Artificial Intelligence Approaches for Gastrointestinal Bleeding Detection in Capsule Endoscopy: A Systematic Review and Critical Analysis

Background/Objectives: Gastrointestinal (GI) bleeding is a common, potentially life-threatening condition, and capsule endoscopy (CE) is the principal investigation for suspected small-bowel bleeding after non-diagnostic upper endoscopy and colonoscopy. Because manual review of thousands of CE frames is labor-intensive, artificial intelligence (AI) has been proposed to automate bleeding detection.

Methods

Following PRISMA 2020, this systematic review synthesizes 15 peer-reviewed journal studies (2020–2025) applying traditional machine learning, convolutional neural networks, transfer learning, attention-based or hybrid architectures, segmentation, or object detection to GI bleeding detection in CE. Methodological quality was appraised with an AI-adapted risk-of-bias framework informed by QUADAS-2 and PROBAST.