Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis
Bibliographic record
Abstract
The diagnosis of cerebral palsy (CP) is difficult before 2 years of age. The general movements assessment (GMA) is a method for predicting CP from the spontaneous movements of infants in the first months of life. This assessment has shown high accuracy in predicting CP, but its use is limited by a lack of trained clinicians and its subjective nature. An objective and cost-effective alternative is the automatic videobased assessment of infant movements. Retrospective videos with clinical GMA outcomes were evaluated against eligibility criteria for the automatic analysis consisting of a skin model for segmentation and large displacement optical flow (LDOF) for motion tracking. Kinematic features were extracted to classify the movements as typical or atypical using different classification algorithms. Preliminary classification results obtained from the analysis of 127 videos of preterm infants showed up to 92% of accuracy in predicting CP. A computerbased assessment would provide clinicians with an objective tool for early diagnosis of CP, to facilitate early intervention and improve functional outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".