Are smartphones and machine learning enough to diagnose tremor?

• Published in: Journal of neurology Vol. 269; no. 11; pp. 6104 - 6115
• Main Authors: Balachandar, Arjun, Algarni, Musleh, Oliveira, Lais, Marsili, Luca, Merola, Aristide, Sturchio, Andrea, Espay, Alberto J., Hutchison, William D., Balasubramaniam, Aniruddh, Rudzicz, Frank, Fasano, Alfonso
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2022; Springer Nature B.V
• Subjects: Accuracy; Classification; Learning algorithms; Machine learning; Medicine; Medicine & Public Health; Movement disorders; Neurodegenerative diseases; Neurology; Neuroradiology; Neurosciences; Original Communication; Parkinson's disease; Patients; Smartphones; Tremor; Tremor (Muscular contraction); Essential tremor; Smartphone; Dystonic tremor; Parkinson’s disease; Machine learning
• ISSN: 0340-5354; 1432-1459; 1432-1459
• DOI: 10.1007/s00415-022-11293-7

Background Patients with essential tremor (ET), Parkinson’s disease (PD) and dystonic tremor (DT) can be difficult to classify and often share similar characteristics. Objectives To use ubiquitous smartphone accelerometers with and without clinical features to automate tremor classification using supervised machine learning, and to use unsupervised learning to evaluate if natural clusterings of patients correspond to assigned clinical diagnoses. Methods A supervised machine learning classifier was trained to classify 78 tremor patients using leave-one-out cross-validation to estimate performance on unseen accelerometer data. An independent cohort of 27 patients were also studied. Next, we focused on a subset of 48 patients with both smartphone-based tremor measurements and detailed clinical assessment metrics and compared two separate machine learning classifiers trained on these data. Results The classifier yielded a total accuracy of 74.4% and F1-score of 0.74 for a trinary classification with an area under the curve of 0.904, average F1-score of 0.94, specificity of 97% and sensitivity of 84% in classifying PD from ET or DT. The algorithm classified ET from non-ET with 88% accuracy, but only classified DT from non-DT with 29% accuracy. A poorer performance was found in the independent cohort. Classifiers trained on accelerometer and clinical data respectively obtained similar results. Conclusions Machine learning classifiers achieved a high accuracy of PD, however moderate accuracy of ET, and poor accuracy of DT classification. This underscores the difficulty of using AI to classify some tremors due to lack of specificity in clinical and neuropathological features, reinforcing that they may represent overlapping syndromes.