Artificial intelligence-assisted versus conventional reading in pan-intestinal capsule endoscopy for suspected mid-lower gastrointestinal bleeding: a retrospective analysis of a prospective cohort.

OBJECTIVE

Pan-intestinal capsule endoscopy (PCE) offers a safer, more effective alternative to colonoscopy for detecting potentially haemorrhagic lesions (PHL) in suspected mid-lower gastrointestinal bleeding (MLGIB), though it is limited by time-consuming review and missed lesions. We compared the diagnostic performance of artificial intelligence-assisted PCE (AI-PCE) versus conventional reading PCE (CR-PCE) and colonoscopy.

METHODS

We retrospectively analysed 100 prospectively enrolled patients undergoing PCE for suspected MLGIB using an externally validated convolutional neural network. Diagnostic performance of AI-PCE, CR-PCE and colonoscopy was evaluated against a consensus reference standard. Accuracy metrics (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV)) were assessed overall and by lesion type and intestinal segment.

RESULTS

AI-PCE detected PHL in 60% of patients versus 42% with CR-PCE (p<0.01). Lesions included vascular (51% vs 33%, p<0.01), ulcers/erosions (16% vs 7%, p=0.012), protuberant (5% vs 4%, p=1.0) and active bleeding (7% vs 7%, p=1.0). AI-PCE achieved higher sensitivity than CR-PCE (95% vs 67%, p<0.0001) with comparable specificity (97% vs 97%), PPV (98% vs 98%) and superior NPV (92% vs 63%, p=0.0015). For the small bowel, AI-PCE outperformed CR-PCE in sensitivity (96% vs 59%, p<0.0001) and NPV (97% vs 76%, p=0.0010). In colon, AI-PCE also showed greater sensitivity (90% vs 68%, p=0.027) and NPV (94% vs 86%, p = 0.066). Compared with colonoscopy, AI-PCE was markedly more sensitive (90% vs 32%, p<0.0001) with higher PPV (100% vs 65%, p<0.001) and NPV (94% vs 65%, p<0.0001).

CONCLUSION

AI-PCE significantly improves diagnostic accuracy over conventional reading and colonoscopy, offering superior sensitivity without compromising specificity, and may establish a new standard for PCE in MLGIB.