ArticleFrontiers in oral health2026
Assessment of AI-based cephalometric landmark recognition on lateral cephalometric radiographs.
Article in Frontiers in oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) strategies have been proposed for automatic landmark recognition applications with the expectation of simplifying and improving cephalometric analysis through accurate and consistent landmarks identification. The aim of the current study was to evaluate the accuracy and reliability of AI in identifying cephalometric landmarks for orthodontics cephalometric tracing. Materials and methods: A total of 506 lateral cephalometric radiographs of patients aged 14-40 years were retrieved from the archives of Dubai Dental Hospital from the period of 2018-2021. Radiographs were traced using Dolphin imaging software, and the AI program (AI Ceph) was trained to identify 19 key cephalometric landmarks. Manual tracings were compared with AI-generated ones to evaluate the accuracy and reliability of the AI Ceph program. Results: High agreement was observed between manual and AI-generated tracings. The highest concordance was detected for Menton (96.6%), whereas the lowest was observed for Nasion (86.6%). The deviation between the two methods was within 2 mm for all landmarks except Glabella ( Conclusions: The tested AI method was accurate and reliable for identifying cephalometric landmarks, with a clinically acceptable 2 mm deviation. AI-assisted cephalometric tracing may serve as a useful adjunctive tool to support orthodontists in diagnosis and treatment planning.
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.