ArticleScientific reports2022
AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
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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
27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 53 citations in OpenAlex.
- 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
- Pooled it
- Do patients with different craniofacial patterns have differences in upper airway volume? A systematic review with network meta-analysis.European journal of orthodontics · 2024Pooled it
- Bone-Anchored CT Angiography Framework for In Vivo Assessment of Zygomaticus Major Morphology.Biomedicines · 2026Article
- Comparison of linear and angular cephalometric measurements in CBCT using semi-automated software and artificial intelligence-generated cephalograms.BMC oral health · 2026Article
- Reliability of AI-Automated and Semiautomated Upper Airway Volume Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Use and regulation of patient-specific three-dimensional printing in medicine: a survey of physician end-users.3D printing in medicine · 2026Article
- Enhancing airway obstruction diagnosis with multimodal 3D shape analysis.International journal of computer assisted radiology and surgery · 2026Article
- Maximizing surgical precision in gracilis muscle reconstruction: anatomical Variants, advanced Imaging, and novel surgical strategies.Surgical and radiologic anatomy : SRA · 2025Review
- Review
- Deploying a novel deep learning framework for segmentation of specific anatomical structures on cone-beam CT.Oral radiology · 2025Article
- An Interdisciplinary Review of the Zygomaticus Muscles: Anatomical Variability, Imaging Modalities, and Clinical Implications.Journal of clinical medicine · 2025Review
- Review
- Strategies for Craniofacial Tissue Engineering: Innovations for Scalable Bone Regeneration.Plastic and aesthetic research · 2025Article
- Analysis of upper airway CT-based radiomics in adult obstructive sleep apnea.Frontiers in medicine · 2025Article
- Comparative Analysis of 3D Cephalometry Provided with Artificial Intelligence and Manual Tracing.Diagnostics (Basel, Switzerland) · 2024Article
- Automatic Segmentation of the Jaws Used in Guided Insertion of Orthodontic Mini Implants to Improve Their Stability and Precision.Medicina (Kaunas, Lithuania) · 2024Article
- Application of Artificial Intelligence in Cone-Beam Computed Tomography for Airway Analysis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- Performance enhancement of deep learning based solutions for pharyngeal airway space segmentation on MRI scans.Scientific reports · 2024Article
- The use of CBCT in orthodontics with special focus on upper airway analysis in patients with sleep-disordered breathing.Dento maxillo facial radiology · 2024Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to generate and also validate an automatic detection algorithm for pharyngeal airway on CBCT data using an AI software (Diagnocat) which will procure a measurement method. The second aim is to validate the newly developed artificial intelligence system in comparison to commercially available software for 3D CBCT evaluation. A Convolutional Neural Network-based machine learning algorithm was used for the segmentation of the pharyngeal airways in OSA and non-OSA patients. Radiologists used semi-automatic software to manually determine the airway and their measurements were compared with the AI. OSA patients were classified as minimal, mild, moderate, and severe groups, and the mean airway volumes of the groups were compared. The narrowest points of the airway (mm), the field of the airway (mm
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.