Facial morphology analysis in osteogenesis imperfecta types I, III and IV using computer vision
Bibliographic record
Abstract
Abstract Objective Osteogenesis imperfecta (OI) is an autosomal dominant genetic disease that mainly affects the COL1A1/A2 genes. Individuals affected by OI types I, III, and IV have been reported to demonstrate characteristic facial manifestations of the disease. This study aimed to quantitatively assess OI patients’ morphological characteristics. Materials and Methods This retrospective case‐control study involved 306 individuals (145 male and 161 female). It used automatic facial annotation and statistical shape analysis to compare facial photographs of individuals affected by OI types I, III, and IV with a normocephalic control group. Four facial ratios were used to compare facial proportions. Additionally, we proposed a novel approach to facial analysis using 68 landmarks and statistical shape analysis to compare morphological features. A predictive model (PCALog) was trained to detect whether a subject was affected by OI, based on facial landmarks. Results Our findings correlate with previous reports of OI type III patients’ facial characteristics being the most severely affected among the three types studied. Our novel approach facilitated an interpretation and comparison of morphological changes. Moreover, we successfully trained our PCALog model to automatically detect OI based on landmark features. Conclusion We found patients’ facial manifestations of OI to be more pronounced at the level of the eyes and temples. Our morphological approach facilitates the comparison of various groups and should be considered for future craniofacial analysis studies. Machine learning models can be trained using facial landmarks to detect the presence of conditions that affect facial morphology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".