## 3 Results
### Main Results  
#### 3.5.1 AI-Assisted Softwares for BA Assessment Through Postero-Anterior Hand and Wrist Radiograph  
Several AI-based systems have been developed and commercially certified for clinical bone age assessment, most notably _BoneXpert(r)_, which debuted in 2009. Fully automated and requiring no expert oversight, _BoneXpert(r)_ has proven its utility in multiple studies: [PERSON] et al., (2022) [53] reported a strong concordance with experienced pediatric radiologists (R\({}^{2}\) = 0.96), and [PERSON] et al., (2024) [77] showed excellent inter-method reliability (ICC = 0.984) against manual readings. However, _VUINO Med(r)-BoneAge_--a clinically approved, AI-driven bone age estimator seamlessly integrated into PACS workflows--slightly outperformed _BoneXpert(r)_ in prepubescent girls. Similarly, [PERSON] et al., (2024) [83] reported robust correlations (r > 0.80) between _BoneXpert(r)_ and the GPA, highlighting the tool's capacity to reduce variability in BA evaluations. In addition, [PERSON] et al., (2024) [99] identified _BoneXpert(r)_ as the system with the lowest RMSE among commercial tools, underscoring its stability and reproducibility. In terms of accuracy, several studies have emphasized _BoneXpert(r)_'s capacity to deliver BA estimations closely aligned with established reference standards. [PERSON] et al., (2020) [27] observed superior accuracy compared to traditional GPA assessments, with _BoneXpert(r)_ achieving a MAD of 0.34 years (\(\approx\)4.08 mos.) versus 0.79 years (\(\approx\)9.48 mos.) manually. [PERSON] et al., (2024) [95] further identified _BoneXpert(r)_ as one of the most accurate AI-based systems, with a MAE as low as 0.17 years (\(\approx\)2.04 mos.) in fully automated hand-and-wrist assessments. Subsequently, [PERSON] et al., (2022) [53] reported a minimal MD of only 0.12 years (\(\approx\)1.44 mos.) between _BoneXpert(r)_ and manual scoring. Complementing these findings, [PERSON] et al., (2020b) [30] evaluated _BoneXpert(r)_ (v2.5.4.1) in Taiwanese children, comparing GPA and TW3 methods. Although they noted systematic discrepancies between them, the authors confirmed the tool's capacity for accurate assessment and called for population-specific calibrations. Beyond its precision and accuracy, _BoneXpert(r)_ also offers significant benefits in workflow efficiency. In this regard, [PERSON] et al., (2020) [27] reported a substantial 87% reduction in radiographic reading time when compared to manual GPA scoring, thereby streamlining clinical practice. Similarly, [PERSON] and [PERSON] (2021) [43] observed a decrease in evaluation time from 54.29 to 35.37 s per image when radiologists used _BoneXpert(r)_-based AI, highlighting its potential to accelerate diagnostic processes without compromising reliability. Collectively, these findings position _BoneXpert(r)_ not only as a precise and accurate solution, but also as a time-efficient tool for routine pediatric BA assessments. 5.2 Deep Learning Architectures for BA Assessment Through Postero-Anterior Hand and Wrist Radiograph  
* Convolutional Neural Networks (CNNs)  
CNNs have shown substantial advancements in BA estimation, consistently delivering high precision and low error rates. For instance, [PERSON] et al., (2019) [24] applied a two stage approach--Faster R-CNN for region-of-interest (ROI) detection followed by InceptionV4 for classification and regression--on 1375 left PA-HW X-rays (ages 0-18 years) from the DHA, achieving a MAE of 0.59 years (\(\approx\)7.08 mos.). In the same way, [PERSON] et al., (2019) [25] focused exclusively on carpal-bone regression in a cohort of 432 pediatric images (ages 0-6 years), reporting an even lower MAE of 0.23 years (\(\approx\)2.75 mos.) --a domain where traditional techniques often struggle. In addition, [PERSON] et al., (2019) [26] enhanced performance by incorporating a non-subsampled contourlet transform (NSCT) prior to CNN analysis and roughly 1400 radiographs (0-19 years), improving MAE by over 0.1 years (\(\approx\)1.2 mos.) compared withclassical methods.