فهرست نشریات علمی دسترسی آزاد ایران

Background: Deep learning methods used for bone age assessment (BAA) mostly employ the whole hand or regional convolutional neural networks without carpal bones; therefore, their application is insufficient in young children. Objectives: This study aimed to improve the accuracy of BAA in young children by integrating a carpal bone analysis and to achieve a similar BAA accuracy for all age groups. Results: The M1 model showed a higher accuracy with a lower MAD (0.366; 95% confidence interval [CI]: 0.337 - 0.395) compared to the M2 model (0.388; 95% CI: 0.358 - 0.418) for all age groups, with a significant difference (P < 0.001). The RMSE values versus the reference standard were 0.483 and 0.505 years for the M1 and M2 models, respectively. According to sex and developmental stage distributions, the M1 model had a greater predictive ability compared to the M2 model for pre-pubertal patients, regardless of sex (P = 0.008 for males and P = 0.022 for females). Conclusion: Based on the present findings, the integration of a carpal bone analysis into the BAA model improved its accuracy, especially in young children.