Intended Use

AI-assisted reading tool to aid small bowel capsule endoscopy reviewers in decreasing review time and identifying digestive tract location in adults with suspected small bowel bleeding; not intended to replace clinical decision making.

Technology

The device uses convolutional neural networks with deep learning models for two main algorithms: digestive tract site recognition (oral cavity, esophagus, stomach, small bowel) and small bowel lesion detection. It processes capsule endoscopy images and overlays graphical markers on the original video to identify potential lesions and anatomical locations, aiding clinicians in review without altering the source video.

Performance

Performance testing included training and validation on a large patient dataset from capsule endoscopy. Lesion detection showed high patient-level sensitivity (98%) but moderate specificity (37%); image-level sensitivity and specificity were higher (95.05% and 97.54%). Tract site recognition sensitivity and specificity were above 98% across all anatomical locations. A multicenter clinical study showed AI-assisted reading reduced mean reading time significantly while maintaining non-inferior diagnostic yield compared to standard reading.

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