AI-based differential diagnosis of dementia etiologies on multimodal data
Boston University · UNSW Sydney · +14 more institutions
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…
Citation impact
- FWCI
- 48.40
- Percentile
- 100%
- References
- 87
Authors
38Topics & keywords
- Dementia
- Etiology
- Differential diagnosis
- Medicine
- Differential (mechanical device)
- Neuroscience
- Bioinformatics
- Intensive care medicine
Funding
- AAAlzheimer's AssociationAwards: P30 AG072947, P30 AG062421, P30 AG062422, P30 AG066468
- AHAmerican Heart AssociationAward: 20SFRN35460031
- BSBristol-Myers SquibbAward: AG024904
- ELEli Lilly and CompanyAward: AG024904
- PPfizerAward: AG024904
- BBiogenAward: AG024904
- UOUniversity of Southern California
- MSMeso Scale DiagnosticsAward: AG024904
- BBioClinicaAward: AG024904
- NPNovartis Pharmaceuticals CorporationAward: AG024904
- NCNorthern California Institute for Research and Education
- EEisaiAward: AG024904
- SServierAward: AG024904
- HLH. Lundbeck A/SAward: AG024904
- IIXICOAward: AG024904
- NINational Institutes of HealthAwards: P30 AG072979, AG066514, P30 AG066508, AG072978, AG024904, P30 AG072973, P30 AG072958, P30 AG062715, P30 AG062429, P20 AG068053, AG066512, P30 AG062422, R01-HL159620, P30 AG066444, P30 AG072975, AG068077, AG066444, P30 AG066518, NS075097, AG066518, P30 AG066519, P30 AG062677, P20 AG068082, AG072972, P30 AG072947, P50-AG047366, 1UL1TR001430, R21-CA253498, P30 AG066507, 4RTNI, AG072946, P30 AG062421, U19-AG068753, AG066515, RF1-AG062109, P30 AG072959, P30 AG066515, AG066468, P30 AG066506, P30 AG079280, P30 AG066509, AG066508, P30 AG066468, R43-DK134273, P30 AG072946, P30 AG066530, U24-AG072122, AG066519, AG047366, AG062677, AG072979, P30 AG066512, P20-GM130447, P30 AG066546, P30 AG072972, P30 AG066511, P30 AG066462, P30 AG072977, AG066511, CTSI 1UL1TR001430, P20 AG068077, AG062429, P30 AG072976, U01-AG024904, AG066509, P30 AG066514, P30 AG072931, AG062421, P20 AG068024
- GGenentechAward: AG024904
- CICanadian Institutes of Health Research
- NINational Institute on AgingAwards: AG072946, P30 AG072976, P30 AG066506, AG066468, P30 AG072975, AG062429, AG066546, AG066507, AG062421, P30 AG066468, AG072979, RF1-AG062109, AG066518, P30 AG072931, AG047366, AG066519, U19-AG068753, P30 AG066507, AG066511, P20 AG068024, P20 AG068053, P30 AG072958, P30 AG072973, AG072977, P30 AG066530, AG072978, AG066509, AG068753, AG072931, 1UL1TR001430, P30 AG066518, AG066444, P30 AG062429, P30 AG066519, AG062715, P30 AG072978, P30 AG066512, P30 AG072947, AG072959, P30 AG062715, P30 AG072959, AG066462, P30 AG072946, P30 AG072979, P30 AG066546, P30 AG066515, P30 AG066511, P20 AG068077, AG066515, P30 AG066508, P30 AG066462, AG024904, U24-AG072122, P30 AG066444, AG062109, AG068082, AG068024, P30 AG062677, AG062422, U01-AG024904, P30 AG062421, P30 AG072972, P30 AG062422, P30 AG066514, P30 AG066509, AG066512, P20 AG068082, P30 AG072977, AG066508, P30 AG079280