Assessment of Children's Dental Fear and Anxiety Using Deep Learning Techniques
Bibliographic record
Abstract
Background About one-third of children face dental fear and anxiety, jeopardizing treatment outcomes. This study develops deep learning algorithms to automate drawing analysis, addressing the limitations of subjective and time-intensive manual assessments. Methods Two deep learning systems were developed. In the pilot study, drawings were manually labeled using the Child Drawing: Hospital score sheet. The first system used a two-step classification, focusing on human figures. A ResNet-18 model categorized drawings into high or low-average anxiety, while YOLO-v5 detected human figures, and YOLO-v8 classified those in low-average anxiety drawings. The second approach evaluated layout characteristics using pixel value calculations and scored color attributes with k-means clustering. A two-step deep learning process assessed human figure features: YOLO-v5 detected human figures and recognized faces, while YOLO-v8 classified faces as happy or unhappy. Final anxiety levels were determined by the combined feature scores. Accuracy and precision were reported for classifiers and object detectors, respectively. Results The first approach showed promising results, with the ResNet-18 classifier achieving a testing accuracy of 0.93 (95% CI: 0.878-0.982), the object detector achieving precision of 0.64 (95% CI: 0.531-0.749), and across five-fold cross-validation, the final classifier achieving a mean test accuracy of 0.66 (95% CI: 0.627, 0.6966). Confidence intervals were computed using the t-distribution with 4 degrees of freedom. Since the testing set was too small, a normal distribution assumption for the testing set was unrealistic. Therefore, a five-fold cross-validation was performed. The second approach achieved a final accuracy of 0.76, with individual features scoring a moderate accuracy of around 0.60. Conclusion This study demonstrates the potential of artificial intelligence to automate children’s dental fear and anxiety assessments through drawing analysis. Practical Implications: The developed algorithms can be used to screen children's dental anxiety, aiding clinicians in managing anxiety effectively, understanding patient concerns, and improving communication with children with linguistic or cognitive barriers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".