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

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 primar...

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Elmentve itt :
Bibliográfiai részletek
Szerzők: Spada Cristiano
Piccirelli Stefania
Hassan Cesare
Ferrari Clarissa
Toth Ervin
Finta Ádám
Rosztóczy András
Baltes Peter
Sidhu Reena
Szalai Milan
Helle Krisztina
Nemeth Artur
Nowak Tanja
Lin Rong
Costamagna Guido
et al
Dokumentumtípus: Cikk
Megjelent: 2024
Sorozat:LANCET DIGITAL HEALTH 6 No. 5
Tárgyszavak:
doi:10.1016/S2589-7500(24)00048-7

mtmt:34824739
Online Access:http://publicatio.bibl.u-szeged.hu/30399
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245 1 0 |a AI-assisted capsule endoscopy reading in suspected small bowel bleeding   |h [elektronikus dokumentum] :  |b a multicentre prospective study /  |c  Spada Cristiano 
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520 3 |a 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. 
650 4 |a Klinikai orvostan 
700 0 1 |a Piccirelli Stefania  |e aut 
700 0 1 |a Hassan Cesare  |e aut 
700 0 1 |a Ferrari Clarissa  |e aut 
700 0 1 |a Toth Ervin  |e aut 
700 0 1 |a Finta Ádám  |e aut 
700 0 1 |a Rosztóczy András  |e aut 
700 0 1 |a Baltes Peter  |e aut 
700 0 1 |a Sidhu Reena  |e aut 
700 0 1 |a Szalai Milan  |e aut 
700 0 1 |a Helle Krisztina  |e aut 
700 0 1 |a Nemeth Artur  |e aut 
700 0 1 |a Nowak Tanja  |e aut 
700 0 1 |a Lin Rong  |e aut 
700 0 1 |a Costamagna Guido  |e aut 
700 0 1 |a et al.  |e aut 
856 4 0 |u http://publicatio.bibl.u-szeged.hu/30399/1/Spada.pdf  |z Dokumentum-elérés