How reliable are AI-Assisted cephalometric programs in assessing measurements involving bilateral landmarks?
Bibliographic record
Abstract
Abstract Objective Recent advancements in technology have promoted artificial intelligence (AI) to automatically detect landmarks on lateral cephalograms. This study aimed to evaluate the reliability of an AI-assisted cephalometric analysis program (Webceph, Gyeonggi-do, Republic of Korea) in the assessment of cephalometric measurements involving bilateral landmarks. Materials and methods Fifty-one high-quality cephalograms were used and inclusion/exclusion criteria were applied. Two researchers manually traced the cephalograms after which an AI-assisted cephalometric analysis program (Webceph) was applied. Both intra-and inter-operator reliability were tested. Independent Sample t-tests were used to compare the means of measurements. Results The inter- and intra-class correlation coefficients were 0.80 which indicated ‘good’ reliability. Statistically significant differences were found in the gonial angle and effective mandibular length measurements ( p <0.05), but not in the articular angle and FMA angle ( p >0.05). The results suggest that, while AI-assisted programs provide reliable measurements, differences in certain measurements may be attributed to inherent AI algorithm limitations. Clinicians should verify and, if needed, correct bilateral landmark locations after the initial AI digitisation. Conclusion AI-driven cephalometric analysis holds promise for improving diagnostic efficiency and precision. However, limitations and further AI advancements require consideration to ensure appropriate clinical use.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".