3D Video Tracking Technology in the Assessment of Orofacial Impairments in Neurological Disorders
Bibliographic record
Abstract
Changes to the orofacial muscles’ movement and speech are often among the earliest signs perceived in Neurological disorders. Detection of subtle changes in speech and facial movements can help with the diagnosis and prognosis of neurological disorders. Deep artificial intelligent video-based facial analysis models have the potential to be used as objective and non-invasive clinical tools. This thesis used the V3 framework for evaluation of digital biomarkers and their adoption into clinical settings to evaluate an automatic video-based facial analysis system as an objective assessment tool for accessing orofacial movements. The proposed system consists of a 3D camera and Artificial Intelligent-based algorithms that automatically extract objective clinically interpretable kinematic features from video recordings of individuals performing standard orofacial tasks. This work investigates the analytical and clinical validation of the proposed system to assess the severity of orofacial impairment in clinical groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".