ArticleThe Angle orthodontist2024
Evaluation of automated photograph-cephalogram image integration using artificial intelligence models.
Article in The Angle orthodontist, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Evaluation of automated tooth landmark localization on digital models.The Angle orthodontist · 2026Article
- Sex estimation from lateral cephalograms via a hybrid multimodel convolutional neural network.Scientific reports · 2026Article
- What amount of data is required to develop artificial intelligence that can accurately predict soft tissue changes after orthognathic surgery?The Angle orthodontist · 2025Article
- Clinical Failure of General-Purpose AI in Photographic Scoliosis Assessment: A Diagnostic Accuracy Study.Medicina (Kaunas, Lithuania) · 2025Article
- Influence of head positioning errors on the accuracy of fully automated artificial intelligence-based cephalometric software.The Angle orthodontist · 2025Article
- Development and validation of a graph convolutional network (GCN)-based automatic superimposition method for maxillary digital dental models (MDMs).The Angle orthodontist · 2025Article
- Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children.The Angle orthodontist · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop and evaluate an automated method for combining a digital photograph with a lateral cephalogram. MATERIALS AND
methodsA total of 985 digital photographs were collected and soft tissue landmarks were manually detected. Then 2500 lateral cephalograms were collected, and corresponding soft tissue landmarks were manually detected. Using the images and landmark identification information, two different artificial intelligence (AI) models-one for detecting soft tissue on photographs and the other for identifying soft tissue on cephalograms-were developed using different deep-learning algorithms. The digital photographs were rotated, scaled, and shifted to minimize the squared sum of distances between the soft tissue landmarks identified by the two different AI models. As a validation process, eight soft tissue landmarks were selected on digital photographs and lateral cephalometric radiographs from 100 additionally collected validation subjects. Paired t-tests were used to compare the accuracy of measures obtained between the automated and manual image integration methods.
resultsThe validation results showed statistically significant differences between the automated and manual methods on the upper lip and soft tissue B point. Otherwise, no statistically significant difference was found.
conclusionsAutomated photograph-cephalogram image integration using AI models seemed to be as reliable as manual superimposition procedures.
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.