SynthesisDento maxillo facial radiology2025
Comparisons of artificial intelligence automated segmentation techniques to manual segmentation techniques of the maxilla and maxillary sinus for CT or cone-beam CT scans-a systematic review.
Synthesis in Dento maxillo facial radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-based segmentation of mandibular canal on cone-beam computed tomography: a systematic review and meta-analysis.Oral radiology · 2026Pooled it
- A novel fluoride tray-assisted workflow for artificial intelligence-driven automated maxillary gingival segmentation on cone beam CT.Dento maxillo facial radiology · 2026Article
- Three-Dimensional Palatal Morphology and Obstructive Sleep Apnea Severity in Children with Unilateral Cleft Lip and Palate: A CBCT Study.Children (Basel, Switzerland) · 2026Article
- AI-Assisted Identification of the Medial Lingual Foramen on CBCT: A Deep Learning Approach for Preoperative Implant Assessment.Medicina (Kaunas, Lithuania) · 2026Article
- CBCT assessment of maxillary sinus sexual dimorphism: morphometry and sex-classification performance in Peruvian adults.The Journal of forensic odonto-stomatology · 2026Observational
- A Novel Method for Assessing Sinusitis Activity in Minimally Symptomatic Patients with ANCA-Associated Vasculitis: A Pilot Study.Journal of clinical medicine · 2025Article
- Assessment of the Connection Between Maxillary Sinus Volume, Number of Surgical Interventions, and Craniofacial Development in Patients with Unilateral Cleft Lip and Palate: A Retrospective Cross-Sectional Study.Journal of clinical medicine · 2025Article
- Shaping Orthodontics of the Future: Concepts and Implications from a Cellular and Molecular Perspective.International journal of molecular sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAccurate segmentation of the maxillary sinus from medical images is essential for diagnostic purposes and surgical planning. Manual segmentation of the maxillary sinus, while the gold standard, is time consuming and requires adequate training. To overcome this problem, artificial intelligence (AI) enabled automatic segmentation software's developed. The purpose of this review is to systematically analyse the current literature to investigate the accuracy and efficiency of automatic segmentation techniques of the maxillary sinus to manual segmentation.
methodsA systematic approach to perform a thorough analysis of the existing literature using PRISMA guidelines. Data for this study was obtained from Pubmed, Medline, Embase, and Google Scholar databases. The inclusion and exclusion eligibility criteria were used to shortlist relevant studies. The sample size, anatomical structures segmented, experience of operators, type of manual segmentation software used, type of automatic segmentation software used, statistical comparative method used, and length of time of segmentation were analysed.
resultsThis systematic review presents 10 studies that compared the accuracy and efficiency of automatic segmentation of the maxillary sinus to manual segmentation. All the studies included in this study were found to have a low risk of bias. Samples sizes ranged from 3 to 144, a variety of operators were used to manually segment the cone-beam computed tomography (CBCT) and segmentation was made primarily to 3D slicer and Mimics software. The comparison was primarily made to Unet architecture softwares, with the dice-coefficient being the primary means of comparison.
conclusionsThis systematic review showed that automatic segmentation technique was consistently faster than manual segmentation techniques and over 90% accurate when compared to the gold standard of manual segmentation.
Indexed as
Identifiers
What OpenQuestion holds
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.