA-cleared software service that performs AI-driven plaque characterization and quantification (AI-QCT) for comparison to L3 readers. Reader generated analyses were compared among readers and to AI-QCT generated analyses.When evaluating plaque volume on a per vessel basis, expert readers achieved moderate to high interobserver consistency with an intra-class correlation coefficient of 0.78 for a single reader score and 0.91 for mean scores. There was a moderate trend between readers 1, 2, and 3 and AI with spearman coefficients of 0.70, 0.68 and 0.74, respectively. There was high discordance between readers and AI plaque component analyses. When quantifying %NCP v. %CP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.23, 0.34 and 0.24, respectively, compared to AI with a spearman coefficient of 0.38, 0.51, and 0.60, respectively. The intra-class correlation coefficient among readers for plaque composition assessment was 0.68. With respect to HRP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.22, 0.26, and 0.17, respectively, and a spearman coefficient of 0.36, 0.35, and 0.44, respectively.Expert readers performed moderately well quantifying total plaque volumes with high consistency. However, there was both significant interobserver variability and high discordance with AI-QCT when quantifying plaque composition.

BACKGROUND. Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. OBJECTIVE. The purpose of our study was to compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography and to assess the downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. METHODS. This study entailed a retrospective post hoc analysis of the derivation cohort of the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) trial. The study included 301 patients (88 women and 213 men; mean age, 64.4 ± 10.2 [SD] years) recruited from May 2014 to May 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software platform that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. RESULTS. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (≥ 50%) stenosis in 54% of patients, including severe (≥ 70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified nonobstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p < .001) than MPI for predicting ≥ 50% stenosis by QCA (0.88 vs 0.66), ≥ 70% stenosis by QCA (0.92 vs 0.81), and FFR < 0.80 (0.90 vs 0.71). An AI-QCT result of ≥ 50% stenosis and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥ 50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AIQCT in all patients and those showing ≥ 70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with ≥ 70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. CONCLUSION. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. CLINICAL IMPACT. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing and costs. TRIAL REGISTRATION. Clinicaltrials.gov NCT02173275.

Adverse cardiovascular events are a significant cause of mortality in end-stage renal disease (ESRD) patients. High-risk plaque anatomy may be a significant contributor. However, their atherosclerotic phenotypes have not been described. We sought to define atherosclerotic plaque characteristics (APC) in dialysis patients using artificial-intelligence augmented CCTA.We retrospectively analyzed ESRD patients referred for CCTA using an FDA approved artificial-intelligence augmented-CCTA program (Cleerly). Coronary lesions were evaluated for APCs by CCTA. APCs included percent atheroma volume(PAV), low-density non-calcified-plaque (LD-NCP), non-calcified-plaque (NCP), calcified-plaque (CP), length, and high-risk-plaque (HRP), defined by LD-NCP and positive arterial remodeling1.10 (PR).79 ESRD patients were enrolled, mean age 65.3 years, 32.9% female. Disease distribution was non-obstructive (65.8%), 1-vessel disease (21.5%), 2-vessel disease (7.6%), and 3-vessel disease (5.1%). Mean total plaque volume (TPV) was 810.0 mmOur study provides novel insight into ESRD plaque phenotypes and demonstrates that artificial-intelligence augmented CCTA analysis is feasible for CAD characterization despite severe calcification. We demonstrate elevated plaque burden and stenosis caused by predominantly non-calcified-plaque. Furthermore, the quantity of calcified-plaques increased with age, with men exhibiting increased number of 2-feature plaques and higher plaque volumes. Artificial-intelligence augmented CCTA analysis of APCs may be a promising metric for cardiac risk stratification and warrants further prospective investigation.

Aims: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA). Methods: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up. Results: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with

OBJECTIVE This study evaluates the relationship between atherosclerotic plaque characteristics (APCs) and angiographic stenosis severity in patients with and without diabetes. Whether APCs differ based on lesion severity and diabetes status is unknown. RESEARCH DESIGN AND METHODS We retrospectively evaluated 303 subjects from the Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia (CREDENCE) trial referred for invasive coronary angiography with coronary computed tomographic angiography (CCTA) and classified lesions as obstructive (≥50% stenosed) or nonobstructive using blinded core laboratory analysis of quantitative coronary angiography. CCTA quantified APCs, including plaque volume (PV), calcified plaque (CP), noncalcified plaque (NCP), low-density NCP (LD-NCP), lesion length, positive remodeling (PR), high-risk plaque (HRP), and percentage of atheroma volume (PAV; PV normalized for vessel volume). The relationship between APCs, stenosis severity, and diabetes status was assessed. RESULTS Among the 303 patients, 95 (31.4%) had diabetes. There were 117 lesions in the cohort with diabetes, 58.1% of which were obstructive. Patients with diabetes had greater plaque burden (P = 0.004). Patients with diabetes and nonobstructive disease had greater PV (P = 0.02), PAV (P = 0.02), NCP (P = 0.03), PAV NCP (P = 0.02), diseased vessels (P = 0.03), and maximum stenosis (P = 0.02) than patients without diabetes with nonobstructive disease. APCs were similar between patients with diabetes with nonobstructive disease and patients without diabetes with obstructive disease. Diabetes status did not affect HRP or PR. Patients with diabetes had similar APCs in obstructive and nonobstructive lesions. CONCLUSIONS Patients with diabetes and nonobstructive stenosis had an association to similar APCs as patients without diabetes who had obstructive stenosis. Among patients with nonobstructive disease, patients with diabetes had more total PV and NCP.

Objectives: This study evaluates the relationship between atherosclerotic plaque characteristics (APCs) and angiographic stenosis severity in patients with and without diabetes. Background: Whether APCs differ based on lesion severity and diabetic status is unknown. Methods: We retrospectively evaluated 303 subjects from the CREDENCE trial referred for invasive coronary angiography with coronary computed tomographic angiography (CCTA) and classified lesions as obstructive (>50% stenosed) or non-obstructive using blinded core laboratory analysis of quantitative coronary angiography. CCTA quantified APCs including plaque volume (PV), calcified plaque (CP), noncalcified plaque (NCP), low density noncalcified plaque (LD-NCP), lesion length, positive remodeling (PR), high-risk plaque (HRP) and percent atheroma volume (PAV; plaque volume normalized for vessel volume). The relationship between APCs, stenosis severity and diabetic status was assessed. Results: Among the 303 patients, 95 (31.4%) had diabetes. There were 117 lesions in the diabetic cohort, 58.1% of which were obstructive. Patients with diabetes had greater plaque burden (p=0.004). Patients with diabetes and nonobstructive disease had greater PV (p=0.02), PAV (p=0.02), NCP (p=0.03), PAV NCP (p=0.02), diseased vessels (p=0.03), and max stenosis (p=0.02) than nondiabetic patients with nonobstructive disease. APCs were similar between diabetics with non-obstructive disease and non-diabetic patients with obstructive disease. Diabetic status did not affect HRP or PR. Patients with diabetes had similar APCs in obstructive and non-obstructive lesions. Conclusions: Patients with diabetes and non-obstructive stenosis had an association to similar APCs as patients without diabetes who have obstructive stenosis. Among patients with non-obstructive disease, patients with diabetes had more total PV and NCP.

Although atherosclerosis represents the primary driver of coronary artery disease, evaluation and treatment approaches have historically relied upon indirect markers of atherosclerosis that include surrogates (cholesterol), signs (angina) and sequelae (ischemia) of atherosclerosis. Direct quantification and characterization of atherosclerosis may encourage a precision heart care paradigm that improves diagnosis, risk stratification, therapeutic decision making and longitudinal disease tracking in a personalized fashion.The American College of Cardiology Innovations in Prevention Working Group introduces the Atherosclerosis Treatment Algorithms that personalize medical interventions based upon atherosclerosis findings from coronary computed tomography angiography and cardiovascular risk factors. Through integration of coronary computed tomography angiography -based atherosclerosis evaluation, clinical practice guidelines and contemporary randomized controlled trial evidence, the Atherosclerosis Treatment Algorithms leverage patient-specific atherosclerosis burden and progression as primary targets for therapeutic intervention. After defining stages of atherosclerosis severity by coronary computed tomography angiography, Atherosclerosis Treatment Algorithms are described for worsening stages of atherosclerosis for patients with lipid disorders, diabetes, hypertension, obesity, and tobacco use. The Working Group anticipates a rapid pace of research in the field, and the manuscript concludes by providing perspectives on future needs that may improve efforts to optimize precision prevention of coronary artery disease. Importantly, the Atherosclerosis Treatment Algorithms are not endorsed by the American College of Cardiology nor should the Atherosclerosis Treatment Algorithms be interpreted as a statement of American College of Cardiology policy.We describe a precision heart care approach that emphasizes atherosclerosis as the primary disease target for evaluation and treatment. To our knowledge, this effort represents the first to propose coronary atherosclerosis burden and progression to personalize therapy selection and therapy changes, respectively.

Background: Atherosclerotic plaque characterization by coronary computed tomography angiography (CCTA) enables quantification of coronary artery disease (CAD) burden and type, which has been demonstrated as the strongest discriminant of future risk of major adverse cardiac events (MACE). To date, there are no clinically useful thresholds to assist with understanding a patient's disease burden and guide diagnosis and management, as there exists with coronary artery calcium (CAC) scoring. The purpose of this manuscript is to establish clinically relevant plaque stages and thresholds based on evidence from invasive angiographic stenosis (ICA) and fractional flow reserve (FFR) data. Methods: 303 patients underwent CCTA prior to ICA and FFR for an AHA/ACC clinical indication. Quantitative computed tomography (QCT) was performed for total plaque volume (TPV, mm(3)) and percent atheroma volume (PAV, %). We segmented atherosclerosis by composition for low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), and calcified plaque (CP). ICAs were evaluated by quantitative coronary angiography (QCA) for all coronary segments for % diameter stenosis. The relationship of atherosclerotic plaque burden and composition by QCT to ICA stenosis extent and severity by QCA and presence of ischemia by FFR was assessed to develop 4 distinct disease stages. Results: The mean age of the patients was 64.4 & PLUSMN; 10.2 years; 71% male. At the 50% QCA stenosis threshold, QCT revealed a mean PAV of 9.7 (& PLUSMN;8.2)% and TPV of 436 (& PLUSMN;444.9)mm(3) for those with non-obstructive CAD; PAV of 11.7 (& PLUSMN;8.0)% and TPV of 549.3 (& PLUSMN;408.3) mm(3) for 1 vessel disease (1VD), PAV of 17.8 (& PLUSMN;9.8)% and TPV of 838.9 (& PLUSMN;550.7) mm(3) for 2VD, and PAV of 19.2 (& PLUSMN;8.2)% and TPV of 799.9 (& PLUSMN;357.4) mm(3) for 3VD/left main disease (LMD). Non-ischemic patients (FFR > 0.8) had a mean PAV of 9.2 (& PLUSMN;7.3) % and TPV of 422.9 (& PLUSMN;387.9 mm(3)) while patients with at least one vessel ischemia (FFR & LE;0.8) had a PAV of 15.2 (& PLUSMN;9.5)% and TPV of 694.6 (& PLUSMN;485.1). Definition of plaque stage thresholds of 0, 250, 750 mm(3) and 0, 5, and 15% PAV resulted in 4 clinically distinct stages in which patients with no, non-obstructive, single VD and multi-vessel disease were optimally distributed. Conclusion: Atherosclerotic plaque burden by QCT is related to stenosis severity and extent as well as ischemia. We propose staging of CAD atherosclerotic plaque burden using the following definitions: Stage 0 (Normal, 0% PAV, 0 mm(3) TPV), Stage 1 (Mild, > 0-5% PAV or > 0-250 mm(3) TPV), Stage 2 (Moderate, > 5-15% PAV or > 250-750 mm(3) TPV) and Stage 3 (Severe, > 15% PAV or > 750 mm(3) TPV).

The three most common mechanisms of thrombosis in acute coronary syndrome (ACS) are plaque rupture, plaque erosion and calcified nodule. Plaque erosion occurs over an intact fibrous cap, commonly over pathological intimal thickening (PIT) rather than thin cap fibroatheroma (TCFA), and is more common among younger and female patients.1 A very rare mechanism of ACS is spontaneous coronary artery dissection (SCAD), resulting from the formation of an intramural hematoma and/or intimal disruption.2 We present a case of sudden coronary death with the appearance of SCAD on coronary computed tomography angiography (CCTA).

The purpose of this review is to highlight the most impactful, educational, and frequently downloaded articles published in the Journal of Cardiovascular Computed Tomography (JCCT) for the year 2020. The JCCT reached new records in 2020 for the number of research submissions, published manuscripts, article downloads and social media impressions. The articles in this review were selected by the Editorial Board of the JCCT and are comprised predominately of original research publications in the following categories: Coronavirus disease 2019 (COVID-19), coronary artery disease, coronary physiology, structural heart disease, and technical advances. The Editorial Board would like to thank each of the authors, peer-reviewers and the readers of JCCT for making 2020 one of the most successful years in its history, despite the challenging circumstances of the global COVID-19 pandemic.

Publisher Copyright: © 2022 The Authors Objectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had 400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters. publishersversion published

Sudden death is the most abrupt clinical presentation of acute coronary syndrome. The presence of acute luminal thrombosis is the histopathological hallmark of sudden coronary death. There are 3 main etiologies that can give rise to an acute luminal thrombus: plaque rupture, plaque erosion and, less frequently, eruptive calcified nodules. Coronary computed tomography angiography (CCTA) has the ability to identify high-risk plaque features of coronary artery disease that are associated with future adverse cardiac events. In this report, we illustrate 2 cases of suspected sudden coronary death with a thorough description of how CCTA can be employed to detect high-risk plaque features using histopathology as a gold standard.

Coronary artery disease is a leading cause of death worldwide. There has been a myriad of advancements in the field of cardiovascular imaging to aid in diagnosis, treatment, and prevention of coronary artery disease. The application of artificial intelligence in medicine, particularly in cardiovascular medicine has erupted in the past decade. This article serves to highlight the highest yield articles within cardiovascular imaging with an emphasis on coronary CT angiography methods for % stenosis evaluation and atherosclerosis quantification for the general cardiologist. The paper finally discusses the evolving paradigm of implementation of artificial intelligence in real world practice.

ObjectiveThe study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT).MethodsThis is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (ResultsThe cohort was 64.4±10.2 years and 29% women. Overall, patients >65 had more PV and CP than patients 65 had more CP than younger patients in both obstructive (29.2 mm3 vs 48.2 mm3; p3 vs 49.4 mm3; pConclusionAI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment.

In the past decade, advances in coronary computed tomography angiography (CTA) technology have resulted in high sensitivity and negative predictive value in detecting coronary artery disease (CAD) compared with invasive coronary angiography, particularly for patients with mild or severe stenosis. However, anatomical evaluation of CAD by CTA has modest specificity for patients with intermediate-grade stenoses. The recent development of the use of cardiac computed tomography–derived fractional flow reserve (FFR-CT) seeks to address this gap as a proposed method of functional assessment of CAD by CTA. In this article we aim to mix common clinical cases with the current technical methods, validation, outcomes, and registry studies as well as the technical, financial and research limitations of FFR-CT analysis to guide the cardiac imaging specialist in evaluating this technique. FFR-CT analysis may help reduce additive functional testing for the smaller proportion of patients with intermediate stenosis undergoing coronary CTA where the atherosclerosis significance is uncertain.

Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved. 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. proof published

The COVID-19 pandemic has affected patient care deliver throughout the world, resulting in a greater emphasis on efficiently and safety. In this article, we discuss the experiences of several North American centers in utilizing cardiac CT during the pandemic. We also provide a case-based overview which highlights the advantages of cardiac CT in evaluating the following scenarios: (1) patients with possible myocardial injury versus myocardial infarction; (2) patients with acute chest pain; (3) patients with stable chest pain; (4) patients with possible intracardiac thrombus; (5) patients with valvular heart disease. For each scenario, we also provide an overview of various societies recommendations which have highlighted the use of cardiac CT during different phases of the COVID-19 pandemic. We hope that the advantages of cardiac CT that have been realized during the pandemic can help promote wider adoption of this technique and improved coverage and payment by payors.

In the United States, non-obstructive coronary disease has been on the rise, and each year, nearly one million adults suffer myocardial infarction, 70% of which are non-ST-segment elevation myocardial infarction (NSTEMI). In addition, approximately 15% of patients suffering NSTEMI will have subsequent readmission for a recurrent acute coronary syndrome (ACS). While invasive angiography remains the standard of care in the diagnostic and therapeutic approach to these patients, these methods have limitations that include procedural complications, uncertain specificity in diagnosis of the culprit lesion in patients with multi-vessel coronary artery disease (CAD), and challenges in following coronary disease over time. The role of coronary computed tomography angiography (CCTA) for evaluating patients with both stable and acute chest pain has seen a paramount upshift in the last decade. This paper reviews the established role of CCTA for the rapid exclusion of obstructive plaque in troponin negative acute chest pain, while exploring opportunities to address challenges in the current approach to evaluating NSTEMI.

The purpose of this review is to summarize the work published by the Journal of Cardiovascular Computed Tomography (JCCT) for the year 2019, highlighting original research and new guidelines.

In emergency department (ED) cases with clinically suspected diverticulitis, diagnostic imaging is often needed for diagnostic confirmation, to exclude complications, and to direct patient management. Patients typically undergo a CT scan in the ED; however, in a subset of cases with suspected diverticulitis, point-of-care ultrasound (POCUS) may provide sufficient data to confirm the diagnosis and ascertain a safe plan for outpatient management.We review the main sonographic features of diverticulitis and discuss the diagnostic accuracy and potential benefits of a POCUS First model.