Feedback from artificial intelligence improved the learning of junior endoscopists on histology prediction of gastric lesions.

Background and study aims Artificial intelligence (AI)-assisted image classification has been shown to have high accuracy on endoscopic diagnosis. We evaluated the potential effects of use of an AI-assisted image classifier on training of junior endoscopists for histological prediction of gastric lesions. Methods An AI image classifier was built on a convolutional neural network with five convolutional layers and three fully connected layers A Resnet backbone was trained by 2,000 non-magnified endoscopic gastric images. The independent validation set consisted of another 1,000 endoscopic images from 100 gastric lesions. The first part of the validation set was reviewed by six junior endoscopists and the prediction of AI was then disclosed to three of them (Group A) while the remaining three (Group B) were not provided this information. All endoscopists reviewed the second part of the validation set independently. Results The overall accuracy of AI was 91.0 % (95 % CI: 89.2-92.7 %) with 97.1 % sensitivity (95 % CI: 95.6-98.7%), 85.9 % specificity (95 % CI: 83.0-88.4 %) and 0.91 area under the ROC (AUROC) (95 % CI: 0.89-0.93). AI was superior to all junior endoscopists in accuracy and AUROC in both validation sets. The performance of Group A endoscopists but not Group B endoscopists improved on the second validation set (accuracy 69.3 % to 74.7 %; P  = 0.003). Conclusion The trained AI image classifier can accurately predict presence of neoplastic component of gastric lesions. Feedback from the AI image classifier can also hasten the learning curve of junior endoscopists in predicting histology of gastric lesions.

Linked color imaging versus narrow-band imaging for colorectal polyp detection: a prospective randomized tandem colonoscopy study.

BACKGROUND AND AIMS: Linked color imaging (LCI) is a newly available image-enhanced endoscopy (IEE) system that emphasizes the red mucosal color. No study has yet compared LCI with other available IEE systems. Our aim was to investigate polyp detection rates using LCI compared with narrow-band imaging (NBI). METHODS: This is a prospective randomized tandem colonoscopy study. Eligible patients who underwent colonoscopy for symptoms or screening/surveillance were randomized in a 1:1 ratio to receive tandem colonoscopy with both colonoscope withdrawals using LCI or NBI. The primary outcome was the polyp detection rate. RESULTS: Two hundred seventy-two patients were randomized (mean age, 62 years; 48.2% male; colonoscopy for symptoms, 72.8%) with 136 in each arm. During the first colonoscopy, the polyp detection rate (71.3% vs 55.9%; P = .008), serrated lesion detection rate (34.6% vs 22.1%; P = .02), and mean number of polyps detected (2.04 vs 1.35; P = .02) were significantly higher in the NBI group than in the LCI group. There was also a trend of higher adenoma detection rate in the NBI group compared with the LCI group (51.5% vs 39.7%, respectively; P = .05). Multivariable analysis confirmed that use of NBI (adjusted odds ratio, 1.99; 95% confidence interval, 1.09-3.68) and withdrawal time >8 minutes (adjusted odds ratio, 5.11; 95% confidence interval, 2.79-9.67) were associated with polyp detection. Overall, 20.5% of polyps and 18.1% of adenomas were missed by the first colonoscopy, but there was no significant difference in the miss rates between the 2 groups. CONCLUSION: NBI was significantly better than LCI for colorectal polyp detection. However, both LCI and NBI missed 20.5% of polyps. (Clinical trial registration number: NCT03336359.).