## 3 Results
### Study Characteristics  
This systematic review included a total of 76 studies employing AI models for BA assessment, encompassing a wide range of methodological approaches. The majority of the research were retrospective cross-sectional designs [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57], primarily aimed at analyzing historical data to identify patterns and correlations. Additionally, prospective validation studies [43; 73; 88; 89] were incorporated to provide evidence regarding the clinical applicability and efficacy of the developed models. Furthermore, comparative experimental studies [46; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99] were included, which assessed the relative effectiveness of various methodologies or algorithms through direct comparison. The review also comprised longitudinal cohort studies [69], designed to evaluate the accuracy and stability of the models over time. Finally, benchmarking analyses [31; 70; 74; 94; 95], which set performance standards and enable systematic comparison of AI approaches, were included. All studies targeted pediatric cohorts evaluated by left PA-HW radiographs (\(n=318\), \(518\)) for skeletal maturity or growth-disorder assessment. The vast majority relied on the RSNA Pediatric Bone Age Challenge dataset for model development and validation [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 54; 55; 56;  
sentation and improve BA prediction accuracy [24; 25; 26; 27; 29; 88]. Moreover, for external validation and benchmarking, several investigations drew on clinical radiographic images repositories from tertiary pediatric hospitals and multinational image archives [31; 32; 33; 34; 36; 37; 38; 39; 41; 46; 92; 94]. The included studies were conducted across a diverse range of 17 countries. Notably, China [24; 25; 26; 29; 30; 32; 33; 34; 40; 46; 47; 48; 50; 51; 54; 56; 62; 63; 64; 65; 66; 68; 79; 80; 81; 85; 86; 90], South Korea [28; 43; 44; 45; 49; 74; 84; 88; 89], and the United States [31; 53; 57; 90; 92; 96] contributed extensively to the research. Additional they were gathered from a variety of other nations, including India [37; 38; 42; 59; 87; 95], Turkey [36; 39; 41; 77], Germany [27; 76; 82], South Africa [55], Pakistan [58; 61], Indonesia [91], Canada [31], Malaysia [94], Algeria [52], France [93], Brazil [66], Australia [70], Saudi Arabia [75], and Italy [83]. In these studies, a wide range of computational techniques were employed, notably AI-assisted software solutions such as _BoneXpert(r)_ (Visiana, Horsholm, Denmark; versions 1.0.3, 2.1, 2.5.4.1, 3.0.3, and 3.2.2), _VUINO Med(r)-BoneAge_ (VUNO Inc., Seoul, South Korea; versions 1.0.3 and 1.1.0), _BoneView(r)_ (Gleamer, Saint-Mande/Paris, France; version 2.3.1.1), and _IB Lab PANDA_ (IB Lab GmbH, Vienna, Austria; versions 1.06 and 1.13.21) [27; 30; 45; 53; 76; 77; 83; 89; 99]. Furthermore, deep learning (DL) architectures were also commonly utilized by the included studies, particularly convolutional neural networks (_CNNs_) [24; 25; 26; 28; 31; 32; 33; 34; 40; 41; 42; 43; 44; 45; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 78; 79; 80; 84; 88; 89; 90; 92; 94; 96; 97], along with transfer learning based on ImageNet-pretrained models such as _InceptionV3_ (Google Inc., Mountain View, CA, USA), _VGG16_ (Visual Geometry Group, University of Oxford, UK), _ResNet50_ (Microsoft, Redmond, WA, USA), _MobileNetV2_ (Google Inc., Mountain View, CA, USA), and _EfficientNetV2B0_ (Google Brain, Mountain View, CA, USA) [36; 38; 42; 44; 59; 87; 97].