Automatic measuring of coronary atherosclerosis from medicolegal autopsy photographs based on deep learning techniques.

Abstract

A diagnosis of atherosclerotic cardiovascular disease is critical importance in forensic medicine, particularly because severe atherosclerosis is known to be associated with a high risk of sudden death. In South Korea, the assessment of coronary atherosclerosis during autopsy largely depends on the forensic pathologist's visual measurements, which may limit diagnostic accuracy. The objective of this study was to develop a deep learning algorithm for rapid and precise assessment of coronary atherosclerosis and to identify factors influencing the model's prediction of atherosclerosis severity. A total of 3,717 digital photographs were retrospectively extracted from a database of 1,920 forensic autopsies, with one image each selected for the left anterior descending coronary artery and the right coronary artery. The deep learning algorithm developed in this study demonstrated a high level of agreement (0.988, 95% CI: 0.985-0.990) and absolute agreement (0.986, 95% CI: 0.978-0.991) between predicted and ground truth atherosclerosis values on the test set. The model demonstrated strong overall performance on the test set, achieving a weighted F1-score of 0.904. However, the class-wise F1-scores were 0.957 for mild, 0.785 for moderate, and 0.876 for severe grades, indicating that performance was lowest for the moderate grade. Additionally, decomposition, stent implantation, and thrombi did not have a statistically significant impact on coronary atherosclerosis assessment except for calcification. Although enhancing model performance for moderate grades remains a challenge, this study's findings demonstrate the potential of artificial intelligence as a practical tool for assessing coronary atherosclerosis in autopsy photographs.