ArticleBMC medical informatics and decision making2024
Comparison of semi and fully automated artificial intelligence driven softwares and manual system for cephalometric analysis.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Diagnostic Accuracy of a Machine Learning Model for Cervical Vertebra-Based Skeletal Maturity Assessment in Pediatric and Adolescent Patients Using Cephalometric Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Agreement of WebCeph-Based Automated and Expert-Adjusted Cephalometric Analyses with Manual and Dolphin Tracings.Diagnostics (Basel, Switzerland) · 2026Article
- Clinical Reliability of AI-Based Cephalometric Analysis Using WebCeph: A Comparative Agreement Study.Journal of clinical medicine · 2026Article
- A YOLOv12-based approach for automatic detection of cephalometric landmarks on 2D lateral skull X-ray images.Scientific reports · 2026Article
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Artificial Intelligence-Assisted Cephalometric Analysis: A Comparative Study Between WebCeph and NemoCeph.International journal of dentistry · 2026Article
- Adaptation and validation of the Wylie cephalometric analysis for three-dimensional assessment of canine occlusion in Labrador retrievers.Frontiers in veterinary science · 2026Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Evaluation of artificial intelligence-based cephalometric tracing versus semi-automatic and manual tracing.BMC oral health · 2025Article
- Article
- Influence of head positioning errors on the accuracy of fully automated artificial intelligence-based cephalometric software.The Angle orthodontist · 2025Article
- Review
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8 authors.
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Abstract
backgroundCephalometric analysis has been used as one of the main tools for orthodontic diagnosis and treatment planning. The analysis can be performed manually on acetate tracing sheets, digitally by manual selection of landmarks or by recently introduced Artificial Intelligence (AI)-driven tools or softwares that automatically detect landmarks and analyze them. The use of AI-driven tools is expected to avoid errors and make it less time consuming with effective evaluation and high reproducibility.
objectiveTo conduct intra- and inter-group comparisons of the accuracy and reliability of cephalometric tracing and evaluation done manually and with AI-driven tools that is WebCeph and CephX softwares.
methodsDigital and manual tracing of lateral cephalometric radiographs of 54 patients was done. 18 cephalometric parameters were assessed on each radiograph by 3 methods, manual method and by using semi (WebCeph) and fully automatic softwares (Ceph X). Each parameter was assessed by two investigators using these three methods. SPSS was then used to assess the differences in values of cephalometric variables between investigators, between softwares, between human investigator means and software means. ICC and paired T test were used for intra-group comparisons while ANOVA and post-hoc were used for inter-group comparisons.
resultsTwelve out of eighteen variables had high intra-group correlation and significant ICC p-values, 5 variables had relatively lower values and only one variable (SNO) had significantly low ICC value. Fifteen out of eighteen variables had minimal detection error using fully-automatic method of cephalometric analysis. Only three variables had lowest detection error using semi-automatic method of cephalometric analysis. Inter-group comparison revealed significant difference between three methods for eight variables; Witts, NLA, SNGoGn, Y-Axis, Jaraback, SNO, MMA and McNamara to Point A.
conclusionThere is a lack of significant difference among manual, semiautomatic and fully automatic methods of cephalometric tracing and analysis in terms of the variables measured by these methods. The mean detection errors were the highest for manual analysis and lowest for fully automatic method. Hence the fully automatic AI software has the most reproducible and accurate results.
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