{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xue C"],"funding":["NCATS NIH HHS","NIA NIH HHS","NIDDK NIH HHS","NHLBI NIH HHS","NINDS NIH HHS","NCI NIH HHS","NIGMS NIH HHS"],"pagination":["2977-2989"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11485262"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["30(10)"],"pubmed_abstract":["Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achi"],"journal":["Nature medicine"],"pubmed_title":["AI-based differential diagnosis of dementia etiologies on multimodal data."],"pmcid":["PMC11485262"],"funding_grant_id":["P30 AG062677","P30 AG066514","P30 AG066515","P30 AG066518","P30 AG066519","P30 AG062715","P20 AG068053","P30 AG066444","P30 AG072958","R01 NS115114","P30 AG072959","U19 AG068753","P30 AG062421","P30 AG062422","P30 AG066546","P30 AG066506","P30 AG066507","P30 AG066508","P30 AG062429","P30 AG066509","P30 AG066511","P30 AG066512","U24 AG072122","P20 AG068024","P30 AG072946","P01 AG003991","P30 AG072947","R01 HL159620","P50 AG047366","P30 AG066462","P30 AG072931","P30 AG072975","P20 AG068077","P30 AG072976","P30 AG072977","P30 AG072972","P20 GM130447","P30 AG066468","P30 AG072973","P30 AG072978","P30 AG072979","R01 AG079280","R43 DK134273","UL1 TR001430","P30 AG066530","P20 AG068082","K23 NS075097","R21 CA253498","P01 AG026276","U01 AG024904","RF1 AG062109"],"pubmed_authors":["O'Shea SA","Sartor EA","Paul AB","Xue C","Bargal SA","Andreu-Arasa VC","Swaminathan A","Ahangaran M","Kaliaev A","Karjadi C","Jasodanand VH","Au R","Rohatgi S","Poesy S","Puducheri S","Mian AZ","Setty BN","Small JE","Zhou OT","Zhou Y","Yuan J","Kowshik SS","Poston KL","Lteif D","Kedar S","Taraschenko O","Murman DL","Guney OB","Walia AS","Hao H","Saint-Hilaire MH","Zhang JD","Alvin Ang TF","Farris CW","Kolachalama VB","Dwyer BC","Zhu S","Plummer BA"],"additional_accession":[]},"is_claimable":false,"name":"AI-based differential diagnosis of dementia etiologies on multimodal data.","description":"Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achi","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Oct","modification":"2025-04-04T01:19:29.54Z","creation":"2025-04-04T01:19:29.54Z"},"accession":"S-EPMC11485262","cross_references":{"pubmed":["38965435"],"doi":["10.1038/s41591-024-03118-z"]}}