Deep learning models to classify skeletal growth phase on 3D radiographs
Bibliographic record
Abstract
Objectives: Cervical vertebral maturation (CVM) is widely used to evaluate growth potential in orthodontics. This study aims to develop an artificial intelligence (AI) algorithm that automatically predicts the CVM stages in terms of growth phases using cone-beam computed tomography images. Material and Methods: A total of 30,016 slices were obtained from 56 patients with an age range of 7–16 years. After cropping the region of interest, a convolutional neural network (CNN) was built to classify the slices based on the presence of a good vision of vertebrae. The output was used to train another model capable of categorizing the slices into phases of growth, which were defined as Phase I (prepubertal), Phase II (circumpubertal), and Phase III (postpubertal). After training the model, 88 new images were used to evaluate the performance of the model using multi-class classification metrics. Results: The average classification accuracy of the first and second CNN-based deep learning models was 96.06% and 95.79%, respectively. The multi-class classification metrics also showed an overall accuracy of 84% for predicting the growth phase in unseen data. Moreover, Phase I ranked the highest accuracy in terms of F1-score (87%), followed by Phase II (83%) and Phase III (80%). Conclusion: Our proposed models could automatically detect the C2–C4 vertebrae and accurately classify slices into three growth phases without the need for annotating the shape and configuration of vertebrae. This will result in the development of a fully automatic and less complex system with reasonable performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".