Deep learning-based clinical decision support system for gastric neoplasms in real-time endoscopy: development and validation study

Background Deep learning models have previously been established to predict the histopathology and invasion depth of gastric lesions using endoscopic images. This study aimed to establish and validate a deep learning-based clinical decision support system (CDSS) for the automated detection and classification ( diagnosis and invasion depth prediction) of gastric neoplasms in real-time endoscopy. Methods The same 5017 endoscopic images that were employed to establish previous models were used for the training data. The primary outcomes were: ( i) the lesion detection rate for the detection model, and (ii) the lesion classification accuracy for the classification model. For performance validation of the lesion detection model, 2524 real- time procedures were tested in a randomized pilot study. Consecutive patients were allocated either to CDSS-assisted or conventional screening endoscopy. The lesion detection rate was compared between the groups. For performance validation of the lesion classification model, a prospective multicenter external test was conducted using 3976 novel images from five institutions. Results The lesion detection rate was 95.6% (internal test). On performance validation, CDSS-assisted endoscopy showed a higher lesion detection rate than conventional screening endoscopy, although statistically not significant (2.0% vs. 1.3%; P = 0.21) ( randomized study). The lesion classification rate was 89.7% in the four-class classification (advanced gastric cancer, early gastric cancer, dysplasia, and non- neoplastic) and 89.2% in the invasion depth prediction ( mucosa confined or submucosa invaded; internal test). On performance validation, the CDSS reached 81.5% accuracy in the four- class classification and 86.4% accuracy in the binary classification (prospective multicenter external test). Conclusions The CDSS demonstrated its potential for reallife clinical application and high performance in terms of lesion detection and classification of detected lesions in the stomach.