Reliability and accuracy of segmentation of mandibular condyles from different three-dimensional imaging modalities: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To critically synthesize the literature surrounding segmentation of the mandibular condyle using three-dimensional imaging modalities. Specifically, analyzing the reliability and accuracy of methods used for three-dimensional condyle segmentation. METHODS: Three electronic databases were searched for studies reporting the reliability and accuracy of various methods used to segment mandibular condyles from three-dimensional imaging modalities. Two authors independently reviewed articles for eligibility and data extraction. RESULTS: Nine studies fulfilled the inclusion criteria. Eight studies assessed the condylar segmentation from CBCT images and limited studies were available on non-CBCT three-dimensional imaging modalities. Threshold-based volume segmentation, manual segmentation, and semi-automatic segmentation techniques were presented. Threshold-based volume segmentation reported higher accuracy when completed by an experienced technician compared to clinicians. Adequate reliability and accuracy were observed in manual segmentation. Although adequate reliability was reported in semi-automatic segmentation, data on its accuracy were lacking. CONCLUSION: A definitive conclusion with regards to which current technique is most reliable and accurate to efficiently segment the mandibular condyle cannot be made with the currently available evidence. This is especially true in terms of non-CBCT imaging modalities with very limited literature available.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".