October 7, 2024The International Journal of Cardiovascular ImagingOpen Access

Artificial intelligence-enhanced detection of subclinical coronary artery disease in athletes: diagnostic performance and limitations

View Full Paper

Ask AI

Bookmark

Why the study?

The study evaluated the diagnostic performance of AI-based CCTA for detecting CAD and assessing FFR in asymptomatic male marathon runners.

Does AI-based CCTA analysis accurately detect coronary artery disease compared to visual analysis in asymptomatic male marathon runners?

Structured PICO

Does AI-based CCTA analysis accurately detect coronary artery disease compared to visual analysis in asymptomatic male marathon runners?

Population

94 asymptomatic male marathon runners over the age of 45 (mean age 52.7) prospectively recruited for CAD screening.

Intervention

Artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) analysis using CorEx and Spimed-AI models to detect significant CAD and assess fractional flow reserve (FFR).

Comparator

Visual CCTA analysis by two experienced radiologists.

Outcome

Diagnostic performance (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) for detecting any CAD (>0% stenosis) and significant CAD (≥50% diameter stenosis) and assessing fractional flow reserve (FFR ≤ 0.8).surrogate

AI-enhanced CCTA demonstrates high sensitivity and negative predictive value for detecting significant coronary artery disease in asymptomatic athletes, though its low positive predictive value highlights a tendency for overestimation.

Limitations

Exclusive focus on male marathon runners limits generalizability to other populations.
Did not utilize invasive FFR measurements as a gold standard for validating AI-derived FFR.
Data were collected between 2012 and 2014, and advancements in imaging techniques and AI algorithms since then may influence applicability.
Need for reconstructed images to be sent to a local server for AI analysis presents practical challenges.

Implication

AI-CCTA feasibility in asymptomatic runners is hypothesis-generating; leaves open outcome validation before clinical adoption.

Abstract

PURPOSE: This study evaluates the diagnostic performance of artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) for detecting coronary artery disease (CAD) and assessing fractional flow reserve (FFR) in asymptomatic male marathon runners. MATERIAL AND METHODS: We prospectively recruited 100 asymptomatic male marathon runners over the age of 45 for CAD screening. CCTA was analyzed using AI models (CorEx and Spimed-AI) on a local server. The models focused on detecting significant CAD (≥ 50% diameter stenosis, CAD-RADS 3, 4, or 5) and distinguishing hemodynamically significant stenosis (FFR ≤ 0.8) from non-significant stenosis (FFR > 0.8). Statistical analysis included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. RESULTS: The AI model demonstrated high sensitivity, with 91.2% for any CAD and 100% for significant CAD, and high NPV, with 92.7% for any CAD and 100% for significant CAD. The diagnostic accuracy was 73.4% for any CAD and 90.4% for significant CAD. However, the PPV was lower, particularly for significant CAD (25.0%), indicating a higher incidence of false positives. CONCLUSION: AI-enhanced CCTA is a valuable non-invasive tool for detecting CAD in asymptomatic, low-risk populations. The AI model exhibited high sensitivity and NPV, particularly for identifying significant stenosis, reinforcing its potential role in screening. However, limitations such as a lower PPV and overestimation of disease indicate that further refinement of AI algorithms is needed to improve specificity. Despite these challenges, AI-based CCTA offers significant promise when integrated with clinical expertise, enhancing diagnostic accuracy and guiding patient management in low-risk groups.