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Artificial Intelligence Methods in Cephalometric Image Analysis—A Systematic Narrative Review

cris.virtual.author-orcid0000-0003-0000-6157
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dc.abstract.enBackground: The dynamic development of information technologies, particularly in the fields of computer image analysis and artificial intelligence (AI) algorithms, plays an increasingly important role in orthodontic diagnostics. Cephalometric images constitute a fundamental element in orthodontic treatment planning. They contain encoded information related to the assessment of craniofacial growth and development, which is the focus of algorithms employing machine learning and process automation. Objectives: The aim of this paper is to present the current state of knowledge regarding the application of artificial intelligence methods in cephalometric image analysis, with particular emphasis on studies published between 2020 and 2025 in the Scopus and Web of Science databases. Results: Twenty key studies were included. The most commonly used models were convolutional neural networks (CNN), You Only Look Once (YOLO), Bayesian convolutional neural networks (BCNN), artificial neural networks (ANN), stacked hourglass networks, and Deep Neural Patchworks (DNP). In landmark detection tasks, the average location errors ranged from 1 to 2 mm compared to expert annotations, remaining within clinically acceptable limits. YOLO- and CNN-based systems achieved accuracy comparable to that of experienced orthodontists, while BCNN models additionally provided uncertainty estimates that improved clinical interpretability. In classification tasks, artificial neural network (ANN) models assessing cervical vertebral maturity (CVM) achieved an accuracy of up to 95%. In screening studies prior to orthognathic surgery, a multilayer perceptron combined with a regional convolutional neural network achieved 96.3% agreement with expert decisions. Conclusions: AI-based tools provide clinically acceptable accuracy in cephalometric analysis, with landmark detection errors typically ranging from 1 to 2 mm compared to expert assessment. These systems improve repeatability and significantly reduce analysis time, especially when used in semi-automated workflows. AI-based assessment of cervical vertebral maturity and surgical eligibility shows high agreement with expert decisions, confirming their role as reliable tools to support clinical decision-making. Nevertheless, broader validation in different patient populations is necessary before routine clinical implementation.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorZaborowicz, Katarzyna [UMed]
dc.contributor.authorZaborowicz, Maciej
dc.contributor.authorCieślińska, Katarzyna [UMed]
dc.contributor.authorBiedziak, Barbara
dc.date.access2026-03-04
dc.date.accessioned2026-03-04T09:21:31Z
dc.date.available2026-03-04T09:21:31Z
dc.date.copyright2026-03-03
dc.date.issued2026
dc.description.abstract<jats:p>Background: The dynamic development of information technologies, particularly in the fields of computer image analysis and artificial intelligence (AI) algorithms, plays an increasingly important role in orthodontic diagnostics. Cephalometric images constitute a fundamental element in orthodontic treatment planning. They contain encoded information related to the assessment of craniofacial growth and development, which is the focus of algorithms employing machine learning and process automation. Objectives: The aim of this paper is to present the current state of knowledge regarding the application of artificial intelligence methods in cephalometric image analysis, with particular emphasis on studies published between 2020 and 2025 in the Scopus and Web of Science databases. Results: Twenty key studies were included. The most commonly used models were convolutional neural networks (CNN), You Only Look Once (YOLO), Bayesian convolutional neural networks (BCNN), artificial neural networks (ANN), stacked hourglass networks, and Deep Neural Patchworks (DNP). In landmark detection tasks, the average location errors ranged from 1 to 2 mm compared to expert annotations, remaining within clinically acceptable limits. YOLO- and CNN-based systems achieved accuracy comparable to that of experienced orthodontists, while BCNN models additionally provided uncertainty estimates that improved clinical interpretability. In classification tasks, artificial neural network (ANN) models assessing cervical vertebral maturity (CVM) achieved an accuracy of up to 95%. In screening studies prior to orthognathic surgery, a multilayer perceptron combined with a regional convolutional neural network achieved 96.3% agreement with expert decisions. Conclusions: AI-based tools provide clinically acceptable accuracy in cephalometric analysis, with landmark detection errors typically ranging from 1 to 2 mm compared to expert assessment. These systems improve repeatability and significantly reduce analysis time, especially when used in semi-automated workflows. AI-based assessment of cervical vertebral maturity and surgical eligibility shows high agreement with expert decisions, confirming their role as reliable tools to support clinical decision-making. Nevertheless, broader validation in different patient populations is necessary before routine clinical implementation.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographybibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if2,9
dc.description.number5
dc.description.points140
dc.description.versionfinal_published
dc.description.volume15
dc.identifier.doi10.3390/jcm15051920
dc.identifier.issn2077-0383
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/7605
dc.identifier.weblinkhttps://www.mdpi.com/2077-0383/15/5/1920
dc.languageen
dc.pbn.affiliationmechanical engineering
dc.relation.ispartofJournal of Clinical Medicine
dc.relation.pagesart. 1920
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.enartificial intelligence
dc.subject.encephalometric analysis
dc.subject.endeep learning
dc.subject.enneural networks
dc.subject.enconvolutional neural network (CNN)
dc.subject.enlandmark detection
dc.subject.enorthodontics
dc.subject.encervical vertebral maturation (CVM)
dc.subject.enmachine learning
dc.subject.enorthognathic surgery
dc.subtypeReviewArticle
dc.titleArtificial Intelligence Methods in Cephalometric Image Analysis—A Systematic Narrative Review
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue5
oaire.citation.volume15