Development and validation of a deep learning model for severe mitral stenosis detection from chest X-rays

Background Although chest X-rays (CXRs) are widely used, diagnosing mitral stenosis (MS) based solely on CXR findings remains challenging in some cases. Objective This study aimed to develop a deep learning-based artificial intelligence (AI) model to detect MS using CXR. Methods In this retrospective study, 515 posteroanterior CXR images were analysed, including 285 from patients with MS and 230 from healthy controls. The dataset was randomly divided into training, validation and test datasets at a 7:2:1 ratio. An AI model was formulated by using the training dataset, and model performance was evaluated on the validation and test datasets using the area under the receiver operating characteristic curve (AUC), precision, recall, F1-score and accuracy. Saliency maps were generated to visualise the regions prioritised by the model. Results The model achieved an AUC of 0.99 on the validation dataset, with a precision of 0.96, recall of 0.96, F1-score of 0.96 and accuracy of 0.96. On the test dataset, the model achieved an AUC of 0.99, with a precision of 0.95, recall of 0.94, F1-score of 0.94 and accuracy of 0.94. Saliency maps highlighted regions consistent with known radiographic features of MS. Conclusion The developed deep learning-based AI model demonstrated high performance in detecting MS from CXR. This approach may provide a convenient and accessible screening tool for MS, particularly in resource-limited areas.