Abstract Introduction The optimal timing of dentofacial orthopedic and growth-modifying orthodontic interventions depends on accurate assessment of skeletal maturation, particularly the pubertal growth spurt (PGS). This systematic review and meta-analysis evaluated the performance of artificial intelligence (AI) models for identifying the PGS from cephalometric and hand–wrist radiographs and compared their diagnostic performance across imaging modalities and algorithm types. Methods A PRISMA 2020–guided systematic review and meta-analysis was conducted and prospectively registered in PROSPERO (CRD42024594040). PubMed, Embase, Web of Science, and LILACS were searched through December 2025. Eligible studies included individuals aged ≤ 21 years in whom AI was applied to cephalometric and/or hand–wrist radiographs to predict the PGS or classify validated skeletal maturation stages associated with it. Two reviewers independently screened studies, extracted data, and assessed methodological quality using QUADAS-AI. Confusion matrices were extracted or reconstructed, skeletal maturation stages were harmonized into a three-class scheme (pre-spurt, spurt, and post-spurt), and the best-performing model from each study was synthesized using random-effects, bivariate, and hierarchical summary receiver operating characteristic (HSROC) models. Results Twenty-one studies published between 2020 and 2025 were included in the quantitative synthesis. The pooled accuracy was 0.83 (95% confidence interval, 0.76–0.89), with substantial between-study heterogeneity. HSROC analysis including 20 studies showed good overall discriminative ability, with summary sensitivity and specificity estimates of 0.76 and 0.88, respectively; complementary bivariate pooling yielded estimates of 0.86 and 0.93, respectively. In subgroup analyses, hand–wrist-based models showed higher sensitivity, whereas cephalometric models showed higher specificity; however, substantial heterogeneity was observed. Deep learning and multimodal approaches generally showed more favorable performance than traditional machine learning models, whereas large language model–based approaches showed more uncertain performance. Conclusions AI models may have potential for identifying the PGS from cephalometric and hand–wrist radiographs and may support the timing of growth-modifying orthodontic interventions. However, substantial heterogeneity, limited external validation, and methodological weaknesses reduce the certainty and generalizability of current evidence. Multicenter studies are needed before routine clinical implementation.
Chaves et al. (Wed,) studied this question.