Retrospective Clinical Trial to Evaluate the Effectiveness of a New Tanner-Whitehouse-Based Bone Age Assessment Algorithm Trained with a Deep Neural Network System.

Background/

Objectives

To develop an automated deep learning-based bone age prediction model using the Tanner-Whitehouse (TW3) method and evaluate its feasibility by comparing its performance with that of pediatric radiologists.

Methods

The hand and wrist radiographs of 560 Korean children and adolescents (280 female, 280 male, mean age 9.43 ± 2.92 years) were evaluated using the TW3-based model and three pediatric radiologists. Images with bony destruction, congenital anomalies, or non-diagnostic quality were excluded. A commercialized AI solution built upon the Rotated Single Shot MultiBox Detector (SSD) and EfficientNet-B0 was used. Bone age measurements from the model and radiologists were compared using the paired t-tests. Linear regression analysis was performed and the coefficient of determination (r²), mean absolute error (MAE), and root mean square error (RMSE) were measured. A Bland-Altman analysis was conducted and the proportion of bone age predictions within 0.6 years of the radiologists' assessments was calculated.

Results

The TW3-based model demonstrated no significant differences between bone age measurements and radiologists, except for participants <6 and >13 years old (overall, p = 0.874; 6-8 years, p = 0.737; 8-9 years, p = 0.093; 9-10 years, p = 0.301; 10-11 years, p = 0.584; 11-13 years, p = 0.976; <6 or >13 years, p < 0.001). There was a strong linear correlation between the model prediction and radiologist assessments (r2 = 0.977). The RMSE and MAE values of the model were 0.529 (95% CI, 0.482-0.575) and 0.388 (95% CI, 0.361-0.417) years. Overall, 82.3% of bone age model predictions were within 0.6 years of the radiologists' interpretation.

Conclusions

Automated deep learning-based bone age assessment has the potential to reduce radiologists' workload and provide standardized measurements for clinical decision making.