Graphical Abstracts
Automated artificial intelligence scoring systems for the endoscopic assessment of ulcerative colitis: How far are we from clinical application?
Artificial intelligence (AI) is going to drastically change our approach to diagnostic endoscopy. In contrast to its human counterpart, AI can manage an exceptional amount of data simultaneously, does not get fatigued, and can be highly effective and efficient. In the past couple of years, we have witnessed a literal blossom of AI systems applied to digestive endoscopy. Industries have been leading this first part of AI application, with the launch of real-time automated polyp detection and characterization systems to screening colonoscopy.Computer-aided characterization of early cancer in Barrett’s esophagus on i-scan magnification imaging: a multicenter international study
We aimed to develop a computer-aided characterization system that could support the diagnosis of dysplasia in Barrett’s esophagus (BE) on magnification endoscopy.Development and validation of artificial neural networks model for detection of Barrett’s neoplasia: a multicenter pragmatic nonrandomized trial (with video)
The aim of this study was to develop and externally validate a computer-aided detection (CAD) system for the detection and localization of Barrett’s neoplasia and assess its performance compared with that of general endoscopists in a statistically powered multicenter study by using real-time video sequences.Novel “resect and analysis” approach for T2 colorectal cancer with use of artificial intelligence
Because of a lack of reliable preoperative prediction of lymph node involvement in early-stage T2 colorectal cancer (CRC), surgical resection is the current standard treatment. This leads to overtreatment because only 25% of T2 CRC patients turn out to have lymph node metastasis (LNM). We assessed a novel artificial intelligence (AI) system to predict LNM in T2 CRC to ascertain patients who can be safely treated with less-invasive endoscopic resection such as endoscopic full-thickness resection and do not need surgery.Correlation of the detection rate of upper GI cancer with artificial intelligence score: results from a multicenter trial (with video)
The quality of EGD is a prerequisite for a high detection rate of upper GI lesions, especially early gastric cancer. Our previous study showed that an artificial intelligence system, named intelligent detection endoscopic assistant (IDEA), could help to monitor blind spots and provide an operation score during EGD. Here, we verified the effectiveness of IDEA to help evaluate the quality of EGD in a large-scale multicenter trial.New concept for colonoscopy including side optics and artificial intelligence
Adenoma detection rate is the crucial parameter for colorectal cancer screening. Increasing the field of view with additional side optics has been reported to detect flat adenomas hidden behind folds. Furthermore, artificial intelligence (AI) has also recently been introduced to detect more adenomas. We therefore aimed to combine both technologies in a new prototypic colonoscopy concept.Artificial intelligence for the assessment of bowel preparation
A reliable assessment of bowel preparation is important to ensure high-quality colonoscopy. Current bowel preparation scoring systems are limited by interobserver variability. This study aimed to demonstrate objective assessment of bowel preparation adequacy using an artificial intelligence (AI)/convolutional neural network (CNN) algorithm developed from colonoscopy videos.Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study
The use of artificial intelligence (AI) during colonoscopy is attracting attention as an endoscopist-independent tool to predict histologic disease activity of ulcerative colitis (UC). However, no study has evaluated the real-time use of AI to directly predict clinical relapse of UC. Hence, it is unclear whether the real-time use of AI during colonoscopy helps clinicians make real-time decisions regarding treatment interventions for patients with UC. This study aimed to establish the role of real-time AI in stratifying the relapse risk of patients with UC in clinical remission.Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study
The diagnosis and characterization of biliary strictures (BSs) is challenging. The introduction of digital single-operator cholangioscopy (DSOC) that allows direct visual inspection of the lesion and targeted biopsy sampling significantly improved the diagnostic yield in patients with indeterminate BSs. However, the diagnostic efficiency of DSOC remains suboptimal. Convolutional neural networks (CNNs) have shown great potential for the interpretation of medical images. We aimed to develop a CNN-based system for automatic detection of malignant BSs in DSOC images.Detection of elusive polyps using a large-scale artificial intelligence system (with videos)
Colorectal cancer is a leading cause of death. Colonoscopy is the criterion standard for detection and removal of precancerous lesions and has been shown to reduce mortality. The polyp miss rate during colonoscopies is 22% to 28%. DEEP DEtection of Elusive Polyps (DEEP2) is a new polyp detection system based on deep learning that alerts the operator in real time to the presence and location of polyps. The primary outcome was the performance of DEEP2 on the detection of elusive polyps.Artificial intelligence−enhanced white-light colonoscopy with attention guidance predicts colorectal cancer invasion depth
Endoscopic submucosal dissection (ESD) and EMR are applied in treating superficial colorectal neoplasms but are contraindicated by deeply invasive colorectal cancer (CRC). The invasion depth of neoplasms can be examined by an automated artificial intelligence (AI) system to determine the applicability of ESD and EMR.
