Back to Search Start Over

AI-based differential diagnosis of dementia etiologies on multimodal data.

Authors :
Xue C
Kowshik SS
Lteif D
Puducheri S
Jasodanand VH
Zhou OT
Walia AS
Guney OB
Zhang JD
Pham ST
Kaliaev A
Andreu-Arasa VC
Dwyer BC
Farris CW
Hao H
Kedar S
Mian AZ
Murman DL
O'Shea SA
Paul AB
Rohatgi S
Saint-Hilaire MH
Sartor EA
Setty BN
Small JE
Swaminathan A
Taraschenko O
Yuan J
Zhou Y
Zhu S
Karjadi C
Ang TFA
Bargal SA
Plummer BA
Poston KL
Ahangaran M
Au R
Kolachalama VB
Source :
MedRxiv : the preprint server for health sciences [medRxiv] 2024 Mar 26. Date of Electronic Publication: 2024 Mar 26.
Publication Year :
2024

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 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 achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.<br />Competing Interests: Ethics declarations V.B.K. is on the scientific advisory board for Altoida Inc., and serves as a consultant to AstraZeneca. S.K. serves as consultant to AstraZeneca. C.W.F. is a consultant to Boston Imaging Core Lab. K.L.P. is a member of the scientific advisory boards for Curasen, Biohaven, and Neuron23, receiving consulting fees and stock options, and for Amprion, receiving stock options. R.A. is a scientific advisor to Signant Health and NovoNordisk. She also serves as a consultant to Davos Alzheimer’s Collaborative. The remaining authors declare no competing interests.

Details

Language :
English
Database :
MEDLINE
Journal :
MedRxiv : the preprint server for health sciences
Accession number :
38585870
Full Text :
https://doi.org/10.1101/2024.02.08.24302531