## 4 Discussion  
The present study systematically reviews recent literature on the deployment of AI-based models for automated BA assessment from left PA-HW X-rays. In recent years, supervised ML--especially CNNs--has transformed BA assessment by automating the identification of ossification patterns more quickly and accurately than manual GPA or TW methods [100, 101]. These CNN models outperform earlier automated systems like HANDX [15], which were developed according to the GPA scoring system, and the PROI/CASAS approach [16, 17] based on the TW method, delivering both higher precision and speed. Fully automated CNN-based solutions now achieve clinical-grade performance. [PERSON] and [PERSON] (2021) highlighted the efficiency and accuracy of _BoneXpert_\({}^{\circledR}\), a commer \begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{2}{c}{**Computational Technique**} & \multicolumn{1}{c}{**Datasets**} & \multicolumn{1}{c}{**Performance**} & \multicolumn{1}{c}{**References**} \\ \hline \multicolumn{2}{l}{**AI-assisted Software**} & & & \\ \hline AI-assisted software & & & \\ (_BoneXpert®_,_VIINO Med®_-BoneAge_, _BoneView®_, etc.) & & RSNA, DHA, TW3 sets & MAE: 2–4 mos. & [27, 30, 45, 53, 76, 77, 83, 89, 93, 99] \\ \hline \multicolumn{2}{l}{**Deep Learning Architectures**} & & & \\ \hline Convolutional Neural & RSNA, Digital & & [24, 25, 26, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 46, 47, 48, 55, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 75, 78, 80, 84, 85, 86, 87, 88, 90, 91, 92, 94, 96, 97] \\ \hline Transfer Learning (_InceptionV3, VGG16, ResNet50, MobileNetV2, EfficientNetV2B0,_ etc.) & RSNA & MAE: 3.85–31.8 mos. & [36, 38, 42, 44, 59, 87, 97] \\ \hline Custom DL Architectures (_AXNet, MMANet, DADPN_, etc.) & RSNA & MAE: 4–5.8 mos. & [70, 86, 94] \\ \hline Multi-domain Neural Networks & RSNA, Local & MAE: –4–5.5 mos. & [62, 71, 72] \\ \hline \multicolumn{2}{l}{**Model Integration Techniques**} & & & \\ \hline Ensemble Learning & RSNA & MAD: 3.79–4.55 mos. & [31, 38, 90, 91] \\ \hline Hybrid Models (_CNN + TW3/GPA_) & RSNA, DHA, TW3 sets & MAE: 5.52–7.08 mos. & [24, 28, 43, 73, 88] \\ \hline \multicolumn{2}{l}{**Region Processing and Enhancement**} & & & \\ \hline U-Net Segmentation & RSNA & MAE: 6–7.35 mos. & [32, 33, 46, 48, 85] \\ \hline Attention Mechanisms & RSNA, Chinese & & \\  & datasets & MAE: \(\sim\)4–6 mos. & [32, 64, 71, 81, 94] \\ \hline Region Localization & RSNA & MAE: 4.8–6.2 mos. & [47, 48, 71, 85] \\ \hline \hline \end{tabular} Comparison of AI-based models for Automated Bone Age Assessment from Posteroanterior Wrist X-rays. Abbreviations: AXNet (Attention-X Network), CNN (Convolutional Neural Network), DHA (Digital Hand Atlas), GPA (Greulich and Pyle Atlas), MAE (Mean Absolute Error), MAD (Median Absolute Deviation), RSNA (Radiological Society of North America), TW3 (Tanner-Whitehouse 3), U-Net (U-shaped Network),YOLO (You Only Look Once).  
\end{table} 
Table 3: Comparison of AI-based models for Automated Bone Age Assessment from Posteroanterior Hand and Wrist X-rays. cial system certified for clinical use [43]. Similarly, [PERSON] et al., (2017) built a CNN model that estimates BA within \(\pm\)1 year in over 90% of cases and processes each image in under 2 s [102]. In this same line, [PERSON] et al., (2017) showed CNNs generalizability across ages, sexes, and races--reporting a MAE of 0.8 years (\(\approx\)9.6 mos.) on a public dataset and releasing their code for others to build on [103]. According to [PERSON] et al., (2022) further confirmed strong agreement with expert pediatric radiologists (R\({}^{2}\) = 0.96, MD = 1.44 mos.) across diverse populations [53]. AI-based models have substantially improved automated bone age (BA) assessment from left PA hand-wrist radiographs. Reported MAEs range from 2 to 31.8 months, with most results below 6 months, meeting the clinically acceptable threshold of <0.8 years [104].