Summary of Deep Learning For Objective Estimation Of Parkinsonian Tremor Severity, by Felipe Duque-quiceno et al.
Deep learning for objective estimation of Parkinsonian tremor severity
by Felipe Duque-Quiceno, Grzegorz Sarapata, Yuriy Dushin, Miles Allen, Jonathan O’Keeffe
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed deep learning model is designed to accurately assess postural tremor in patients with Parkinson’s disease (PD) from video data. The model overcomes traditional pose estimation limitations and demonstrates robust concordance with clinical evaluations, effectively predicting treatment effects for levodopa and DBS. It also detects lateral asymmetry of symptoms and differentiates between different tremor severities. Feature space analysis reveals a non-linear distribution of tremor severity, and the model identifies outlier videos, suggesting potential for adaptive learning and quality control in clinical settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new computer program can help doctors better understand Parkinson’s disease by analyzing video recordings of patients’ movements. The program uses deep learning techniques to assess postural tremors, which are an important symptom of the disease. It is trained on a large dataset of videos and can accurately predict how well different treatments will work. The program also detects when symptoms are not symmetrical, which can be an important indicator of the disease’s progression. |
Keywords
» Artificial intelligence » Deep learning » Pose estimation
