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

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. 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. 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%. These findings highlight the value of integrating digital-health technologies and machine-learning models for CA NMS evaluation, potentially serving as scalable digital-markers. Cerebellar ataxia (CA) is a neurological condition that affects movement, but people with CA often have problems beyond movement, such as changes in thinking and mood. People with CA find it difficult to travel to clinics, making assessments challenging. We tested whether these non-movement symptoms could be measured remotely using online platforms. We compared people with CA with healthy controls and people with Parkinson’s disease. We found that people with CA showed a distinct pattern of non-movement symptoms, especially in cognitive abilities and personality traits. Computer programs trained solely on these online measures achieved medium accuracy in identifying people with CA. These findings suggest that online assessments could improve access to care and research for people with CA, especially those who live far from medical centers. Ponger et al. combine remote non-motor assessments with machine learning to identify cerebellar ataxia across geographically diverse populations. Their findings support the potential of remotely acquired non-motor digital markers as scalable tools for ataxia detection and monitoring.