SwePub
Sök i LIBRIS databas

  Extended search

onr:"swepub:oai:lup.lub.lu.se:3cde1f60-d798-41dd-bc1a-158b83416935"
 

Search: onr:"swepub:oai:lup.lub.lu.se:3cde1f60-d798-41dd-bc1a-158b83416935" > AI-assisted capsule...

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist
  • Spada, CristianoPoliclinico Universitario Agostino Gemelli (author)

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

  • Article/chapterEnglish2024

Publisher, publication year, extent ...

  • 2024

Numbers

  • LIBRIS-ID:oai:lup.lub.lu.se:3cde1f60-d798-41dd-bc1a-158b83416935
  • https://lup.lub.lu.se/record/3cde1f60-d798-41dd-bc1a-158b83416935URI
  • https://doi.org/10.1016/S2589-7500(24)00048-7DOI

Supplementary language notes

  • Language:English
  • Summary in:English

Part of subdatabase

Classification

  • Subject category:art swepub-publicationtype
  • Subject category:ref swepub-contenttype

Notes

  • Background: 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. Methods: 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. Findings: 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). Interpretation: AI-assisted reading might provide more accurate and faster detection of clinically relevant small bowel bleeding lesions than standard reading. Funding: ANKON Technologies, China and AnX Robotica, USA provided the NaviCam SB system.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Piccirelli, StefaniaPoliclinico Universitario Agostino Gemelli (author)
  • Hassan, CesareHumanitas Research Hospital (author)
  • Ferrari, Clarissa (author)
  • Toth, ErvinLund University,Lunds universitet,Kirurgi,Forskargrupper vid Lunds universitet,Surgery,Lund University Research Groups,Skåne University Hospital(Swepub:lu)ront-eto (author)
  • González-Suárez, BegoñaHospital Clínic of Barcelona (author)
  • Keuchel, MartinUniversity of Hamburg (author)
  • McAlindon, MarcUniversity of Sheffield,Sheffield Teaching Hospitals (author)
  • Finta, Ádám (author)
  • Rosztóczy, AndrásUniversity of Szeged (author)
  • Dray, XavierHospital Saint-Antoine (author)
  • Salvi, DanielePoliclinico Universitario Agostino Gemelli (author)
  • Riccioni, Maria ElenaPoliclinico Universitario Agostino Gemelli (author)
  • Benamouzig, RobertHôpital Avicenne (author)
  • Chattree, Amit (author)
  • Humphries, Adam (author)
  • Saurin, Jean ChristopheLyon Civil Hospital / Hospices Civils de Lyon (author)
  • Despott, Edward J.Royal Free Hospital (author)
  • Murino, AlbertoRoyal Free Hospital (author)
  • Johansson, Gabriele WurmLund University,Lunds universitet,Gastroenterologi,Forskargrupper vid Lunds universitet,Gastroenterology,Lund University Research Groups,Skåne University Hospital(Swepub:lu)med-giw (author)
  • Giordano, AntonioHospital Clínic of Barcelona (author)
  • Baltes, PeterUniversity of Hamburg (author)
  • Sidhu, ReenaSheffield Teaching Hospitals,University of Sheffield (author)
  • Szalai, Milan (author)
  • Helle, KrisztinaUniversity of Szeged (author)
  • Nemeth, ArturLund University,Lunds universitet,Kirurgi,Forskargrupper vid Lunds universitet,Surgery,Lund University Research Groups,Skåne University Hospital(Swepub:lu)med-aun (author)
  • Nowak, Tanja (author)
  • Lin, RongTongji Medical University (author)
  • Costamagna, GuidoPoliclinico Universitario Agostino Gemelli (author)
  • Policlinico Universitario Agostino GemelliHumanitas Research Hospital (creator_code:org_t)

Related titles

  • In:The Lancet Digital Health6:5, s. 345-3532589-7500

Internet link

Find in a library

To the university's database

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view