## 2 Left Hand and Wrist Bone Age Assessment
### AI-Based Approaches
For example, in a large comprehensive study in 2010, BoneXpert was applied to American children of four ethnicities--Caucasian, African American, Hispanic, and Asian)--finding the largest deviation in Hispanic and Asian children older than 12 years who were about 1 year advanced relative to the GP standard. The automated method was validated for all ethnicities in males from 2.5 to 17 years and for females from 2 to 15 years [72]. In addition, the tool has been tested on Chinese children of middle and large Chinese cities without finding any significant differences compared to Chinese children in Los Angeles [71]. On the contrary, some differences were evident in children from Saudi Arabia, where the BoneXpert-derived GP method has been found suitable with no modification to Saudi Arabian females, while BoneXpert-derived TW3 can be applied to Saudi Arabian males [73]. One of the most important remaining challenges, therefore, is ethnic differences, with the need for more people of various ethnic and socioeconomic backgrounds to be included in future studies to increase the accuracy of AI systems to adapt to all ethnicities that compose our globalized society [74]. [PERSON] et al. tested various AI-based models, such as kernel learning and convolutional neural network algorithms, to find the best solution for combining features from X-ray images, race, and gender, discovering that heterogeneous features improve bone age estimation performance and that multi-kernel learning algorithms best suit heterogeneous data [63]. Another limitation is that AI-based tools have shown limited efficacy in cases of abnormal bone morphology; therefore, despite technological advances, radiologists are still required for bone age evaluation. This is supported by a recent survey of European radiologists, in which only 18% said they used automatic solutions for bone age assessment alone, while 60% said they always checked radiographs to rule out underlying diseases [75]. To address this limitation, some automatic software provides information about bone mineral density and cortical thickness, which indicate bone health, particularly in chronically ill children. For example, a retrospective study published in 2016 found that bone health indices provided by BoneXpert largely correlate with dual-energy X-ray absorption (DXA) or peripheral quantitative computed tomography (pQCT) measurements, suggesting that they may be a valuable screening tool [76]. The main advantages and disadvantages of systems analyzing hand X-ray for bone age calculation are listed in Table 3. \begin{table}
\begin{tabular}{c c c} \hline
**Traditional Method** & **Procedure** & **Commercially Available AI-Based Tool** \\ \hline [PERSON] and Pyle (GP) & Comparison with reference images contained in the Atlas & BoneXpert; VUNO Med-BoneAge \\ \hline [PERSON] (GR) & Comparison with reference images contained in the Digital Atlas & \\ \hline Tanner Whitehouse (TW) & Scoring the level of maturity of specific regions of interest based on the reference scale & BoneXpert; HH-boneage.io \\ \hline \end{tabular}
\end{table} 
Table 2: The table describes the most common traditional age-assessment methods and the most famous commercialized AI software applied to left-hand and wrist radiographs.