Machine-learning based prediction of obstructive coronary artery disease using integrated submodules of clinical information, chest x-ray, and electrocardiography

Abstract

Abstract Background The early diagnosis of obstructive coronary artery disease (CAD) is critical. However, delays in diagnosis may occur due to subsequent high-cost tests, contributing to substantial medical expenses. A more insightful interpretation of fundamental diagnostic tools, such as chest x-ray (CXR) and electrocardiography (ECG), could mitigate medical costs and facilitate a timely diagnosis. Purpose This study explores the application of machine learning to interpret each modality and create a consolidated prediction model for significant CAD, aiming to construct a diagnostic model based on primary tests, including CXR, ECG, and clinical information. Methods Clinical information-ECG-CXR paired data were generated from 19,140 patients, with CAD confirmed through coronary angiography (CAG). Machine learning (ML) was employed to develop submodules for each modality, and various integration methods were explored. The area under the curve (AUC) served as the outcome metric for predicting obstructive CAD. Results The development set comprised data from 17,976 patients, with CAG-confirmed obstructive CAD observed in approximately 60% of patients. The obstructive CAD group exhibited characteristics such as advanced age, male predominance, a higher prevalence of chest pain, and more risk factors. The ML model, incorporating clinical information, CXR, and ECG with submodules, demonstrated the optimal prediction of obstructive CAD (AUC 0.722). This model significantly outperformed the model without submodules (0.722 vs. 0.638, p<0.0001) in obstructive CAD prediction. Conclusions Through the integration of submodules encompassing clinical information and basic tests, leveraging a high-quality database, the integrated ML algorithm offers improved prediction capabilities for CAG-confirmed obstructive CAD. This underscores the potential role of machine learning in clinical decision-making based on diverse modalities with distinct characteristics.