## 2 Left Hand and Wrist Bone Age Assessment
### AI-Based Approaches
The mean of bone age estimates from the clinical report and additional three human reviewers were applied as the reference standard. Overall model performance was assessed by comparing the root mean square error (RMSE) and MAD between the model estimates and the reference standard bone ages. The mean difference between bone age estimates of the model and the reviewers was 0 years, with a mean RMSE and MAD of 0.63 and 0.50 years, respectively. Results from the model, the radiological report, and the readers' revisions were within the 95% limits of agreement. Therefore, the proposed model was able to provide bone age estimation with accuracy similar to that of an expert radiologist. [PERSON] et al. developed a customized neural network algorithm trained on 10,289 images of different skeletal age exams (8909 from their Picture Archiving and Communication System and 1383 from the public Digital Hand Atlas Database). Images were divided into four cohorts: two for boys and girls < 10 years, and two for boys and girls 8-10 years old. The test set included left-hand radiographs taken for bone age assessment and trauma investigation and consisted of 20 X-rays of each 1-year-age cohort from 0 to 1 year to 14-15+ years, 50% of male subjects and 50% of female subjects. A 14-hidden-layer customized neural network was created. Data augmentation was applied to the network inputs [60] and a linear regression output was utilized. On the validation and test sets, MAE accuracies for young females were 0.654 and 0.561, respectively, and 0.662 and 0.497 for older females. For young males, the validation and test accuracies were 0.649 and 0.585, respectively, whereas for older males, they were 0.581 and 0.501, respectively. The customized neural network architecture reached an aggregate validation and test set MAE of 0.637 and 0.536, respectively. [PERSON] et al. tested different DL approaches in automated skeletal bone age assessment on a public dataset including different age ranges, genders, and ethnicities, and demonstrated an average discrepancy between the manual and automatic calculation of 0.8 years, considered as state-of-the-art performance [61]. [PERSON] et al. developed a fully automated DL system to detect bones and tissues, construct a hand/wrist mask, standardize and preprocessed input X-rays, and calculate bone age using X-rays of the left hand and wrist of patients aged 5-18 years and more than 18 years, including 4278 images for females and 4047 for males. In the female and male cohorts, their fine-tuned CNN achieved an accuracy of 57.32% and 61.40%, respectively [62]. To process heterogenous features, [PERSON] et al. developed a deep automated skeletal bone age assessment model based on CNNs and support vectors using the multiple kernels learning algorithm. This was not only aimed at the automated assessment of bone age from X-ray images of the hand and wrist, but also for the detection of heterogeneous information such as race and gender. The results demonstrated that the fused heterogeneous features offer a better description of the degree of bone maturation because they performed better and with higher bone age assessment accuracy [63]. [PERSON] et al. first used a dataset of 2518 left-hand radiographs and applied a fine-grained classification to obtain the region of interest through automatic object detection, then executed the bone age assessment with a model based on the TW3 method: the accuracy of bone grading was 86.93%, with a MAE of bone age of 7.68 months on the clinical dataset [64]. Another proposed approach is based on the analysis of the carpal bones. Using projections in both the horizontal and vertical axes, [PERSON] et al. [65] selected the carpal bone region and extracted the boundaries of the carpal bones. From the segmented carpal bones, they extracted five morphological features and applied them to regression using a support vector machine. [PERSON] et al.