Journal of the World federation of orthodontists · 2026
Artificial intelligence for detecting orthodontic root resorption: A systematic review and meta-analysis of diagnostic accuracy.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод пока не подготовлен
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Matos Karla Nogueira, Dos Santos Dantas Guimarães Hugo Henrique
Аннотация
BACKGROUND: External root resorption (ERR) and external cervical resorption (ECR) are common orthodontic complications with prognostic impact. Early, accurate detection helps prevent irreversible damage.
OBJECTIVES: Our objective is to synthesize diagnostic accuracy of artificial intelligence (AI) for ERR/ECR on orthodontic imaging and compare performance by imaging modality and model architecture.
METHODS: Data Sources: Web of Science, PubMed, Scopus, Embase, IEEE Xplore, and Cochrane Library (through September 9, 2025).
STUDY SELECTION: Original human or ex-vivo imaging studies of AI models for ERR/ECR with extractable accuracy data.
DATA EXTRACTION AND SYNTHESIS: Two reviewers independently extracted TP/FP/TN/FN. Pooled sensitivity/specificity and HSROC were estimated via a bivariate random-effects model; risk of bias with QUADAS-2; PROSPERO: CRD420251103690.
RESULTS: Five studies (1089 images) met criteria. Pooled sensitivity was 90.7% (95% CI, 85.6-94.1) and specificity 91.8% (95% CI, 86.0-95.3). CBCT-based models outperformed panoramic models, and transformer/hybrid architectures showed slightly higher accuracy than CNNs, though subgroup power was limited. Heterogeneity was moderate (I² ≈ 55-61%), plausibly related to variable diagnostic thresholds, mixed tooth types, and differing reference standards (orthodontists/endodontists/radiologists).
LIMITATIONS: Small evidence base (n = 5), small geographically limited datasets, and absence of true external validation restrict generalizability. With <10 studies, formal publication-bias testing was not feasible.
CONCLUSIONS AND RELEVANCE: AI shows high accuracy for ERR/ECR, particularly with CBCT and transformer/hybrid models, yet moderate heterogeneity and limited generalizability warrant cautious interpretation. AI should augment, not replace, clinician judgment within explainable, standardized workflows. Priorities include open multicenter annotated datasets, harmonized thresholds/protocols, and external validation to enable reliable clinical adoption.
OBJECTIVES: Our objective is to synthesize diagnostic accuracy of artificial intelligence (AI) for ERR/ECR on orthodontic imaging and compare performance by imaging modality and model architecture.
METHODS: Data Sources: Web of Science, PubMed, Scopus, Embase, IEEE Xplore, and Cochrane Library (through September 9, 2025).
STUDY SELECTION: Original human or ex-vivo imaging studies of AI models for ERR/ECR with extractable accuracy data.
DATA EXTRACTION AND SYNTHESIS: Two reviewers independently extracted TP/FP/TN/FN. Pooled sensitivity/specificity and HSROC were estimated via a bivariate random-effects model; risk of bias with QUADAS-2; PROSPERO: CRD420251103690.
RESULTS: Five studies (1089 images) met criteria. Pooled sensitivity was 90.7% (95% CI, 85.6-94.1) and specificity 91.8% (95% CI, 86.0-95.3). CBCT-based models outperformed panoramic models, and transformer/hybrid architectures showed slightly higher accuracy than CNNs, though subgroup power was limited. Heterogeneity was moderate (I² ≈ 55-61%), plausibly related to variable diagnostic thresholds, mixed tooth types, and differing reference standards (orthodontists/endodontists/radiologists).
LIMITATIONS: Small evidence base (n = 5), small geographically limited datasets, and absence of true external validation restrict generalizability. With <10 studies, formal publication-bias testing was not feasible.
CONCLUSIONS AND RELEVANCE: AI shows high accuracy for ERR/ECR, particularly with CBCT and transformer/hybrid models, yet moderate heterogeneity and limited generalizability warrant cautious interpretation. AI should augment, not replace, clinician judgment within explainable, standardized workflows. Priorities include open multicenter annotated datasets, harmonized thresholds/protocols, and external validation to enable reliable clinical adoption.