Artificial intelligence in colonoscopy: where have we been and where should we go?

© Translational Gastroenterology and Hepatology. All rights reserved. Transl Gastroenterol Hepatol 2023;8:23 | https://dx.doi.org/10.21037/tgh-23-25 The use of artificial intelligence (AI) in colonoscopy has gathered significant attention in recent years. Successful execution and publication of randomized trials have paved the way to Food and Drug Administration (FDA) approval of a handful of computer-vision based AI assistant tools in colonoscopy (1). However, it is yet to take a lead role as a helpful aid to the endoscopist on a day-today basis. Especially so in the private gastroenterology [gastrointestinal (GI)] practice setting where the majority of the population-based screening colonoscopies are performed (2). Although a good number of private practice settings in the US have tried some of the commercially available AI assistants in colonoscopy, most of them (to the best of our knowledge) have abandoned its ongoing use due to prolonged overall procedure time. A very important limitation in private GI practice. In fact, in a meta-analysis of six randomized controlled trials (RCTs) evaluating the use of real-time computer aided tools in colonoscopy, the withdrawal time was significantly greater in comparison to standard colonoscopy when AI was utilized (1). We should ensure that we all talk the same language when it comes to AI. AI encompasses every aspect of machine learning, where computer-based algorithms and softwares make tasks easier. A set of learning inputs are used to pre-train the system, and the system then generates outcome predictions on an unknown new data input. Computer vision is a branch of machine learning that is specific for identifying objects from an image or a video. Computer vision is the backbone of all ‘face-recognition’ technology, self-driving cars, and is also the concept that goes behind identifying polyps or pretty much anything of interest (like bleeding, or ulcers) in an image (whether it comes from a video capsule image, a colonoscopy image, or any endoscopy image). Convolutional neural networks (CNN) belong to deeplearning (a subset of machine learning) methods where the algorithms are connected by multiple arrays of ‘logisticregression’ connections or ‘nodes’. Only certain data detail at a specific numerical (to the decimal point in almost all cases) cut-off would get transmitted to the next level, so on and so forth to generate a final outcome, when all analyzed features of input data is broken up and evaluated through the CNN framework. With regards to colonoscopy and polyp detection, various terms have been used to describe the role of computer-aided systems, such as computer aided detection (CADe) and computer aided diagnosis (CADx). The difference in these terms is just the output parameter. Detection detects a polyp, whereas diagnosis characterizes the polyp (3). The machine learning algorithm is however agnostic to these terms. Randomized trials have, indeed, demonstrated better adenoma detection rates (ADRs) with the use of AI-tools in colonoscopy as compared to endoscopists alone. It is important to note that both cohorts met appropriate ADR benchmarks. However, the withdrawal time was significantly greater in the AI arm based on pooled data as mentioned above and it is an established fact that prolonged withdrawal times directly correlate with increased ADRs (1). Therefore, does AI really add any additional advantage by improving the ADR? Common sense would indicate that it should, as Editorial Commentary