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Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy.
Sinonquel, Pieter; Eelbode, Tom; Bossuyt, Peter; Maes, Frederik; Bisschops, Raf.
Affiliation
  • Sinonquel P; Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium.
  • Eelbode T; Departments of, Department of, Translational Research in Gastrointestinal Diseases (TARGID), KU Leuven, Leuven, Belgium.
  • Bossuyt P; Medical Imaging Research Center (MIRC), University Hospitals Leuven, Leuven, Belgium.
  • Maes F; Department of Electrical Engineering (ESAT/PSI), KU Leuven, Leuven, Belgium.
  • Bisschops R; Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium.
Dig Endosc ; 33(2): 242-253, 2021 Jan.
Article in En | MEDLINE | ID: mdl-33145847
ABSTRACT
Artificial intelligence (AI) and its application in medicine has grown large interest. Within gastrointestinal (GI) endoscopy, the field of colonoscopy and polyp detection is the most investigated, however, upper GI follows the lead. Since endoscopy is performed by humans, it is inherently an imperfect procedure. Computer-aided diagnosis may improve its quality by helping prevent missing lesions and supporting optical diagnosis for those detected. An entire evolution in AI systems has been established in the last decades, resulting in optimization of the diagnostic performance with lower variability and matching or even outperformance of expert endoscopists. This shows a great potential for future quality improvement of endoscopy, given the outstanding diagnostic features of AI. With this narrative review, we highlight the potential benefit of AI to improve overall quality in daily endoscopy and describe the most recent developments for characterization and diagnosis as well as the recent conditions for regulatory approval.
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Full text: 1 Database: MEDLINE Main subject: Artificial Intelligence / Quality Improvement Type of study: Diagnostic_studies Limits: Humans Language: En Journal: Dig Endosc Journal subject: DIAGNOSTICO POR IMAGEM / GASTROENTEROLOGIA Year: 2021 Type: Article Affiliation country: Belgium

Full text: 1 Database: MEDLINE Main subject: Artificial Intelligence / Quality Improvement Type of study: Diagnostic_studies Limits: Humans Language: En Journal: Dig Endosc Journal subject: DIAGNOSTICO POR IMAGEM / GASTROENTEROLOGIA Year: 2021 Type: Article Affiliation country: Belgium