{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T16:52:48Z","timestamp":1787503968079,"version":"build-2736575974"},"reference-count":48,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T00:00:00Z","timestamp":1692403200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["headachejournal.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Headache"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We developed an artificial intelligence (AI)\u2010based headache diagnosis model using a large questionnaire database from a headache\u2010specializing clinic.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Misdiagnosis of headache disorders is a serious issue and AI\u2010based headache diagnosis models are scarce.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed an AI\u2010based headache diagnosis model and conducted internal validation based on a retrospective investigation of 6058 patients (4240 training dataset for model development and 1818 test dataset for internal validation) diagnosed by a headache specialist. The ground truth was the diagnosis by the headache specialist. The diagnostic performance of the AI model was evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The dataset included 4829\/6058 (79.7%) patients with migraine, 834\/6058 (13.8%) with tension\u2010type headache, 78\/6058 (1.3%) with trigeminal autonomic cephalalgias, 38\/6058 (0.6%) with other primary headache disorders, and 279\/6058 (4.6%) with other headaches. The mean (standard deviation) age was 34.7\u2009(14.5)\u2009years, and 3986\/6058 (65.8%) were female. The model's micro\u2010average accuracy, sensitivity (recall), specificity, precision, and\n                      <jats:italic>F<\/jats:italic>\n                      \u2010values for the test dataset were 93.7%, 84.2%, 84.2%, 96.1%, and 84.2%, respectively. The diagnostic performance for migraine was high, with a sensitivity of 88.8% and\n                      <jats:italic>c<\/jats:italic>\n                      \u2010statistics of 0.89 (95% confidence interval 0.87\u20130.91).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our AI model demonstrated high diagnostic performance for migraine. If secondary headaches can be ruled out, the model can be a powerful tool for diagnosing migraine; however, further data collection and external validation are required to strengthen the performance, ensure the generalizability in other outpatients, and demonstrate its utility in real\u2010world settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/head.14611","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T17:57:23Z","timestamp":1692640643000},"page":"1097-1108","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Developing an artificial intelligence\u2013based diagnostic model of headaches from a dataset of clinic patients' records"],"prefix":"10.1111","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0192-5430","authenticated-orcid":false,"given":"Masahito","family":"Katsuki","sequence":"first","affiliation":[{"name":"Department of Neurosurgery 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