AUTOMATED BONE AGE ESTIMATION USING ARTIFICIAL INTELLIGENCE - Boneage.io® - IN HEALTHY CHILDREN - Jeong Eun Moon; Yong Jin Cho (Journal of Dharma, 2024)
Bone age assessment is vital for diagnosing and managing growth disorders. Traditional methods like the Greulich & Pyle Atlas and Tanner-Whitehouse 3 (TW3) method are either quick but less accurate or detailed but labor-intensive. This study evaluates the accuracy and reproducibility of Boneage.io®, a cloud-based AI solution using the TW3 method to estimate bone age in healthy Korean children aged 6–13. A total of 1,040 radiographs were analyzed; the results showed minimal deviation between estimated bone age and chronological age, with Cohen’s D effect sizes of 0.021566 for boys and 0.026172 for girls. Boneage.io® provides reliable, real-time monthly bone age results, effectively addressing challenges of traditional methods and demonstrates high accuracy and reproducibility for clinical use.