Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach

作者：Juan Manuel Zambrano Chaves, Andrew L. Wentland, Arjun Desai, Imon Banerjee, Gurkiran Kaur, Ramón Correa, Robert D. Boutin, David J. Maron, Fátima Rodríguez, Alexander T. Sandhu, Daniel L. Rubin, Akshay Chaudhari, Bhavik N. Patel · 发表于：Scientific Reports · 年份：2023 · DOI：10.1038/s41598-023-47895-y · 被引用次数：37 · 研究领域：Cardiac Imaging and Diagnostics、Cardiovascular Function and Risk Factors、Radiomics and Machine Learning in Medical Imaging

Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events-the leading cause of global mortality-have known limitations and may be improved by imaging biomarkers. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8139 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient's electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data, can enhance IHD risk assessment and aid primary prevention efforts for IHD. To further promote research, we release the Opportunistic L3 Ischemic heart disease (OL3I) dataset, the first public multimodal dataset for opportunistic CT prediction of IHD.