Deep Learning Prediction of Parkinson's Disease using Remotely Collected Structured Mouse Trace Data
Shahriar Zawad, M. R.; Tumpa, Z. N.; Sollis, L.; Parab, S.; Washington, P.
Show abstract
Parkinsons Disease (PD) is the second most common neurodegenerative disorder globally, and current screening methods often rely on subjective evaluations. We developed deep learning-based classification models using structured mouse trace data collected via a web application. 261 participants (73 PD, 155 non-PD, 33 suspected PD) completed three hand movement tasks: tracing a straight line, spiral, and sinewave. We developed three types of models: (1) engineered features model, (2) computer vision models, and (3) multimodal models. The best-performing models were image-based DenseNet-201 model with an F1 score of 0.9027 {+/-} 0.0332 (PD vs. non-PD), multimodal ResNet-50 with an F1 score of 0.9353 {+/-} 0.0334 (suspected PD vs. non-PD), and multimodal ViT with an F1 score of 0.7619 {+/-} 0.0535 (PD vs non-PD). Feature importance for the best-performing models was evaluated using Gradient Shapley Additive Explanations (GradShap). Image inputs consistently proved most predictive. The findings suggested that models trained on confirmed PD diagnoses hold promise for early-stage PD screening.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Quantifying Device Type and Handedness Biases in a Remote Parkinson’s Disease AI-Powered Assessment 98%
- Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge 94%
- A Machine-Learning Based Objective Measure for ALS Disease Severity 93%
Similar papers in this journal
- An explainable spatial-temporal graphical convolutional network to score freezing of gait in parkinsonian patients 95%
- Motor signatures in digitized cognitive and memory tests enhances characterization of Parkinson’s disease 95%
- Trends in Technology Usage for Parkinson's Disease Assessment: A Systematic Review 94%
Similar papers in this journal
- CDS-PD: A Novel Clinical Decision Support Platform for Parkinson's Disease 94%
- Developing and Validating a New Web-Based Tapping Test for Measuring Distal Bradykinesia in Parkinson's Disease 94%
- Remote monitoring of progression in early Parkinson’s disease: reliability and validity of the Roche PD Mobile Application v2 93%
Similar papers in this journal
- Use of assistive technology to assess distal motor function in subjects with neuromuscular disease 91%
- Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images 90%
- Performance of Generative Pretrained Transformer on the National Medical Licensing Examination in Japan 90%
Similar papers in this journal
- Characterizing subgroup performance of probabilistic phenotype algorithms within older adults: A case study for dementia, mild cognitive impairment, and Alzheimer’s and Parkinson’s diseases 91%
- Evaluation of Eye-tracking for a Decision Support Application 91%
- A Study of Calibration as a Measurement of Trustworthiness of Large Language Models in Biomedical Research 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.