Communications medicine · 2026
Integrating remote testing and machine learning to identify markers of cerebellar ataxia at home.
СтатьяНазвание и аннотация: Перевод в очереди
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Ponger Penina, De Picciotto Yael, Maisel Daniela, Liandres Hanna, Brisman Sarah, Saban William
Аннотация
BACKGROUND: In-person assessments face accessibility, scalability, and geographic diversity challenges, especially for rare diseases. Additionally, Cerebellar Ataxia (CA) non-motor symptoms(NMS) are often overlooked. We aimed to address these gaps by leveraging the Internet and machine-learning.
METHODS: In a bi-center study, we assessed 100 participants: 30 CA, 45 neurotypically healthy(NH), and 25 Parkinson's disease(PD), recruited from 57 geographical locations across two countries. We evaluated multiple domains-cognition, anxiety, depression, social support, and personality-using accessible online tools. We applied leave-one-out cross-validation and feature importance analysis to examine the machine-learning model's ability to distinguish between groups and identify the most sensitive and specific CA predictors.
RESULTS: Machine-learning models trained on these remote non-motor features alone, yield AUCs of 0.74/0.76(CA vs. NH) and 0.78/0.79 (CA vs. PD) using leave-one-out cross-validation, demonstrating classification power exceeding 20%.
CONCLUSION: These findings highlight the value of integrating digital-health technologies and machine-learning models for CA NMS evaluation, potentially serving as scalable digital-markers.
METHODS: In a bi-center study, we assessed 100 participants: 30 CA, 45 neurotypically healthy(NH), and 25 Parkinson's disease(PD), recruited from 57 geographical locations across two countries. We evaluated multiple domains-cognition, anxiety, depression, social support, and personality-using accessible online tools. We applied leave-one-out cross-validation and feature importance analysis to examine the machine-learning model's ability to distinguish between groups and identify the most sensitive and specific CA predictors.
RESULTS: Machine-learning models trained on these remote non-motor features alone, yield AUCs of 0.74/0.76(CA vs. NH) and 0.78/0.79 (CA vs. PD) using leave-one-out cross-validation, demonstrating classification power exceeding 20%.
CONCLUSION: These findings highlight the value of integrating digital-health technologies and machine-learning models for CA NMS evaluation, potentially serving as scalable digital-markers.