Artificial intelligence and deep learning for small bowel capsule endoscopy. Issue 2 (27th December 2020)

• Record Type: Journal Article
• Authors: Trasolini, Roberto; Byrne, Michael F.
• Is Part Of: Digestive endoscopy. Volume 33:Issue 2(2021)
• Page Start: 290
• Page End: 297
• Publication Date: 2020-12-27
• Subjects: artificial intelligence -- capsule endoscopy -- convolutional neural network -- deep learning -- machine learning; Digestive organs -- Diseases -- Periodicals; Digestive organs -- Diseases -- Diagnosis -- Periodicals; Endoscopy -- Periodicals; Digestive System Diseases -- diagnosis -- Periodicals; Digestive System Diseases -- therapy -- Periodicals; Endoscopy -- Periodicals; 616.3
• DOI: 10.1111/den.13896
• Languages: English
• ISSNs: 0915-5635

Abstract

Abstract : Capsule endoscopy is ideally suited to artificial intelligence‐based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software necessitates close human supervision given poor sensitivity relative to an expert reader. However, with the advent of deep learning, artificial intelligence is becoming increasingly reliable and will be increasingly relied upon. We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence‐based clinical applications are likely to proliferate rapidly.