Chonghua Xue, Sahana S Kowshik, Diala Lteif, Shreyas Puducheri, Varuna H Jasodanand, Olivia T Zhou, Anika S Walia, Osman B Guney, J Diana Zhang, Serena T Pham, Artem Kaliaev, V Carlota Andreu-Arasa, Brigid C Dwyer, Chad W Farris, Honglin Hao, Sachin Kedar, Asim Z Mian, Daniel L Murman, Sarah A O'Shea, Aaron B Paul, Saurabh Rohatgi, Marie-Helene Saint-Hilaire, Emmett A Sartor, Bindu N Setty, Juan E Small, Arun Swaminathan, Olga Taraschenko, Jing Yuan, Yan Zhou, Shuhan Zhu, Cody Karjadi, Ting Fang Alvin Ang, Sarah A Bargal, Bryan A Plummer, Kathleen L Poston, Meysam Ahangaran, Rhoda Au, Vijaya B Kolachalama
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...
March 26, 2024: medRxiv