In Endoscopy ; h5-index 58.0
BACKGROUND : Authors previously established deep-learning models 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 5,017 endoscopic images, which were employed to establish previous models, were used for the training data. The primary outcomes were the 1. Lesion-detection rate for the detection model and 2. Lesion-classification accuracy for the classification model. For the performance validation of lesion-detection model, 2,524 real-time procedures were tested in a randomized pilot study. Consecutive patients were allocated either to CDSS-assisted screening endoscopy or conventional screening endoscopy. The lesion-detection rate was compared between the groups. For the performance validation of lesion-classification model, a prospective multicenter external-test was conducted using 3,976 novel images from five institutions.
RESULTS : The lesion-detection rate was 95.6% (internal-test). For the performance validation, CDSS-assisted endoscopy showed higher lesion-detection rate compared to conventional screening endoscopy, although statistically not significant (2.0% vs. 1.3%, P-value=0.21) (randomized study). The lesion-classification rate was 89.7% in the four-class classification (advanced-, early gastric cancer, dysplasia, and non-neoplasm) and 89.2% in the invasion-depth prediction (mucosa-confined or submucosa-invaded) (internal-test). For the performance validation, 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 potential for real-clinic application and high performance in terms of lesion detection and classification of detected lesions in the stomach.
Gong Eun Jeong, Bang Chang Seok, Lee Jae Jun, Baik Gwang Ho, Lim Hyun, Jeong Jae Hoon, Choi Sung Won, Cho Joonhee, Kim Deok Yeol, Lee Kang Bin, Shin Seung-Il, Sigmund Dick, Moon Byeong In, Park Sung Chul, Lee Sang Hoon, Bang Ki Bae, Son Dae-Soon
2023-Feb-08
