REMOVED: Human-AI collaboration for mandibular canal tracing accuracy on cone-beam computed tomography: A multi-evaluator study
Bibliographic record
Abstract
Objectives To evaluate the accuracy, efficiency, and reliability of manual, artificial intelligence (AI)-driven, and AI-assisted tracing methods for locating the mandibular canal (MC) on cone-beam computed tomography (CBCT) across clinical scenarios and evaluators. Methods Phase 1 established a calibration reference standard using a dry human mandible. In Phase 2, five evaluators assessed ten CBCT scans using manual and AI-assisted methods, while one expert performed all methods. The dataset included a range of anatomical variations and alterations. MC tracings were evaluated using Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD95), and boundary precision differences was analyzed with a linear mixed-effects model. Accuracy, efficiency, and agreement were assessed. Results A total of 292 MC tracings were evaluated. Manual tracing showed excellent intra-evaluator consistency (DSC: 0.99, HD95: 1.11) but low inter-evaluator agreement (ICC: 0.30). AI-assisted methods demonstrated near-perfect intra-evaluator reliability (DSC: 0.81, HD95: 2.2) and good inter-evaluator agreement (ICC: 0.72, DSC: 0.91, HD95: 1.23). Manual tracing showed the highest accuracy (DSC: 0.98) and was the slowest (6.2 min). The AI-driven method showed strong accuracy (DSC: 0.87) and was the fastest (13-20 s). The AI-assisted method balanced speed (4.2 min) and accuracy (DSC: 0.83), with superior boundary precision compared to manual ( p = 3×10 -4 ) and AI-driven methods ( p = 1.0). Conclusion Manual tracing remains the most accurate method, time-intensive, and variable across evaluators. AI-driven tracing improved efficiency but was less reliable in complex cases. AI-assisted tracing balanced accuracy and efficiency, improved boundary precision, and reduced variability, supporting its role in clinical decision-making. Clinical Relevance The AI-assisted mandibular canal tracing method provides an effective balance between accuracy and efficiency. This reduces inter-user variability and improves reliability across users, even in complex anatomical cases.
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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 | 0.047 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".