Virtual indigo carmine chromoendoscopy images: A novel modality for peroral cholangioscopy using artificial intelligence technology (with video).

BACKGROUND AND AIMS: Accurately diagnosing biliary strictures is crucial for surgical decisions, and although peroral cholangioscopy (POCS) aids in visual diagnosis, diagnosing malignancies or determining lesion margins via this route remains challenging. Indigo carmine is commonly used to evaluate lesions during gastrointestinal endoscopy. We aimed to establish the utility of virtual indigo carmine chromoendoscopy (VICI) converted from POCS images using artificial intelligence. METHODS: This single-center, retrospective study analyzed 40 patients with biliary strictures who underwent POCS using white light imaging (WLI) and narrow-band imaging (NBI). A "cycle-consistent adversarial network" (CycleGAN) was used to convert the WLI into VICI of POCS images. Three experienced endoscopists evaluated WLI, NBI, and VICI via POCS in all patients. The primary outcome was the visualization quality of surface structures, surface microvessels, and lesion margins. The secondary outcome was diagnostic accuracy. RESULTS: VICI showed superior visualization of the surface structures and lesion margins compared with WLI (P<0.001) and NBI (P<0.001). The diagnostic accuracies were 72.5%, 87.5%, and 90.0% in WLI alone, WLI and VICI simultaneously, and WLI and NBI simultaneously, respectively. WLI and VICI simultaneously tended to result in higher accuracy than WLI alone (P=0.083) and the results were not significantly different from WLI and NBI simultaneously (P=0.65). CONCLUSIONS: VICI in POCS proved valuable for visualizing surface structures and lesion margins and contributed to higher diagnostic accuracy comparable to NBI. In addition to NBI, VICI may be a novel supportive modality for POCS.

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.

Application of convolutional neural networks for evaluating the depth of invasion of early gastric cancer based on endoscopic images.

BACKGROUND AND AIM: Recently, artificial intelligence (AI) has been used in endoscopic examination and is expected to help in endoscopic diagnosis. We evaluated the feasibility of AI using convolutional neural network (CNN) systems for evaluating the depth of invasion of early gastric cancer (EGC), based on endoscopic images. METHODS: This study used a deep CNN model, ResNet152. From patients who underwent treatment for EGC at our hospital between January 2012 and December 2016, we selected 100 consecutive patients with mucosal (M) cancers and 100 consecutive patients with cancers invading the submucosa (SM cancers). A total of 3508 non-magnifying endoscopic images of EGCs, including white-light imaging, linked color imaging, blue laser imaging-bright, and indigo-carmine dye contrast imaging, were included in this study. A total of 2288 images from 132 patients served as the development dataset, and 1220 images from 68 patients served as the testing dataset. Invasion depth was evaluated for each image and lesion. The majority vote was applied to lesion-based evaluation. RESULTS: The sensitivity, specificity, and accuracy for diagnosing M cancer were 84.9% (95% confidence interval [CI] 82.3%-87.5%), 70.7% (95% CI 66.8%-74.6%), and 78.9% (95% CI 76.6%-81.2%), respectively, for image-based evaluation, and 85.3% (95% CI 73.4%-97.2%), 82.4% (95% CI 69.5%-95.2%), and 83.8% (95% CI 75.1%-92.6%), respectively, for lesion-based evaluation. CONCLUSIONS: The application of AI using CNN to evaluate the depth of invasion of EGCs based on endoscopic images is feasible, and it is worth investing more effort to put this new technology into practical use.