Early-Stage Parkinson's Disease Detection Based on Optical Flow and Video Vision Transformer
Bibliographic record
Abstract
Hypomimia, a symptom of Parkinson's disease (PD), is marked by reduced facial movements and loss of face emotional expressions. This study focuses on identifying hypomimia in individuals with early-stage PD using optical-flow-based video vision transformer. Our study included video recordings from 109 PD and 45 healthy control (HC) subjects with an average of two videos per person (294 videos in total). The participants asked to speak freely while being recorded. To extract typical facial muscle movements from subjects, we computed the optical flow (OF) from the videos. Video vision transformer is then used to infer feature representations from OF and RGB modalities, input to a Random Forest (RF) classifier to classify PD vs. HC. We obtained classification scores up to 83% in terms of balanced accuracy (BA) and an area under the curve (AUC) of 84% at subject level. The results are promising for identifying hypomimia in the early stages of PD, and this research could lead to the possibility of continuous monitoring of hypomimia outside of hospital settings via telemedicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".