The diagnostic ability to classify neoplasias occurring in inflammatory bowel disease by artificial intelligence and endoscopists: A pilot study

Background and Aim Although endoscopic resection with careful surveillance instead of total proctocolectomy become to be permitted for visible low-grade dysplasia, it is unclear how accurately endoscopists can differentiate these lesions, as classifying neoplasias occurring in inflammatory bowel disease (IBDN) is exceedingly challenging due to background chronic inflammation. We evaluated a pilot model of an artificial intelligence (AI) system for classifying IBDN and compared it with the endoscopist's ability. Methods This study used a deep convolutional neural network, the EfficientNet-B3. Among patients who underwent treatment for IBDN at two hospitals between 2003 and 2021, we selected 862 non-magnified endoscopic images from 99 IBDN lesions and utilized 6 375 352 images that were increased by data augmentation for the development of AI. We evaluated the diagnostic ability of AI using two classifications: the "adenocarcinoma/high-grade dysplasia" and "low-grade dysplasia/sporadic adenoma/normal mucosa" groups. We compared the diagnostic accuracy between AI and endoscopists (three non-experts and four experts) using 186 test set images. Results The diagnostic ability of the experts/non-experts/AI for the two classifications in the test set images had a sensitivity of 60.5% (95% confidence interval [CI]: 54.5-66.3)/70.5% (95% CI: 63.8-76.6)/72.5% (95% CI: 60.4-82.5), specificity of 88.0% (95% CI: 84.7-90.8)/78.8% (95% CI: 74.3-83.1)/82.9% (95% CI: 74.8-89.2), and accuracy of 77.8% (95% CI: 74.7-80.8)/75.8% (95% CI: 72-79.3)/79.0% (95% CI: 72.5-84.6), respectively. Conclusions The diagnostic accuracy of the two classifications of IBDN was higher than that of the experts. Our AI system is valuable enough to contribute to the next generation of clinical practice.