Table.

Future Directions for ML in Computer Vision Endoscopy

Problem	ML solution
Classification of esophageal varices and risk stratification	Agreement among endoscopists varies widely regarding size of esophageal varices. Automated stratification and risk classification could significantly impact practice and provide endoscopists with a tool to more accurately define varices.62
Differentiation of ulcerative colitis vs Crohn’s disease	Distinction between ulcerative colitis and Crohn’s disease can be challenging endoscopically as well as histologically.63,64 Numerous scoring tools have been created to assist endoscopists in diagnosis; however, this problem lends itself well to ML assistance in the future.
Assessment of biliary strictures during ERCP	The differentiation between benign and malignant biliary strictures during ERCP is challenging even for the most advanced endoscopists, and it is possible that ML could help identify features associated with benign and malignant disease that could assist the endoscopist.65
Predicting bile duct cannulation difficulty	One of the most challenging aspects of ERCP is determining the likelihood of success in achieving bile duct cannulation, and thus presents another opportunity for ML assistance to transform practice.65
Quality assessment of mucosal inspection	ML could not only assist with training of endoscopists but also provide real-time feedback regarding the percent of mucosa examined during EGD or colonoscopy to ensure adequate examination is taking place.66
Standardized training in endoscopy	ML-developed systems can likely help standardize the training and assessment of fellows in the development of endoscopy skills.10

EGD, esophagogastroduodenoscopy; ERCP, endoscopic retrogradecholangiopancreatography.