Artificial intelligence and deep learning for small bowel capsule endoscopy

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.

Automated summary

Capsule endoscopy relies on artificial intelligence, particularly machine learning algorithms, which currently require human supervision. However, with the advancement of deep learning, artificial intelligence is becoming more reliable and will be increasingly relied upon in the future. Despite recent advancements in artificial intelligence not yet being fully incorporated into practice, regulatory and standardization hurdles are being overcome, leading to a rapid proliferation of AI-based clinical applications.