A patient swallows a capsule. It travels the entire GI tract capturing up to 50,000 frames. This pipeline extracts every frame, detects anomalies, draws contours, classifies pathologies across 17 categories, and auto-sorts results into labelled folders.
Each slider shows the exact transformation produced by the pipeline — raw PillCam frame on the left, AI-annotated output on the right.
A chylous cyst (lymphangiectasia) of the small bowel mucosa — a pale, well-defined ovoid lesion visible in the lower field. Multiple scattered angioectasias also visible in the background. The AI isolates the primary cyst with a yellow boundary, critical for differential diagnosis with lipoma or polyp.
A larger chylous cyst at close proximity. The AI detects and contours the lesion robustly despite partial occlusion by an intestinal fold and challenging proximity to the mucosa. Single-class yellow boundary output with high-confidence segmentation.
Two aphthoid erosions detected simultaneously. The AI applies a dual-class contour system: an orange outer boundary delineates the lesion zone, a blue inner mask segments the erosion core — replicating the annotation protocol used by the clinical team at Saint-Antoine.
A vascular malformation of the small intestinal mucosa. The AI classifies the frame then applies binary segmentation with a blue contour overlay, isolating the lesion from surrounding healthy mucosa with sub-pixel precision.
This tool is part of ongoing research at AP-HP Saint-Antoine. Open to academic and industrial partnerships.