{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T04:54:31Z","timestamp":1782968071986,"version":"3.54.5"},"reference-count":16,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2024,1,30]]},"abstract":"<jats:p>A recent study by Ding et al. explores the integration of artificial intelligence (AI) in predicting dementia risk over a 10-year period using a multimodal approach. While revealing the potential of machine learning models in identifying high-risk individuals through neuropsychological testing, MRI imaging, and clinical risk factors, the imperative of dynamic frailty assessment emerges for accurate late-life dementia prediction. The commentary highlights challenges associated with AI models, including dimensionality and data standardization, emphasizing the critical need for a dynamic, comprehensive approach to reflect the evolving nature of dementia and improve predictive accuracy.<\/jats:p>","DOI":"10.3233\/jad-231071","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T10:44:33Z","timestamp":1704451473000},"page":"1097-1100","source":"Crossref","is-referenced-by-count":5,"title":["Rethinking Dementia Risk Prediction: A Critical Evaluation of a Multimodal Machine Learning Predictive Model"],"prefix":"10.1177","volume":"97","author":[{"given":"Silvia","family":"Ottaviani","sequence":"first","affiliation":[{"name":"Department of Internal Medicine and Medical Specialties (DIMI), Section of Geriatrics, University of Genoa, Genoa, Italy"},{"name":"IRCCS Ospedale Policlinico San Martino, Genoa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fiammetta","family":"Monacelli","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine and Medical Specialties (DIMI), Section of Geriatrics, University of Genoa, Genoa, Italy"},{"name":"IRCCS Ospedale Policlinico San Martino, Genoa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JAD-231071_ref1","doi-asserted-by":"crossref","first-page":"204","DOI":"10.5498\/wjp.v12.i2.204","article-title":"Screening dementia and predicting high dementia risk groups using machine learning","volume":"12","author":"Byeon","year":"2022","journal-title":"World J Psychiatry"},{"key":"10.3233\/JAD-231071_ref2","doi-asserted-by":"crossref","first-page":"277","DOI":"10.3233\/JAD-230496","article-title":"Multimodal machine learning for 10-year dementia risk prediction: The Framingham Heart Study","volume":"96","author":"Ding","year":"2023","journal-title":"J Alzheimers Dis"},{"key":"10.3233\/JAD-231071_ref3","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s10916-023-01906-7","article-title":"Machine learning for dementia prediction: A systematic review and future research directions","volume":"47","author":"Javeed","year":"2023","journal-title":"J Med Syst"},{"key":"10.3233\/JAD-231071_ref4","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.procs.2018.05.102","article-title":"Comparative analysis of various machine learning algorithms for detecting dementia","volume":"132","author":"Bansal","year":"2018","journal-title":"Procedia Comput Sci"},{"key":"10.3233\/JAD-231071_ref5","first-page":"5629090","article-title":"Machine learning for the preliminary diagnosis of dementia","volume":"2020","author":"Zhu","year":"2020","journal-title":"Sci Program"},{"key":"10.3233\/JAD-231071_ref6","doi-asserted-by":"crossref","first-page":"e2136553","DOI":"10.1001\/jamanetworkopen.2021.36553","article-title":"Performance of machine learning algorithms for predicting progression to dementia in memory clinic patients","volume":"4","author":"James","year":"2021","journal-title":"JAMA Netw Open"},{"key":"10.3233\/JAD-231071_ref8","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.3233\/JAD-191075","article-title":"Brain aging, cardiovascular diseases, mixed dementia, and frailty in the oldest old: From brain phenotype to clinical expression","volume":"75","author":"Rosa","year":"2020","journal-title":"J Alzheimers Dis"},{"key":"10.3233\/JAD-231071_ref9","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/S1474-4422(18)30371-5","article-title":"Investigation of frailty as a moderator of the relationship between neuropathology and dementia in Alzheimer\u2019s disease: A cross-sectional analysis of data from the Rush Memory and Aging Project","volume":"18","author":"Wallace","year":"2019","journal-title":"Lancet Neurol"},{"key":"10.3233\/JAD-231071_ref10","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1093\/gerona\/56.3.M146","article-title":"Frailty in older adults: Evidence for a phenotype. , M146-M","volume":"56","author":"Fried","year":"2001","journal-title":"J Gerontol A Biol Sci Med Sci"},{"key":"10.3233\/JAD-231071_ref11","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1503\/cmaj.050051","article-title":"A global clinical measure of fitness and frailty in elderly people","volume":"173","author":"Rockwood","year":"2005","journal-title":"CMAJ"},{"key":"10.3233\/JAD-231071_ref12","doi-asserted-by":"crossref","first-page":"e194","DOI":"10.1016\/S2666-7568(21)00028-3","article-title":"The interaction of cognitive and brain reservewith frailty in the association with mortality: An observationalcohort study","volume":"2","author":"Zijlmans","year":"2021","journal-title":"Lancet Healthy Longev"},{"key":"10.3233\/JAD-231071_ref13","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1038\/s41380-018-0098-1","article-title":"Brain age and other bodily \u2018ages\u2019: Implications for neuropsychiatry","volume":"24","author":"Cole","year":"2019","journal-title":"Mol Psychiatry"},{"key":"10.3233\/JAD-231071_ref14","doi-asserted-by":"crossref","first-page":"1063721","DOI":"10.3389\/fnagi.2022.1063721","article-title":"Health-related heterogeneity in brain aging and associations with longitudinal change in cognitive function","volume":"14","author":"Wrigglesworth","year":"2023","journal-title":"Front Aging Neurosci"},{"key":"10.3233\/JAD-231071_ref15","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.3233\/JAD-191058","article-title":"Late-life cognitivetrajectories and their associated lifestyle factors","volume":"73","author":"Yu","year":"2020","journal-title":"JAlzheimers Dis"},{"key":"10.3233\/JAD-231071_ref16","doi-asserted-by":"crossref","first-page":"116662","DOI":"10.1016\/j.jns.2019.116662","article-title":"Mixed dementia: Neglected clinical entity ornosographic artifice?","volume":"410","author":"Fierini","year":"2020","journal-title":"J Neurol Sci"},{"key":"10.3233\/JAD-231071_ref17","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1093\/brain\/awz099","article-title":"Limbic-predominant age-related TDP-43 encephalopathy (LATE): Consensus working group report","volume":"142","author":"Nelson","year":"2019","journal-title":"Brain"}],"container-title":["Journal of Alzheimer's Disease"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JAD-231071","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T10:09:19Z","timestamp":1777370959000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JAD-231071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,30]]},"references-count":16,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/jad-231071","relation":{},"ISSN":["1387-2877","1875-8908"],"issn-type":[{"value":"1387-2877","type":"print"},{"value":"1875-8908","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,30]]}}}