{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T17:29:09Z","timestamp":1776706149235,"version":"3.51.2"},"reference-count":19,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T00:00:00Z","timestamp":1749686400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Digestive Endoscopy"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>Achalasia is an esophageal motility disorder that impairs quality of life and is often missed (20\u201350%) on endoscopy. A newly developed computer\u2010aided detection (CAD) software has shown high accuracy for achalasia diagnosis in preclinical settings. However, its benefit in a clinical setting remains unclear.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Between February and August 2023, 83 endoscopists from 27 centers assessed 50 randomized endoscopic videos (25 achalasia, 25 nonachalasia) without and with CAD. Endoscopists assessed videos without CAD, then with CAD after 2 months. The primary end\u2010point was improvement in sensitivity for nonexperienced endoscopists (no endoscopic experience of achalasia). Sensitivity, specificity, and accuracy with and without CAD were compared using the McNemar test.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Sensitivity for diagnosing achalasia increased significantly with CAD, rising from 74.2% (95% confidence interval [CI] 72.2\u201376.0%) to 91.2% (95% CI 89.9\u201392.4%) for all readers, showing a difference of 17.1% (95% CI 15.1\u201319.0%). Specifically, sensitivity improved from 66.9% (95% CI 63.6\u201370.0%) to 91.9% (95% CI 89.9\u201393.6%) among nonexperienced endoscopists, resulting in a difference of 25.0% (95% CI 21.7\u201328.4%), and from 79.5% (95% CI 77.1\u201381.8%) to 90.8% (95% CI 89.0\u201392.3%) among experienced endoscopists (endoscopic experience of at least one achalasia case), with a difference of 11.3% (95% CI 8.9\u201313.6%). Accuracy and specificity improved significantly with CAD assistance, regardless of reader's experience.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>CAD improves achalasia detection by 17%, confirming preclinical results. The benefit was higher for nonexperienced endoscopists. CAD assistance may lead to prompt and effective treatment, minimizing the risk of false\u2010negative diagnosis in clinical practice.<\/jats:p><\/jats:sec><jats:sec><jats:title>Trial registration<\/jats:title><jats:p>This study was registered in the University Hospital Medical Information Network Clinical Trial\u00a0Registry (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/www.umin.ac.jp\/ctr\/\">https:\/\/www.umin.ac.jp\/ctr\/<\/jats:ext-link>) number: UMIN000053047.<\/jats:p><\/jats:sec>","DOI":"10.1111\/den.15028","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T22:55:37Z","timestamp":1749768937000},"page":"851-861","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Computer\u2010aided detection for esophageal achalasia (with\u00a0video)"],"prefix":"10.1111","volume":"37","author":[{"given":"Hironari","family":"Shiwaku","sequence":"first","affiliation":[{"name":"Fukuoka University  Fukuoka Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8520-2036","authenticated-orcid":false,"given":"Masashi","family":"Misawa","sequence":"additional","affiliation":[{"name":"Showa Medical University Northern Yokohama Hospital  Kanagawa Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haruhiro","family":"Inoue","sequence":"additional","affiliation":[{"name":"Showa Medical University Koto Toyosu Hospital  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Jiang","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics Nagoya University  Aichi Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiro","family":"Oda","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics Nagoya University  Aichi Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pietro","family":"Familiari","sequence":"additional","affiliation":[{"name":"Fondazione Policlinico Universitario A. 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