BMJ open gastroenterology · 2025
Artificial intelligence-assisted versus conventional reading in pan-intestinal capsule endoscopy for suspected mid-lower gastrointestinal bleeding: a retrospective analysis of a prospective cohort.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Rosa Bruno, Mascarenhas Saraiva Miguel José, Afonso João, Cúrdia Gonçalves Tiago, Mendes Francisco, Moreira Maria João, Martins Miguel, Dias de Castro Francisca, Ribeiro Tiago, Cardoso Pedro, Almeida Maria João, Mota Joana, Ferreira João, Macedo Guilherme, Cotter José
Аннотация
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.
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.