## 3 Results
### Main Results
#### 3.5.1 AI-Assisted Softwares for BA Assessment Through Postero-Anterior Hand and Wrist Radiograph
More recently, [PERSON] et al., (2022) [46] introduced a fully automated pipeline--combining preprocessing, unsupervised segmentations and gender-aware modeling--and demonstrated MAEs of 0.52 years (\(\approx\)6.2 mos.) on the RSNA dataset and 0.43 years (\(\approx\)5.1 mos.) on an independent cohort. Commercial systems have shown similar accuracy: _Physis\({}^{\circledR}\)_ (16-bit AI\({}^{\text{TM}}\)) [57] achieved a MAE of 0.57 years (\(\approx\)6.8 mos.) in internal validation and 0.58 years (\(\approx\)6.9 mos.) in external data, albeit with noted biases across sex, age and Tanner stage. In architecture comparisons, [PERSON] et al., (2023) [97] showed that their stacked MobileNetV2 achieved an MAE of 0.32 years (\(\approx\)3.85 months) on the RSNA test set--outperforming Inception V3's MAE of 0.33 years (\(\approx\)3.99 mos.) --thereby underscoring MobileNetV2's robustness for this task. Advanced segmentation strategies also contribute: [PERSON] et al., (2020) [34] embedded U-Net within an active-learning loop and reported MAEs between 0.58 and 0.61 years (\(\approx\)6.96 to 7.35 mos. ), varying by gender. Finally, [PERSON] et al., (2023) [93] evaluated the Gleamer _BoneVieur\({}^{\circledR}\)_ system and found it outperformed general radiologists, yielding an MAE of 0.49 years (\(\approx\)5.9 mos. ), and higher sensitivity (72.1% vs. 57.4%) in detecting pathological cases. Taken together, these studies highlight CNNs' capacity to deliver MAEs as low as 0.23 years (\(\approx\)2.75 mos.) and generally below 0.59 years (\(\approx\)7.08 mos. ), making them highly promising for fast, accurate BA assessment in pediatric radiology. Transfer Learning  
Transfer learning--by fine-tuning models pre-trained in large, general datasets--has proven especially valuable for BA assessment from left PA-HW X-rays, where labeled pediatric images are often scarce. [PERSON] et al., (2019) [25] leveraged a pre-trained regression CNN on carpal bone features, reporting 90.15% of predictions within six months and 99.43% within 1 yr. of true BA, despite a relatively small training set. [PERSON] et al., (2019) [26] combined multi-scale data fusion in combination with transfer learning, to reduce MAE by over 0.1 years (\(\approx\)1.2 mos.) versus classical approaches, underscoring the method's robustness on limited, domain-specific data. Building on these foundations, [PERSON] et al., (2021) [44] fine-tuned InceptionV3 and VGG16 on the 12,611 radiographs RSNA Pediatric Bone Age dataset, applying ROI masking and gamma correction; InceptionV3 model achieved an MAE of 0.495 years (\(\approx\)5.94 mos. ), while cutting computational time by 30% relative to VGG16. [PERSON] et al., (2021) [42] similarly used InceptionV3 with enhanced preprocessing and reached an MAE of 0.493 years (\(\approx\)5.92 mos. ), confirming the efficiency gains of transfer learning coupled with optimized image handling. More recent comparisons highlight architecture-specific advantages: [PERSON] et al., (2023) [59] found VGG19 to outperform ResNet 50 on pediatric X-rays--1.625 years (\(\approx\)19.5 mos.) versus 2.65 years (\(\approx\)31.8 mos.) MAE, in turn--while [PERSON] et al., (2023) [97] demonstrated that lightweight models such as MobileNetV2 and EfficientNetV2B0, can, via transfer learning, reach MAEs as low as 0.32 years (\(\approx\)3.85 mos. ), on the RSNA dataset. To sum up, these studies confirm that transfer learning not only boosts prediction accuracy (often cutting MAE to under 0.50 years) but also accelerates inference--making it a highly practical strategy for pediatric BA assessment in resource-constrained clinical settings. 5.3 Model Integration Techniques for BA Assessment Through Postero-Anterior Hand and Wrist Radiograph  
* Ensemble Learning  
Ensemble strategies--combining multiple models--routinely outperform single-network approaches in bone-age estimation from left PA-HW radiographs.