## 2 Left Hand and Wrist Bone Age Assessment
### AI-Based Approaches
proposed a similar approach with extraction from carpal bones of hand-engineered features, then used this as an input for a fuzzy logic classifier [66]. As the carpal bones are typically fully developed by ages 5 to 7 and do not allow for meaningful discrimination after that age, this method could be applied only to younger children [10]. A recent study developed three DL models from a Chinese private dataset, an American public dataset, and a dataset combining the above two datasets, with test data labeled by ten senior pediatric radiologists, with a demonstrated mean absolute deviation of 0.42 years [67]. The authors noted that the result corresponded to good accuracy for the whole group but did not indicate an accurate estimation of individual bone age, as the kappa value was 0.714 and the agreement between the system and human clinical determinations was significantly different. They invited researchers to consider possible biases related to patients' sex and age, institutions, and radiologists. The main characteristics of the above studies are listed in Table 1.  
Only a few systems have been commercialized, one of which is BoneXpert, developed in 2008, which has been progressively upgraded and extended to estimate bone maturation until 19 years (Figure 3). BoneXpert is currently used in Europe to automatically extract features such as the shape and density of hand-wrist bones, reducing reading times by 87% [70]: it automatically reconstructs the borders from X-ray images of the hand and wrist and calculates \"intrinsic\" bone ages for each of 13 bones (radius, ulna, and 11 short bones). Finally, BoneXpert converts intrinsic bone ages into TW or Greulich-Pyle GP bone ages. Images with abnormal bone morphology or extremely poor image quality are automatically rejected by the bone reconstruction method [68]. The tool has been demonstrated to be significantly more precise than the traditional application of the GP method and its performance has been improved with the new updates. For instance, version 2.1 achieved an overall root-mean-square deviation of 0.38 years versus 0.71 for the first version [68; 70]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
VUNO Med-BoneAge and HH-boneage.io are two other AI-based solutions that have been approved and commercialized in Korea by the Korea Food and Drug Administration. VUNO Med-BoneAge is a semi-automatic system that recommends the three most likely estimated bone ages to the physician, based on the GP method. HH-boneage.io, on the other hand, is a fully automatic system based on the TW3 method that predicts bone age with an MAE of 0.46 years and an RMSE of 0.62 years when compared to manual determination [17] (Table 2). [PERSON] et al. [69] investigated the positive effects of AI-based software on inter- and intra-observer variability in a recent prospective study, in which six board-certified residents  
Figure 3: Example of an X-ray analyzed with BoneXpert. Standard radiograph of left hand and wrist. BoneXpert automatically recognizes bone segments and analyzes the hand–wrist bones. The analysis results are shown in the black box on the right, where bone age is calculated through both GP and TW3 methods. evaluated 56 left-hand wrist radiographs of pre-scholar children aged 3 to 6 years twice: once with and once without the assistance of artificial intelligence. Each resident evaluated the same images in the same way after 4 weeks. The results were compared to three experts' reference bone ages using the RMSE, MAD, and accuracy within 0.5 and 1 year. The study found that AI assistance improves inter-observer agreement and intra-observer reproducibility significantly, making it a valuable learning tool for residents. As a fully automated tool has the potential to overcome variability among raters, it allows comparative studies of maturation across the world, beyond the differences among populations, because it could be applied to different populations [71].