This study aimed to develop and validate a multi-task deep learning model that uses a single-plane echocardiographic video as input to predict hypertrophic cardiomyopathy (HCM) and its associated signs.

In this retrospective study, 377 subjects were included, comprising patients with HCM and a control group (non-HCM group) who visited Renmin Hospital of Wuhan University between January 2016 and May 2025 (the two groups were matched at a ratio of approximately 1∶2). Standard transthoracic two-dimensional gray-scale echocardiographic video data of the parasternal long-axis view (PLAX) at rest were collected for each subject. The entire dataset was divided into a training set and a test set at a 4∶1 ratio using random stratified sampling. A multi-task deep learning model was trained on single-plane video data and simultaneously outputs probability estimates for predicting HCM, LVOTO, and SAM. On the training set, the model underwent 4-fold cross-validation, and the final version of the deep learning model was developed. The performance of the final model was evaluated on the test set using the area under the receiver operating characteristic curve (ROC-AUC) and the area under the precision-recall curve (PR-AUC) for the three tasks.

Among the 377 subjects, 117 had HCM and 260 were in the control group. A total of 452 videos were collected (including 179 positive HCM videos, 111 positive LVOTO videos, and 91 positive SAM videos). In the test set, the ROC-AUC and PR-AUC of the final version of the deep learning model for predicting HCM were 0.987 [95%(confidence interval, CI): 0.966-0.999] and 0.981 (95%CI: 0.945-1.000), respectively; for predicting LVOTO, they were 0.899 (95%CI: 0.769-0.978) and 0.789 (95%CI: 0.585-0.925); and for predicting SAM, they were 0.980 (95%CI: 0.943-1.000) and 0.940 (95%CI: 0.837-1.000).

This multi-task deep learning model based on single-view echocardiographic video can accurately and simultaneously identify HCM and its associated signs (LVOTO and SAM), demonstrating its potential as a real-time decision support tool and helping reduce missed diagnoses of HCM.