Three-dimensional segmentation of the upper airway using cone beam CT: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objectives of this study were to systematically review the literature for studies that used cone beam CT (CBCT) to automatically or semi-automatically model the upper airway (including the pharyngeal, nasal and paranasal airways), and to assess their validity and reliability. Several electronic databases (MEDLINE®, MEDLINE In-Process & Other Non-Indexed Citations, all evidence-based medicine reviews including the Cochrane database, and Scopus) were searched. Abstracts that appeared to meet the initial selection criteria were selected by consensus. The original articles were then retrieved and their references were searched manually for potentially suitable articles that were missed during the electronic search. Final articles that met all the selection criteria were evaluated using a customized evaluation checklist. 16 articles were finally selected. From these, five scored more than 50% based on their methodology. Although eight articles reported the reliability of the airway model generated, only three used intraclass correlation (ICC). Two articles tested the accuracy/validity of airway models against the gold standard, manual segmentation, using volumetric measurements; however, neither used ICC. Only three articles properly tested the reliability of the three-dimensional (3D) upper airway model generated from CBCT and only one article had sufficiently sound methodology to test the airway model's accuracy/validity. The literature lacks proper scientific justification of a solid and optimized CBCT protocol for airway imaging. Owing to the limited number of adequate studies, it is difficult to generate a strong conclusion regarding the current validity and reliability of CBCT-generated 3D models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it