Considerable advances in artificial intelligence (AI) and machine-learning (ML) methodologies have led to the emergence of promising tools in the field of gastrointestinal endoscopy. Computer vision is an application of AI/ML that has been successfully applied for the computer-aided detection (CADe) and computer-aided diagnosis (CADx) of colon polyps and numerous other conditions encountered during GI endoscopy. Outside of computer vision, a wide variety of other AI applications have been applied to gastroenterology, ranging from natural language processing (NLP) to optimize clinical documentation and endoscopy quality reporting to ML techniques that predict disease severity/treatment response and augment clinical decision-making.
Dr. Eugenia N. Uche-Anya
In the United States, colonoscopy is the standard for colon cancer screening and prevention; however, precancerous polyps can be missed for various reasons, ranging from subtle surface appearance of the polyp or location behind a colonic fold to operator-dependent reasons such as inadequate mucosal inspection. Though clinical practice guidelines have set adenoma detection rate (ADR) thresholds at 20% for women and 30% for men, studies have shown a 4- to 10-fold variation in ADR among physicians in clinical practice settings,1 with an estimated adenoma miss rate (AMR) of 25% and a false-negative colonoscopy rate of 12%.2 Variability in adenoma detection affects the risk of interval colorectal cancer post colonoscopy.3,4
AI provides an opportunity for mitigating this risk. Advances in deep learning and computer vision have led to the development of CADe systems that automatically detect polyps in real time during colonoscopy, resulting in reduced adenoma miss rates (Table 1). In addition to polyp detection, deep-learning technologies are also being used in CADx systems for polyp diagnosis and characterization of malignancy risk. This could aid therapeutic decision-making: Unnecessary resection or histopathologic analysis could be obviated for benign hyperplastic polyps. On the other end of the polyp spectrum, an AI tool that could predict the presence or absence of submucosal invasion could be a powerful tool when evaluating early colon cancers for consideration of endoscopic submucosal dissection vs. surgery. Examples of CADe polyp detection and CADx polyp characterization are shown in Figure 1.
Figure 1. CADe colonic polyp detection is shown at left (source: Wision AI), and CADx real-time characterization of a colonic polyp is shown at right (source: Y. Mori and M. Misawa).
Other potential computer vision applications that may improve colonoscopy quality include tools that help measure adequacy of mucosal exposure, segmental inspection time, and a variety of other parameters associated with polyp detection performance. These are promising areas for future research. Beyond improving colonoscopy technique, natural language processing tools already are being used to optimize clinical documentation as well as extract information from colonoscopy and pathology reports that can facilitate reporting of colonoscopy quality metrics such as ADR, cecal intubation rate, withdrawal time, and bowel preparation adequacy. AI-powered analytics may help unlock large-scale reporting of colonoscopy quality metrics on a health-systems level5 or population-level,6 helping to ensure optimal performance and identifying avenues for colonoscopy quality improvement.
