Enhancing Prediction of Myocardial Recovery After Coronary Revascularization: Integrating Radiomics from Myocardial Contrast Echocardiography with Machine Learning

Deyi Huang,1 Xingan Yang,2 Hongbiao Ruan,3 Yushui Zhuo,1 Kai Yuan,4 Bowen Ruan,1 Fang Li1 1Department of Ultrasound, The People’s Hospital of Yuhuan, Yuhuan City, Zhejiang Province, People’s Republic of China; 2Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Linhai City, Zhejiang Province, People’s Republic of China; 3Department of Cardiology, The People’s Hospital of Yuhuan, Yuhuan City, Zhejiang Province, People’s Republic of China; 4Department of Clinical Laboratory, The People’s Hospital of Yuhuan, Yuhuan City, Zhejiang Province, People’s Republic of ChinaCorrespondence: Fang Li, Department of Ultrasound, The People’s Hospital of Yuhuan, No. 18, Changle Road, Yuhuan City, Zhejiang Province, 317600, People’s Republic of China, Tel +8613967672898, Email 3260772796@qq.comIntroduction: Chronic coronary artery disease (CAD) management often relies on myocardial contrast echocardiography (MCE), yet its effectiveness is limited by subjective interpretations and difficulty in distinguishing hibernating from necrotic myocardium. This study explores the integration of machine learning (ML) with radiomics to predict functional recovery in dyskinetic myocardial segments in CAD patients undergoing revascularization, aiming to overcome these limitations.Methods: This prospective study enrolled 55 chronic CAD patients, dividing into training (39 patients, 205 segments) and testing sets (16 patients, 68 segments). Dysfunctional myocardial segments were identified by initial wall motion scores (WMS) of ≥ 2 (hypokinesis or higher). Functional recovery was defined as a decrease of ≥ 1 grade in WMS during follow-up echocardiography. Radiomics features were extracted from dyssynergic segments in end-systolic phase MCE images across five cardiac cycles post- “flash†impulse and processed through a five-step feature selection. Four ML classifiers were trained and compared using these features and MCE parameters, to identify the optimal model for myocardial recovery prediction.Results: ...