## 3 Results
### Main Results
#### 3.5.1 AI-Assisted Softwares for BA Assessment Through Postero-Anterior Hand and Wrist Radiograph
For example,Pan et al., (2019) [90] simply averaged the outputs of ten RSNA Challenge models, cutting the MAD to 0.316 years (\(\approx\) 3.8 months) versus 0.380 years (\(\approx\) 4.6 months). Likewise, [PERSON] et al., (2019) [91] contrasted pure DL and hybrid pipelines: a standalone VGG16 CNN reached an MAE of 1.23 years (\(\approx\)14.78 mos. ), RMSE of 1.58 years (\(\approx\)18.93 mos. ), and Symmetric Mean Absolute Percentage Error (SMAPE) of 38.92% in \(\approx\)91 min., whereas a Support Vector Regression (SVR) on CNN-extracted features delivered an MAE of 2.71 years (\(\approx\)32.49 mos. ), RMSE of 3.38 years (\(\approx\)40.58 mos. ), and SMAPE of 28.34% in \(\approx\)8 min (XGBoost in \(\approx\)9.2 min). Furthermore, [PERSON] et al., (2019) [24] combined the TW3 atlas method with a Faster R-CNN/Inception-V4 pipeline--reducing MAE to 0.59 years (\(\approx\)7.08 months) and achieving 99.8% mAP. [PERSON] et al., (2019) [25] fine-tuned a regression CNN on carpal-bone regions, placing 90.15% of predictions within 6 mos. and 99.43% within one year of true BA. Finally, [PERSON] et al., (2019) [26] introduced a NSCT as a preprocessing step before CNN inference, cutting MAE by over 0.10 years (\(\approx\)1.2 mos.) relative to traditional methods. Building on these results, [PERSON] et al., (2020) [33] merged U-Net segmentation with active-learning regression, reporting MAEs of 0.58 years for males and 0.61 years for females. [PERSON] et al., (2020) [34] benchmarked their TW3-AI ensemble against expert radiologists, achieving a root-mean-square error of 0.50 years (\(\approx\)6.0 mos.). Finally, these studies demonstrate that ensemble learning significantly enhances precision, robustness, and efficiency--making it a highly practical strategy for pediatric BA assessment. * Hybrid models  
The fusion of DL with established BA techniques has driven remarkable improvements in both accuracy and precision, providing powerful alternatives to manual procedures. For example, [PERSON] et al., (2019) [24] augmented the TW3 method with a Faster-RCNN/Inception-V4 pipeline, cutting MAE to just 0.59 years (\(\approx\)7.08 mos.) and achieving 99.8% mAP--an enhancement that both denoised the input data and streamlined inference. Likewise, [PERSON] et al., (2019) [25] tailored a regression CNN-model to carpal-bone features, reaching 90.15% of estimates within 6 mos. and a MAE of only 0.23 years (\(\approx\)2.75 mos. ), a help for assessing preschool children where traditional methods often struggle [13]. Beyond precision gains, hybrid frameworks also accelerate throughput. For instance, [PERSON] et al., (2020) [27] showed that AI systems like _BoneXpert(r)_ reduce reading times by 87% while maintaining expert-level accuracy. Crucially, these hybrid solutions can be applied across populations. [PERSON] et al., (2023) [57] validated a 16-Bit AI model on both internal and external cohorts--yielding MADs of 0.57 years (\(\approx\)6.8 mos. ), in that order--though they cautioned that sex, age, and Tanner stage can introduce subtle biases. Nevertheless, automated TW3-based systems such as [PERSON] et al. 's [88] achieved an MAE of 0.46 years (\(\approx\)5.52 mos. ), outperforming GPA-based approaches and underscoring the consistency of hybrid models. Finally, field-specific adaptations further highlight their versatility: [PERSON] et al., (2020) [29] applied a hybrid AI model to Tibetan and Han cohorts, matching expert readings with a 0.56 years (\(\approx\)6.72 mos.) MAE, while [PERSON] et al., (2021) [35], noted greater variability among younger age groups--reminding us that ongoing refinement is needed to mitigate demographic biases. More details in Table 3.  
In conclusion, these studies confirm that integrating CNN with traditional BA methods not only elevates precision but also accelerates workflows and broadens applicability--laying a robust foundation for next-generation pediatric radiology.