ArticleBMC oral health2024
Reliability and accuracy of Artificial intelligence-based software for cephalometric diagnosis. A diagnostic study.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Parameter-level comparison of artificial intelligence and manual cephalometric measurements: a systematic review and meta-analysis.Frontiers in dental medicine · 2026Pooled it
- Artificial intelligence for skeletal classification in orthodontics: A systematic review and meta-analysis of lateral cephalometric studies.Journal of Taibah University Medical Sciences · 2026Review
- Left-Right Consistency and Quantification of Craniofacial Asymmetry by Two Fully Automatic AI-Powered Three-Dimensional Cephalometry Systems on CBCT.Diagnostics (Basel, Switzerland) · 2026Article
- Agreement of WebCeph-Based Automated and Expert-Adjusted Cephalometric Analyses with Manual and Dolphin Tracings.Diagnostics (Basel, Switzerland) · 2026Article
- Progress of Multimodal Imaging: Bridging Orthodontics to Comprehensive Oral Multidisciplinary Care.International dental journal · 2026Review
- Evaluation of artificial intelligence-based cephalometric analysis compared to the manual method.Progress in orthodontics · 2026Article
- Evaluation of cephalometric landmarks using artificial intelligence compared to the manual method.Head & face medicine · 2026Article
- Artificial Intelligence Methods in Cephalometric Image Analysis-A Systematic Narrative Review.Journal of clinical medicine · 2026Review
- Current Applications and Future Perspectives of Artificial Intelligence in Face-Driven Orthodontics: A Scoping Review.Biomimetics (Basel, Switzerland) · 2026Review
- Digital Dentistry in Clinical Practice: A Scoping Review of Current Capabilities and Future Directions.International dental journal · 2026Article
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
- Artificial Intelligence-Assisted Cephalometric Analysis: A Comparative Study Between WebCeph and NemoCeph.International journal of dentistry · 2026Article
- Evaluating a cost-effective, web-based AI platform for lateral cephalometric analysis: a comparative in-silico study.BMC oral health · 2025Article
- Evaluation of artificial intelligence-based cephalometric tracing versus semi-automatic and manual tracing.BMC oral health · 2025Article
- Automated classification of skeletal malocclusion in German orthodontic patients.Clinical oral investigations · 2025Article
- Tardiness hair in transitional zone: Analysis between age and frontal forehead hairline in children aged below 5 years old.PloS one · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is revolutionizing cephalometric diagnosis in orthodontics, streamlining the patient assessments. This study aimed to assess the reliability, accuracy, and time consumption of artificial intelligence (AI)-based software compared to a conventional digital cephalometric analysis method on 2D lateral cephalogram.
methods408 lateral cephalometries were analysed using three methods: manual landmark localization, automatic localization, and semi-automatic localization with AI-based software. On each lateral cephalogram, 15 variables were selected, including skeletal, dental, and soft tissue measurements. The difference between the two AI-based software options (automatic and semi-automatic) was compared with the conventional digital technique. The time required to produce a complete cephalometric tracing was evaluated for each method using Student's t-test.
resultsStatistically significant differences in the accuracy of landmark positioning were detected among the three different techniques (p < 0,01). However, it is noteworthy that almost all of these differences were not clinically significant. There was a small difference in accuracy between the semi-automatic AI-based option and conventional digital techniques. Regarding the time used for each technique, the automatic version was the fastest, followed by the semi-automatic option and the conventional digital technique. (p < 0,000).
conclusionsThe study showed a statistical difference in accuracy between the conventional digital technique and two AI-based software alternatives, but these differences were not clinically significant except for specific measurements. The semi-automatic option was more accurate than the automatic one and faster than conventional tracing. Further research is needed to confirm AI's accuracy in cephalometric tracing.
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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.