Comparison of Two Artificial Intelligence Programs for Bone Age Assessment Using Hand-Wrist Radiographs in Korean Children and Adolescents Undergoing Orthodontic Diagnosis.
Authors
Affiliations (3)
Affiliations (3)
- Department of Pediatric Dentistry, School of Dentistry, Chonnam National University, 33 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea.
- Chaeumieum Dental Clinic, Ilsan-si 10544, Republic of Korea.
- Department of Pediatric Dentistry, College of Dentistry, Wonkwang University, Iksan-si 54538, Republic of Korea.
Abstract
<b>Purpose:</b> Accurate assessment of skeletal maturation is important for timing pediatric dental and orthodontic interventions. This study compared bone age estimates generated by two AI-based programs, CRESCOM MediAI-BA and VUNO Med-BoneAge<sup>®</sup>, in Korean children and adolescents undergoing orthodontic diagnostic evaluation. <b>Methods:</b> This retrospective study compared bone-age estimates generated by CRESCOM MediAI-BA and VUNO Med-BoneAge<sup>®</sup> using left hand-wrist radiographs from 200 Korean children and adolescents (100 boys and 100 girls; 6-17 years) undergoing orthodontic diagnostic evaluation. Chronological age and program-derived bone age were compared by sex and age group using the Wilcoxon signed-rank test. Standardized differences were summarized using Cohen's d, and Spearman correlations were calculated among chronological age, program-derived bone age, and the CRESCOM skeletal maturity indicator (SMI). <b>Results:</b> The standardized differences between chronological age and CRESCOM-predicted bone age were very small in boys (d = 0.143) and small in girls (d = 0.258). Corresponding differences for VUNO Med-BoneAge<sup>®</sup> were small in boys (d = 0.254) and girls (d = 0.216). CRESCOM SMI correlated strongly with chronological age (ρ = 0.852), VUNO-predicted bone age (ρ = 0.910), and CRESCOM-predicted bone age (ρ = 0.932; all <i>p</i> < 0.001). However, statistically significant age- and sex-specific differences were observed between chronological age and AI-predicted bone age and between the two programs. <b>Conclusions:</b> CRESCOM MediAI-BA may provide clinically useful adjunctive information for skeletal-maturity assessment; nevertheless, AI-derived bone-age estimates from different programs should not be considered interchangeable and should be interpreted in conjunction with clinical findings and established skeletal-maturity indicators.