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Background <p>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.</p> Methods: <p>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.</p> Results <p>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%.</p> Conclusion <p>These findings highlight the value of integrating digital-health technologies and machine-learning models for CA NMS evaluation, potentially serving as scalable digital-markers.</p>

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Integrating remote testing and machine learning to identify markers of cerebellar ataxia at home

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Penina Ponger,
  • Yael De Picciotto,
  • Daniela Maisel,
  • Hanna Liandres,
  • Sarah Brisman,
  • William Saban

摘要

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.