JACC. Case reports · 2026
Reducing Diagnostic Variation With Hybrid AI Coronary CTA Interpretation.
Клинический случайСтатьяНазвание и аннотация: Перевод в очереди
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Abueh Barilee, Beshara Michael, Johnson Qwynton, Ho Wing Lam, San Lee Ryung, Page Brian John, Sharma Umesh
Аннотация
BACKGROUND: In hospitalized patients undergoing coronary computed tomography angiography (CCTA), variability in stenosis interpretation may contribute to missed obstructive coronary artery disease (CAD) or unnecessary invasive coronary angiography.
PROJECT SUMMARY: We retrospectively evaluated inpatient CCTA interpretation workflows at a tertiary academic center between 2023 and 2025. Among 177 hospitalized patients undergoing CCTA, 139 had both artificial intelligence (AI)-assisted and physician interpretations available for comparison. Twenty patients underwent same-admission invasive coronary angiography, with 19 included in final invasive angiographic analysis. AI-assisted interpretation demonstrated higher agreement with invasive coronary angiography than physician interpretation within the exploratory invasive angiography subgroup (78.6% vs 55.0%). Discordance patterns were primarily driven by false-negative physician interpretations and AI-assisted stenosis overestimation. Significant CAD was identified in 73.7% of the invasive angiography subgroup, with 52.6% ultimately undergoing revascularization.
TAKE-HOME MESSAGE: Hybrid AI-physician interpretation workflows may support more consistent inpatient CAD evaluation while preserving physician-guided clinical decision making.
PROJECT SUMMARY: We retrospectively evaluated inpatient CCTA interpretation workflows at a tertiary academic center between 2023 and 2025. Among 177 hospitalized patients undergoing CCTA, 139 had both artificial intelligence (AI)-assisted and physician interpretations available for comparison. Twenty patients underwent same-admission invasive coronary angiography, with 19 included in final invasive angiographic analysis. AI-assisted interpretation demonstrated higher agreement with invasive coronary angiography than physician interpretation within the exploratory invasive angiography subgroup (78.6% vs 55.0%). Discordance patterns were primarily driven by false-negative physician interpretations and AI-assisted stenosis overestimation. Significant CAD was identified in 73.7% of the invasive angiography subgroup, with 52.6% ultimately undergoing revascularization.
TAKE-HOME MESSAGE: Hybrid AI-physician interpretation workflows may support more consistent inpatient CAD evaluation while preserving physician-guided clinical decision making.