Artificial intelligence algorithms effectively classify 38 movements in infants born full-term and preterm recorded in the laboratory and at home
Bibliographic record
Abstract
BACKGROUND: Although recent advancements in artificial intelligence (AI) provide an alternative for infant motor assessment, such applications focus mainly on infants' movements within a narrow age range in a laboratory setting. This study aimed to develop and validate AI algorithms for movement recognition in full-term and preterm children in the laboratory and at home throughout infancy. METHODS: This prospective cohort study included 85 full-term infants and 84 preterm infants who were video recorded during the Alberta Infant Motor Assessment (AIMS) administered by physiotherapists in a laboratory, while parents uploaded movement videos at home using the Baby Go mobile application (app) from 4 to 18 months of age. An AI model was developed to classify movements using physiotherapists' labeling results as the ground truth. The AI-classified movements were organized into age-based sets to test concurrent validity against the AIMS examiners' results. RESULTS: Validation of the AI model for classifying 38 movements in full-term and preterm infants revealed an accuracy of 0.91, precision of 0.92, recall of 0.90, and F1 score of 0.91 with the laboratory videos and an accuracy of 0.84, precision of 0.84, recall of 0.77, and F1 score of 0.78 with the home videos. These movements were dispatched into age-based sets with two to five movements per age that showed high concurrent validity with the AIMS results (agreement = 0.99). CONCLUSION: The AI model accurately classified 38 movements in full-term and preterm infants performed in the laboratory and at home. The age-based sets also highly correlated with the physiotherapists' assessment results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".