Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy

Abstract

Colorectal cancer is common and can be devastating, with long-term survival rates vastly improved by early diagnosis. Colon capsule endoscopy (CCE) is increasingly recognised as a reliable option for colonic surveillance, but widespread adoption has been slow for several reasons, including the time-consuming reading process of the CCE recording. Automated image recognition and artificial intelligence (AI) are appealing solutions in CCE. Through a review of the currently available and developmental technologies, we discuss how AI is poised to deliver at the forefront of CCE in the coming years. Current practice for CCE reporting often involves a two-step approach, with a ‘pre-reader’ and ‘validator’. This requires skilled and experienced readers with a significant time commitment. Therefore, CCE is well-positioned to reap the benefits of the ongoing digital innovation. This is likely to initially involve an automated AI check of finished CCE evaluations as a quality control measure. Once felt reliable, AI could be used in conjunction with a ‘pre-reader’, before adopting more of this role by sending provisional results and abnormal frames to the validator. With time, AI would be able to evaluate the findings more thoroughly and reduce the input required from human readers and ultimately autogenerate a highly accurate report and recommendation of therapy, if required, for any pathology identified. As with many medical fields reliant on image recognition, AI will be a welcome aid in CCE. Initially, this will be as an adjunct to ‘double-check’ that nothing has been missed, but with time will hopefully lead to a faster, more convenient diagnostic service for the screening population.

Scope of Artificial Intelligence in Gastrointestinal Oncology

“…Although the diagnostic accuracy of AI systems has markedly increased, it still needs collaboration with physicians. Robertson et al proposed a five-step process that they foresee can help integrate AI with clinical practice, namely, Quality improvement, Productivity improvement, and Performance improvement, followed by Evaluation (step 4) where AI replaces human analysis and the results can be reviewed by the gastroenterologist before being reported to the final step 5, Diagnostic, where AI may replace the diagnostician for simple pathologic results releasing them to the patients without review [ 75 ]. However, before we can follow these steps, there is a need to identify one or two methodologies from numerous choices that can be developed, generalized, and commercialized.…”

“…All studies had a higher polyp detection rate in the AI group than the standard colonoscopy alone group [ 74 ]. Recent studies have also shown considerable promise in the use of AI, especially CNN-based systems in colon capsule endoscopy, to improve rates of colon polyp detection [ 75 , 76 ]. Laiz et al developed a CNN model to detect polyps of all sizes and morphologies using capsule endoscopy images and reported specificity of over 90% for small to large size polyps as well as pedunculated or sessile polyps [ 76 ].…”

The optimal use of colon capsule endoscopes in clinical practice

“… 27 However, few studies have reported the yield of AI in CCE. 28 Ribeiro et al. developed an AI algorithm for detecting blood in CCE images.…”

“…27 However, few studies have reported the yield of AI in CCE. 28 Ribeiro et al developed an AI algorithm for detecting blood in CCE images. 29 They retrospectively extracted greater than 5,000 CCE pictures used to train and validate the CNN, achieving a sensitivity of 99.8% and specificity of 93.2% for the detection of blood.…”

Real-life practice data on colon capsule endoscopy: We need them fast!

“…CCE studies are time-consuming to read and interpret, and human errors are bound to happen 9 10 . The application so artificial intelligence (AI) to CCE with deep learning convolutional neural network algorithms 11 could lead to automated polyp identification and/or characterisztion with improved sensitivity and reduced time demands by highlighting images with abnormalities for physician review. However, AI will only detect lesions that are already visible; therefore, future research should focus on improving bowel preparation to improve the cleanliness and completeness rate for CCE to the recommended minimum level of 90 % for optical colonoscopy 12 .…”