AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study

胶囊内镜 医学 不明原因胃肠道出血 内窥镜检查 前瞻性队列研究 阅读(过程) 胃肠病学 普通外科 内科学 哲学 语言学
作者
Cristiano Spada,Stefania Piccirelli,Cesare Hassan,Clarissa Ferrari,Ervin Tóth,Begoña González-Suárez,Martin Keuchel,ME McAlindon,Ádám Finta,A Rosztóczy,Xavier Dray,Daniele Salvi,Maria Elena Riccioni,Robert Bénamouzig,Amit Chattree,Adam Humphries,Jean–Christophe Saurin,Edward J. Despott,Alberto Murino,Gabriele Wurm Johansson,Antonio Giordano,Peter Baltes,Reena Sidhu,M Szalai,Krisztina Helle,Artúr Németh,Tanja Nowak,Rong Lin,Guido Costamagna
出处
期刊:The Lancet Digital Health [Elsevier]
卷期号:6 (5): e345-e353
标识
DOI:10.1016/s2589-7500(24)00048-7
摘要

Capsule endoscopy reading is time consuming, and readers are required to maintain attention so as not to miss significant findings. Deep convolutional neural networks can recognise relevant findings, possibly exceeding human performances and reducing the reading time of capsule endoscopy. Our primary aim was to assess the non-inferiority of artificial intelligence (AI)-assisted reading versus standard reading for potentially small bowel bleeding lesions (high P2, moderate P1; Saurin classification) at per-patient analysis. The mean reading time in both reading modalities was evaluated among the secondary endpoints.Patients aged 18 years or older with suspected small bowel bleeding (with anaemia with or without melena or haematochezia, and negative bidirectional endoscopy) were prospectively enrolled at 14 European centres. Patients underwent small bowel capsule endoscopy with the Navicam SB system (Ankon, China), which is provided with a deep neural network-based AI system (ProScan) for automatic detection of lesions. Initial reading was performed in standard reading mode. Second blinded reading was performed with AI assistance (the AI operated a first-automated reading, and only AI-selected images were assessed by human readers). The primary endpoint was to assess the non-inferiority of AI-assisted reading versus standard reading in the detection (diagnostic yield) of potentially small bowel bleeding P1 and P2 lesions in a per-patient analysis. This study is registered with ClinicalTrials.gov, NCT04821349.From Feb 17, 2021 to Dec 29, 2021, 137 patients were prospectively enrolled. 133 patients were included in the final analysis (73 [55%] female, mean age 66·5 years [SD 14·4]; 112 [84%] completed capsule endoscopy). At per-patient analysis, the diagnostic yield of P1 and P2 lesions in AI-assisted reading (98 [73·7%] of 133 lesions) was non-inferior (p<0·0001) and superior (p=0·0213) to standard reading (82 [62·4%] of 133; 95% CI 3·6-19·0). Mean small bowel reading time was 33·7 min (SD 22·9) in standard reading and 3·8 min (3·3) in AI-assisted reading (p<0·0001).AI-assisted reading might provide more accurate and faster detection of clinically relevant small bowel bleeding lesions than standard reading.ANKON Technologies, China and AnX Robotica, USA provided the NaviCam SB system.
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