Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray

• URL: http://arxiv.org/abs/2403.18756v1
• Date: Wed, 27 Mar 2024 16:56:14 GMT
• Title: Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray
• Authors: Guglielmo Gallone, Francesco Iodice, Alberto Presta, Davide Tore, Ovidio de Filippo, Michele Visciano, Carlo Alberto Barbano, Alessandro Serafini, Paola Gorrini, Alessandro Bruno, Walter Grosso Marra, James Hughes, Mario Iannaccone, Paolo Fonio, Attilio Fiandrotti, Alessandro Depaoli, Marco Grangetto, Gaetano Maria de Ferrari, Fabrizio D'Ascenzo,
• Abstract summary: Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
• Score: 86.38767955626179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58.4% male, median age 63 [51-74] years) with available paired chest x-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on an temporally-independent cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by the area under the curve (AUC) was the primary outcome. Overall, median AI-CAC score was 35 (0-388) and 28.9% patients had no AI-CAC. AUC of the AI-CAC model to identify a CAC>0 was 0.90 in the internal validation cohort and 0.77 in the external validation cohort. Sensitivity was consistently above 92% in both cohorts. In the overall cohort (n=540), among patients with AI-CAC=0, a single ASCVD event occurred, after 4.3 years. Patients with AI-CAC>0 had significantly higher Kaplan Meier estimates for ASCVD events (13.5% vs. 3.4%, log-rank=0.013). Conclusion. The AI-CAC model seems to accurately detect subclinical atherosclerosis on chest x-ray with elevated sensitivity, and to predict ASCVD events with elevated negative predictive value. Adoption of the AI-CAC model to refine CV risk stratification or as an opportunistic screening tool requires prospective evaluation.

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