{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T09:21:56Z","timestamp":1787649716016,"version":"build-2736575974"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":25,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Traditional manual Clock Drawing Tests (CDTs) are widely used for cognitive screening but are constrained by human subjectivity and alphanumeric literacy or cultural barriers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study validated an open-source artificial intelligence (AI)-based drawing platform utilizing universal, pre-linguistic Lea symbols against manual CDT metrics for cognitive screening and differentiating Alzheimer's disease (AD) from healthy controls (HCs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this cross-sectional validation study, 111 participants aged \u226565 years, 55 clinically diagnosed AD patients and 56 HCs, were evaluated. Participants completed manual CDTs (6-point and 10-point scoring) and the AI-based drawing task on a tablet. Diagnostic accuracy was evaluated using receiver operating characteristic curve analysis, Cohen's kappa (\u03ba), and multivariable logistic regression adjusted for age and gender.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The AI model demonstrated clear diagnostic accuracy (area under the curve = 0.868), with overall accuracy comparable to the 10-point manual scale. An optimal diagnostic cut-off of &amp;lt;27.00 points, identified in this study, achieved 83.6% sensitivity and 75.0% specificity, demonstrating substantial agreement with clinical diagnosis (\u03ba = 0.586, P &amp;lt; 0.001). Crucially, the AI-based drawing platform flagged 14 HCs (25.0%) as at-risk who were classified as intact by conventional manual CDT scales. Multivariable regression confirmed the AI score as a powerful independent predictor of AD status after adjusting for demographics (odds ratio: 0.940, 95% confidence interval: 0.904\u20130.979, P &amp;lt; 0.001).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The AI-based drawing platform provides an objective, automated alternative to traditional CDTs. Its sensitivity to subtle visuoconstructional variations positions it as an effective, low-cost triage tool to optimize early neurological referrals in primary care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/fampra\/cmag066","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T11:17:19Z","timestamp":1786965439000},"source":"Crossref","is-referenced-by-count":0,"title":["Validation of an artificial intelligence-based drawing platform against the Clock Drawing Test for cognitive screening: a comparative study between Alzheimer's disease and healthy controls"],"prefix":"10.1093","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4758-4413","authenticated-orcid":false,"given":"\u00dclk\u00fc","family":"Sur \u00dcnal","sequence":"first","affiliation":[{"name":"Department of Family Medicine, Marmara University School of Medicine , Istanbul 34854 ,","place":["T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Halil Alper","family":"Karaba\u015f","sequence":"additional","affiliation":[{"name":"Marmara University School of Medicine , Istanbul 34854 ,","place":["T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aybora","family":"Yadigar","sequence":"additional","affiliation":[{"name":"Marmara University School of Medicine , Istanbul 34854 ,","place":["T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahsen Simanur","family":"Turnal\u0131","sequence":"additional","affiliation":[{"name":"Marmara University School of Medicine , Istanbul 34854 ,","place":["T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elifnur","family":"Demir","sequence":"additional","affiliation":[{"name":"Marmara University School of Medicine , Istanbul 34854 ,","place":["T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"2026082504534392000_cmag066-B1","doi-asserted-by":"publisher","first-page":"2742","DOI":"10.1111\/jan.13810","article-title":"The Clock Drawing Test: a systematic review and meta-analysis of diagnostic accuracy","volume":"74","author":"Park","year":"2018","journal-title":"J Adv Nurs"},{"key":"2026082504534392000_cmag066-B2","doi-asserted-by":"publisher","DOI":"10.1002\/pcn5.70351","article-title":"Psychometric properties, pragmatic screening cut-offs, and feasibility of two Clock Drawing Test formats in older psychiatric outpatients: associations with MMSE and HDS-R","volume":"5","author":"Hatasaki","year":"2026","journal-title":"PCN Rep"},{"key":"2026082504534392000_cmag066-B3","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1017\/S1355617722000662","article-title":"Cultural influence on Clock Drawing Test: a systematic review","volume":"29","author":"Maestri","year":"2023","journal-title":"J Int Neuropsychol Soc"},{"key":"2026082504534392000_cmag066-B4","doi-asserted-by":"publisher","first-page":"59","DOI":"10.4103\/psychiatry.IndianJPsychiatry_62_18","article-title":"Usefulness of Clock-Drawing Test in Indian older adults with diabetes mellitus","volume":"62","author":"Tripathi","year":"2020","journal-title":"Indian J Psychiatry"},{"key":"2026082504534392000_cmag066-B5","doi-asserted-by":"publisher","first-page":"M534","DOI":"10.1093\/gerona\/54.11.M534","article-title":"The Clock Drawing Test: utility for dementia detection in multiethnic elders","volume":"54","author":"Borson","year":"1999","journal-title":"J Gerontol A Biol Sci Med Sci"},{"key":"2026082504534392000_cmag066-B6","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.3233\/JAD-230110","article-title":"Clock Drawing Test: types of errors and accuracy in early cognitive screening","volume":"95","author":"Sejunaite","year":"2023","journal-title":"J Alzheimers Dis"},{"key":"2026082504534392000_cmag066-B7","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1177\/10731911211053851","article-title":"The value of clock drawing process assessment in screening for mild cognitive impairment and Alzheimer's dementia","volume":"30","author":"Huang","year":"2023","journal-title":"Assessment"},{"key":"2026082504534392000_cmag066-B8","doi-asserted-by":"publisher","first-page":"38","DOI":"10.2174\/1567205014666170908101822","article-title":"The Clock Drawing Test: a reasonable instrument to assess probable dementia in primary care?","volume":"15","author":"Reiner","year":"2018","journal-title":"Curr Alzheimer Res"},{"key":"2026082504534392000_cmag066-B9","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1080\/13803390701346113","article-title":"Do age and education contribute to performance on the Clock Drawing Test? 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