## 2 Left Hand and Wrist Bone Age Assessment
### AI-Based Approaches  
Traditional methods for determining bone age have undeniable limitations due to generational and ethnic differences. Furthermore, most radiologists regard the process as time-consuming and irritating, and the result is prone to inter- and intra-observer variability (Figure 2). Automated assessment may result in a quick, objective, and reproducible age calculation. Figure 1: Tanner Whitehouse Method (TW2) with a demonstration of the region of interest that should be placed for age calculation. Figure 2: The steps of a traditional time-consuming age assessment procedure. The first developed tool was a semi-automated system--HANDX--was based on the left hand and wrist X-ray and was introduced in 1989 [23]. In 1995, [PERSON] et al. developed a neural network for the calculation of skeletal age based on measurements from hand X-rays [53]. To stimulate interest in this topic, the Radiological Society of North America (RSNA) launched the Pediatric Bone Age Machine Learning Challenge to create an algorithm or model with ML techniques to obtain an accurate assessment of skeletal age from a dataset of pediatric hand X-rays labeled by different experienced readers. They publicly recognized the team that created the algorithms with the best performances at 2017 RSNA annual meeting [54]. The datasets included 12,611 hand radiographs and a validation set of 1425 X-rays, and a separate test set of 200 images. [PERSON] et al., for example, applied transfer learning to pre-train a neural network architecture obtaining a difference between the actual and estimated age of 5.9 months [55]. [PERSON] et al. investigated potential model assembling benefits to improve the efficiency in automatic bone age estimation, including 48 submissions from the 2017 RSNA Pediatric Bone Age Machine Learning Challenge. The mean absolute deviation (MAD) and the mean pairwise model correlation were used to evaluate the performance of each potential model combination in ensembles of up to ten models. A single model expected generalization MAD was 4.55 months. The quartet that performed the best had a MAD of 3.79 months. Models in this group had an average pairwise correlation of 0.47. When the highest-ranking models based on individual scores were combined, the lowest MAD using eight models and a mean pairwise model correlation of 0.67 was 3.93 months [56]. The authors therefore supported ML contests to encourage the development of heterogeneous models whose predictions can be combined to achieve optimal performance. Other authors tested the winning DL model of the 2017 RSNA Pediatric Bone Age Challenge on an internal validation set (1425 individuals) and an external test set (1202 individuals) of pediatric hand X-rays, using images reporting as ground truth for bone age. On the external test set, the bone age model performed well, with no change in MAD (6.8 months in the validation set versus 6.9 months in the external set). Model predictions would have resulted in clinically significant errors in 194 of 1202 images (16%) in the external test group. In both the age and Tanner stage subcategories of the external test set, as well as the internal validation set (\(p\) = 0.01), the MAD was higher for girls than for boys. The authors concluded that, although clinically significant sex, age-, and sexual-maturity-based biases in DL bone age were discovered, the model generalization to an external test group was good [57]. [PERSON] et al. developed a GP-method-based DL technique to create a system for automated age assessment analyzing left-hand radiographs of 200 patients (3-17 years old) [58]. The reference was represented by the consensus of two experienced radiologists. The software showed a 69.5% concordance rate and significant correlations with the reference bone age (\(p\) < 0.001), with a reduction of image reading times of 18.0% and 40.0% for reviewers one and two, respectively. 14,036 clinical hand radiographs from two pediatric hospitals were used to train and validate a DL neural network to assess bone age [59].