{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T11:53:57Z","timestamp":1791978837576,"version":"4.4.0"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T00:00:00Z","timestamp":1672012800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["UEG Journal"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Lynch syndrome (LS), an autosomal dominant disorder caused by pathogenic germline variants in DNA mismatch repair (MMR) genes, represents the most common hereditary colorectal cancer (CRC) syndrome. Lynch syndrome patients are at high risk of CRC despite regular endoscopic surveillance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Our aim was to investigate the diagnostic performance of artificial intelligence (AI)\u2010assisted colonoscopy in comparison to High\u2010Definition white\u2010light endoscopy (HD\u2010WLE) for the first time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Patients \u226518\u00a0years with LS, with a pathogenic germline variant (\n                      <jats:italic>MLH1<\/jats:italic>\n                      ,\n                      <jats:italic>MHS2<\/jats:italic>\n                      ,\n                      <jats:italic>MSH6<\/jats:italic>\n                      ), and at least one previous colonoscopy (interval 10\u201336\u00a0months) were eligible. Patients were stratified by previous CRC and affected MMR gene with a 1:1 allocation ratio (AI\u2010assisted vs. HD white\u2010light endoscopy) in this exploratory pilot trial.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Between Dec\u20102021 and Dec\u20102022, 101 LS patients were randomised and 96 patients were finally analyzed after exclusion of 5 patients due to insufficient bowel preparation. In the HD\u2010WLE arm, adenomas were detected in 12\/46 patients compared to 18\/50 in the AI arm (26.1% [95% CI 14.3\u201341.1] vs. 36.0% [22.9\u201350.8];\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.379). The use of AI\u2010assisted colonoscopy especially increased detection of flat adenomas (Paris classification 0\u2010IIb) (examinations with detected flat adenomas: 3\/46 [6.5%] vs. 10\/50 [20%];\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.07; numbers of detected flat adenomas: 4\/20 vs. 17\/30,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.018). The median withdrawal time did not differ significantly between HD\u2010WLE and AI (14 vs. 15\u00a0min;\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.170).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We here present first data suggesting that real\u2010time AI\u2010assisted colonoscopy is a promising approach to optimize endoscopic surveillance in LS patients, in particular to improve the detection of flat adenomas.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ueg2.12354","type":"journal-article","created":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T06:40:31Z","timestamp":1672036831000},"page":"60-68","source":"Crossref","is-referenced-by-count":49,"title":["Real\u2010time use of artificial intelligence (CADEYE) in colorectal cancer surveillance of patients with Lynch syndrome\u2014A 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