TY  - JOUR
AU  - Gong, Eun Jeong
AU  - Bang, Chang Seok
AU  - Lee, Jae Jun
AU  - Jeong, Hae Min
AU  - Baik, Gwang Ho
AU  - Jeong, Jae Hoon
AU  - Dick, Sigmund
AU  - Lee, Gi Hun
PY  - 2023
DA  - 2023/10/30
TI  - Clinical Decision Support System for All Stages of Gastric Carcinogenesis in Real-Time Endoscopy: Model Establishment and Validation Study
JO  - J Med Internet Res
SP  - e50448
VL  - 25
KW  - atrophy
KW  - intestinal metaplasia
KW  - metaplasia
KW  - deep learning
KW  - endoscopy
KW  - gastric neoplasms
KW  - neoplasm
KW  - neoplasms
KW  - internal medicine
KW  - cancer
KW  - oncology
KW  - decision support
KW  - real time
KW  - gastrointestinal
KW  - gastric
KW  - intestinal
KW  - machine learning
KW  - clinical decision support system
KW  - CDSS
KW  - computer aided
KW  - diagnosis
KW  - diagnostic
KW  - carcinogenesis
AB  - Background: Our research group previously established a deep-learning–based clinical decision support system (CDSS) for real-time endoscopy-based detection and classification of gastric neoplasms. However, preneoplastic conditions, such as atrophy and intestinal metaplasia (IM) were not taken into account, and there is no established model that classifies all stages of gastric carcinogenesis. Objective: This study aims to build and validate a CDSS for real-time endoscopy for all stages of gastric carcinogenesis, including atrophy and IM. Methods: A total of 11,868 endoscopic images were used for training and internal testing. The primary outcomes were lesion classification accuracy (6 classes: advanced gastric cancer, early gastric cancer, dysplasia, atrophy, IM, and normal) and atrophy and IM lesion segmentation rates for the segmentation model. The following tests were carried out to validate the performance of lesion classification accuracy: (1) external testing using 1282 images from another institution and (2) evaluation of the classification accuracy of atrophy and IM in real-world procedures in a prospective manner. To estimate the clinical utility, 2 experienced endoscopists were invited to perform a blind test with the same data set. A CDSS was constructed by combining the established 6-class lesion classification model and the preneoplastic lesion segmentation model with the previously established lesion detection model. Results: The overall lesion classification accuracy (95% CI) was 90.3% (89%-91.6%) in the internal test. For the performance validation, the CDSS achieved 85.3% (83.4%-97.2%) overall accuracy. The per-class external test accuracies for atrophy and IM were 95.3% (92.6%-98%) and 89.3% (85.4%-93.2%), respectively. CDSS-assisted endoscopy showed an accuracy of 92.1% (88.8%-95.4%) for atrophy and 95.5% (92%-99%) for IM in the real-world application of 522 consecutive screening endoscopies. There was no significant difference in the overall accuracy between the invited endoscopists and established CDSS in the prospective real-clinic evaluation (P=.23). The CDSS demonstrated a segmentation rate of 93.4% (95% CI 92.4%-94.4%) for atrophy or IM lesion segmentation in the internal testing. Conclusions: The CDSS achieved high performance in terms of computer-aided diagnosis of all stages of gastric carcinogenesis and demonstrated real-world application potential. 
SN  - 1438-8871
UR  - https://www.jmir.org/2023/1/e50448
UR  - https://doi.org/10.2196/50448
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37902818
DO  - 10.2196/50448
ID  - info:doi/10.2196/50448
ER  -