SynthesisClinical and experimental dental research2026
Efficacy of Automatic 3D Segmentation of the Upper Airway in CBCT or CT Scans via Artificial Intelligence Versus Manual Segmentation by Human Experts: A Systematic Review and Meta-Analysis.
Synthesis in Clinical and experimental dental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Efficacy of Automatic 3D Segmentation of the Upper Airway in CBCT or CT Scans via Artificial Intelligence Versus Manual Segmentation by Human Experts: A Systematic Review and Meta-Analysis.Clinical and experimental dental research · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectives3D segmentation of the upper airway is crucial for dental and medical practices. However, it is a difficult and daunting task. Like almost all other areas, AI can theoretically help in airway segmentation. Nevertheless, AI's efficacy remains unknown. This meta-analysis investigated this matter for the first time. MATERIAL AND
methodsVarious search engines/databases/articles were searched for articles published until April 25, 2025. All English-language articles on the use of AI for upper airway evaluations based on CBCT or CT scans were included in the study. The desired population was considered studies assessing the upper airway. Intervention was the use of any tool of AI such as deep learning and machine learning for image analysis. The comparator was the manual analysis of CBCT or CT scans by human. The outcome was the analysis of upper airway on CBCT or CT images. The recorded and analyzed effect sizes were: accuracies, precisions, dice similarity scores, total volume differences, intersection over union (IoU), recall, or any other parameters relevant to segmentation. A meta-analysis was conducted for each of the mentioned parameters if adequate data were available. The outcome was the analysis of upper airway on CBCT or CT images (PROSPERO: CRD42024508004).
resultsEleven studies were included, with 6 studies included in meta-analyses. Most studies had a low risk of bias in most aspects. The qualitative part of review showed promising results for AI segmentation. Four of the effects sizes were meta-analyzed: Precision, dice similarity score, intersection over union, and recall were all above 90%. Total volume difference was small but significantly above zero. Sensitivity analyses showed robustness of all meta-analysis results. Publication bias was insignificant.
conclusionsThe results showed promising AI efficacies in 3D segmentation of the upper airway in CBCTs. However, much more studies are needed before decisive conclusions.
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