Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence

Jonas, Rebecca; Earls, James P.; Marques, Hugo; Chang, Hyuk‐Jae; Choi, Jung Hyun; Doh, Joon‐Hyung; Her, Ae-Young; Koo, Bon Kwon; Nam, Chang‐Wook; Park, Hyung‐Bok; Shin, Sanghoon; Cole, Jason; Gimelli, Alessia; Khan, Muhammad Akram; Lü, Bin; Gao, Yang; Nabi, Faisal; Nakazato, Ryota; Schoepf, U. Joseph; Driessen, Roel; Bom, Michiel J.; Thompson, Randall C.; Jang, James J.; Ridner, Michael; Rowan, Chris; Avelar, Erick; Généreux, Philippe; Knaapen, Paul; Waard, Guus A. de; Andreini, Daniele; Al‐Mallah, Mouaz H.; Jennings, Robert J.; Crabtree, Tami; Villines, Todd C.; Min, James K.; Choi, Andrew D.

doi:10.1136/openhrt-2021-001832

Cited by 12 publications

(9 citation statements)

References 27 publications

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“…Here the data show that patients with diabetes have similar plaque composition in obstructive and nonobstructive lesions. This aligns with the findings of a prior study on age-based plaque composition that showed that the plaque profile becomes less distinct with more varied APC composition in older patients with longstanding disease ( 37 ). It also suggests that additional components other than distinct APCs and stenosis may be responsible for ischemia-inducing lesions in this population.…”

Section: Discussionsupporting

confidence: 89%

Diabetes, Atherosclerosis, and Stenosis by AI

Jonas

Earls²,

Marques³

et al. 2023

Diabetes Care

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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.

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Section: Discussionsupporting

confidence: 89%

Diabetes, Atherosclerosis, and Stenosis by AI

Jonas

Earls²,

Marques³

et al. 2023

Diabetes Care

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“… 9 This FDA-cleared software service utilizes a series of validated convolutional neural networks for image quality assessment, coronary segmentation and labelling, lumen wall evaluation, vessel contour determination, and plaque characterization. Prior validation of AI-QCT has been reported in multi-centre trials using expert consensus, quantitative coronary angiography, and fractional flow reserve (FFR) as previously published, 9 , 10 , 12 as well as intravascular ultrasound. 15 The algorithm first produces a centreline, lumen, and outer vessel wall contouring for every phase available and subsequently selects the optimal series for analysis.…”

Section: Methodsmentioning

confidence: 99%

Impact of atherosclerosis imaging-quantitative computed tomography on diagnostic certainty, downstream testing, coronary revascularization, and medical therapy: the CERTAIN study

Nurmohamed,

Cole,

Budoff

et al. 2024

European Heart Journal - Cardiovascular Imaging

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Aims The incremental impact of Atherosclerosis Imaging-Quantitative Computed Tomography (AI-QCT) on diagnostic certainty and downstream patient management is not yet known. The aim of the present study was to compare the clinical utility of routine implementation of AI-QCT versus conventional visual coronary CT angiography (CCTA) interpretation. Methods and Results In this multicenter crossover study in 5 expert CCTA sites, 750 consecutive adult patients referred for CCTA were prospectively recruited. Blinded to the AI-QCT analysis, site physicians established patient diagnosis and plans for downstream non-invasive testing, coronary intervention and medication management based on the conventional site assessment. Next, physicians were asked to repeat their assessments based upon AI-QCT results. The included patients had an age of 63.8 ± 12.2 years, 433 (57.7%) were male. Compared to conventional site CCTA evaluation, AI-QCT analysis improved physician’s confidence 2-5-fold at every step of the care pathway and was associated with change in diagnosis or management in the majority of patients (428; 57.1%; p < 0.001), including for such measures as Coronary Artery Disease-Reporting and Data System (CAD-RADS) (295; 39.3%; p < 0.001) and plaque burden (197; 26.3%; p < 0.001). After AI-QCT including ischemia assessment, the need for downstream non-invasive and invasive testing was reduced by 37.1% (p < 0.001), compared with the conventional site CCTA evaluation. Incremental to the site CCTA evaluation alone, AI-QCT resulted in statin initiation/increase an aspirin initiation in an additional 28.1% (p < 0.001) and 23.0% (p < 0.001) of patients, respectively. Conclusions Use of AI-QCT improves diagnostic certainty, and may result in reduced downstream need for non-invasive testing and increased rates of preventive medical therapy.

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“…AI offers an innovative approach to risk assessment, early disease detection, and guidance in formulating personalized intervention strategies. AI-driven algorithms, capable of analyzing extensive datasets, become indispensable tools for predicting patient outcomes and assisting healthcare professionals in real-time decision-making during cardiovascular interventions ( 20 ).…”

Section: Precision Cardiovascular Interventions Through Artificial In...mentioning

confidence: 99%

Evolutionary trends and innovations in cardiovascular intervention

Vento,

Kuntz,

Lejay

et al. 2024

Front. Med. Technol.

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Cardiovascular diseases remain a global health challenge, prompting continuous innovation in medical technology, particularly in Cardiovascular MedTech. This article provides a comprehensive exploration of the transformative landscape of Cardiovascular MedTech in the 21st century, focusing on interventions. The escalating prevalence of cardiovascular diseases and the demand for personalized care drive the evolving landscape, with technologies like wearables and AI reshaping patient-centric healthcare. Wearable devices offer real-time monitoring, enhancing procedural precision and patient outcomes. AI facilitates risk assessment and personalized treatment strategies, revolutionizing intervention precision. Minimally invasive procedures, aided by robotics and novel materials, minimize patient impact and improve outcomes. 3D printing enables patient-specific implants, while regenerative medicine promises cardiac regeneration. Augmented reality headsets empower surgeons during procedures, enhancing precision and awareness. Novel materials and radiation reduction techniques further optimize interventions, prioritizing patient safety. Data security measures ensure patient privacy in the era of connected healthcare. Modern technologies enhance traditional surgeries, refining outcomes. The integration of these innovations promises to shape a healthier future for cardiovascular procedures, emphasizing collaboration and research to maximize their transformative potential.

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Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence

Cited by 12 publications

References 27 publications

Diabetes, Atherosclerosis, and Stenosis by AI

Diabetes, Atherosclerosis, and Stenosis by AI

Impact of atherosclerosis imaging-quantitative computed tomography on diagnostic certainty, downstream testing, coronary revascularization, and medical therapy: the CERTAIN study

Evolutionary trends and innovations in cardiovascular intervention

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