Imaging Sci Dent.  2020 Sep;50(3):237-243. 10.5624/isd.2020.50.3.237.

Evaluation of the clinical efficacy of a TW3-based fully automated bone age assessment system using deep neural networks

• Shin NY
• Lee BD
• Kang JH
• Kim HR
• Oh DH
• Lee BI
• Kim SH
• Lee MS
• Heo MS

• KMID: 2506611
• DOI: http://doi.org/10.5624/isd.2020.50.3.237

Abstract

Purpose
The aim of this study was to evaluate the clinical efficacy of a Tanner-Whitehouse 3 (TW3)-based fully automated bone age assessment system on hand-wrist radiographs of Korean children and adolescents.
Materials and Methods
Hand-wrist radiographs of 80 subjects (40 boys and 40 girls, 7-15 years of age) were collected. The clinical efficacy was evaluated by comparing the bone ages that were determined using the system with those from the reference standard produced by 2 oral and maxillofacial radiologists. Comparisons were conducted using the paired t-test and simple regression analysis.
Results
The bone ages estimated with this bone age assessment system were not significantly different from those obtained with the reference standard (p>0.05) and satisfied the equivalence criterion of 0.6 years within the 95% confidence interval ( - 0.07 to 0.22), demonstrating excellent performance of the system. Similarly, in the comparisons of gender subgroups, no significant difference in bone age between the values produced by the system and the reference standard was observed (p>0.05 for both boys and girls). The determination coefficients obtained via regression analysis were 0.962, 0.945, and 0.952 for boys, girls, and overall, respectively (p=0.000); hence, the radiologist-determined bone ages and the system-determined bone ages were strongly correlated.
Conclusion
This TW3-based system can be effectively used for bone age assessment based on hand-wrist radiographs of Korean children and adolescents.

Keyword

Age Determination by Skeleton; Radiography; Deep Learning; Artificial Intelligence