Artificial intelligence in luminal endoscopy.
Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imagin...
| Publicado en: | Therapeutic Advances in Gastrointestinal Endoscopy pp. 1 - 16 |
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| Autores principales: | , , , , , , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
6/23/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144240906&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144240906 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26317745 M8AA jtl: Therapeutic Advances in Gastrointestinal Endoscopy issn: 26317745 maglogo: Y pubinfo: dt: 6/23/2020 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 144240906 144240906 144240906 10.1177/2631774520935220 144240906 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence in luminal endoscopy. aug: au: Gulati, Shraddha Emmanuel, Andrew Patel, Mehul Williams, Sophie Haji, Amyn Hayee, Bu'Hussain Neumann, Helmut affil: King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK sug: subj: Endoscopy, Gastrointestinal Methods Artificial Intelligence Machine Learning Deep Learning Algorithms Intestine, Small Surgery Neural Networks (Computer) Gastrointestinal Neoplasms Diagnosis Neoplasms, Squamous Cell Diagnosis Stomach Neoplasms Diagnosis Microscopy Methods Colitis Diagnosis Polyps Diagnosis Referral and Consultation Diagnostic Imaging ab: Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett's, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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