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Updated: Aug 6, 2026
A Simple Composite Phenotype Scoring System for Evaluating Mouse Models of Cerebellar Ataxia
Published on: May 21, 2010
Integrating remote testing and machine learning to identify markers of cerebellar ataxia at home
Penina Ponger1,2, Yael De Picciotto2, Daniela Maisel2
1Movement Disorders Division, Department of Neurology, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo, Israel.
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.
