{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T17:18:28Z","timestamp":1790443108904,"version":"4.1.0"},"reference-count":37,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T00:00:00Z","timestamp":1723161600000},"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":["J. pediatr. gastroenterol. nutr."],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objectives<\/jats:title><jats:p>Classical Hirschsprung disease (HD) is defined by the absence of ganglion cells in the rectosigmoid colon. The diagnosis is made from rectal biopsy, which reveals the aganglionosis and the presence of cholinergic hyperinnervation. However, depending on the method of rectal biopsy, the quality of the specimens and the related diagnostic accuracy varies substantially. To facilitate and objectify the diagnosis of HD, we investigated whether software\u2010based identification of cholinergic hyperinnervation in digitalized histopathology slides is suitable for distinguishing healthy individuals from affected individuals.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p><jats:italic>N<\/jats:italic>\u2009=\u2009190 samples of 112 patients who underwent open surgical rectal biopsy at our pediatric surgery center between 2009 and 2019 were included in this study. Acetylcholinesterase (AChE) stained slides of these samples were collected and digitalized via slide scanning and analyzed using two digital imaging software programs (HALO, QuPath). The AChE\u2010positive staining area in the mucosal layers of the intestinal wall was determined. In the next step machine learning was employed to identify patterns of cholinergic hyperinnervation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The area of AChE\u2010positive staining was greater in HD patients compared to healthy individuals (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001). Artificial intelligence\u2010based assessment of parasympathetic hyperinnervation identified HD with a high precision (area under the curve [AUC] 0.96). The accuracy of the prediction model increased when nonrectal samples were excluded (AUC 0.993).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Software\u2010assisted machine\u2010learning analysis of AChE staining is suitable to improve the diagnostic accuracy of HD.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jpn3.12339","type":"journal-article","created":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T08:16:13Z","timestamp":1723191373000},"page":"729-737","source":"Crossref","is-referenced-by-count":13,"title":["Diagnosis of Hirschsprung disease by analyzing acetylcholinesterase staining using artificial intelligence"],"prefix":"10.1002","volume":"79","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2939-4512","authenticated-orcid":false,"given":"Yannick","family":"Braun","sequence":"first","affiliation":[{"name":"Department of Pediatric Surgery and Pediatric Urology University Hospital\u00a0Frankfurt, Goethe\u2010University Frankfurt am Main 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