INTRODUCTION
Artificial intelligence (AI) has been increasingly applied to facial-image analysis in orthodontics, with potential applications in radiation-free screening, dentofacial assessment, and treatment-decision support. This systematic review evaluated the diagnostic and predictive performance of AI models applied to two-dimensional (2D) and three-dimensional (3D) facial data.
METHODS
The protocol was registered in PROSPERO (CRD420261419145). PubMed, Embase, Scopus, Web of Science, Cochrane Library, ClinicalTrials.gov, Google Scholar, and Open Science Framework were searched. Eligible studies evaluated AI models using facial photographs, multi-view photographs, 3D facial scans, reconstructed facial models, or landmark-coordinate inputs for downstream diagnostic, predictive, classification, or treatment-decision tasks. Studies limited exclusively to landmark localization were excluded. Data were synthesized qualitatively. Risk of bias was assessed with QUADAS-2 and certainty of evidence with GRADE.
RESULTS
Fourteen studies were included. Applications comprised cephalometric prediction, skeletal-pattern classification, mandibular-deformity diagnosis, soft-tissue depth assessment, treatment-difficulty classification, and orthognathic-surgery-need prediction. Across classification studies, accuracy ranged from 73.1% to 97.7%, AUC from 0.768 to 0.987, sensitivity or recall from 64.3% to 97.3%, and specificity from 80.5% to 95.6%. Only one study performed true external validation. Methodological heterogeneity, predominantly internal evaluation, and low overall certainty limited clinical translation.
CONCLUSION
AI-based analysis of 2D and 3D facial data showed promising performance for selected orthodontic and dentofacial tasks. Current evidence supports its use as an adjunct to specialist assessment rather than as an autonomous diagnostic substitute.
PROSPERO REGISTRATION
CRD420261419145.
Hugo Henrique dos Santos Dantas Guimarães, Karla-Nogueira Matos· International Orthodontics· 0 citations
Vertical root fracture (VRF) remains a diagnostic challenge because radiographic signs are often subtle, non-specific and influenced by imaging modality, fracture characteristics, artefacts and observer interpretation. This systematic review and meta-analysis assessed the accuracy of artificial intelligence (AI)-based models for VRF detection on cone-beam computed tomography (CBCT), periapical radiographs and panoramic radiographs and evaluated the certainty of evidence. This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies recommendations and was prospectively registered in International Prospective Register of Systematic Reviews. Studies evaluating AI-based models for VRF detection on dental imaging were included. Sensitivity and specificity were pooled using bivariate random-effects models when complete 2 × 2 contingency tables were available. Summary receiver operating characteristic curves were generated. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 and certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation approach adapted for diagnostic test accuracy. Post-hoc sensitivity analyses retained one representative dataset per study to assess the influence of multiple model-specific datasets. Six studies were included, of which 4 contributed to quantitative synthesis. The CBCT-based AI models showed high diagnostic performance under predominantly controlled or partially controlled conditions, with a pooled sensitivity of 85.3% and specificity of 89.0% (area under the curve [AUC]=0.921). The CBCT convolutional neural network–only subgroup showed sensitivity of 88.3% and specificity of 87.6% (AUC=0.929). Periapical radiograph-based models showed high sensitivity but limited specificity, with pooled sensitivity of 86.9% and specificity of 61.2% (AUC=0.708). The probabilistic neural network-only periapical subgroup improved sensitivity, but specificity remained limited. Sensitivity analyses confirmed preserved CBCT performance and persistently low periapical specificity. Panoramic radiography was assessed qualitatively because complete 2 × 2 data were unavailable. Certainty of evidence was moderate for CBCT and low for periapical radiographs. AI-based models show promising diagnostic potential for VRF detection, particularly on CBCT. However, current evidence mainly reflects performance under controlled or partially controlled conditions and should not be interpreted as definitive real-world diagnostic effectiveness. AI should currently be considered an adjunctive decision-support tool rather than an autonomous diagnostic method, especially for 2-dimensional imaging modalities. Prospective, multicentre clinical validation studies are needed.
Karla-Nogueira Matos, Hugo Henrique dos Santos Dantas Guimarães, Pedro Vitor Dos Santos Sobrinho et al.· European Endodontic Journal· 0 citations
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