In recent days, skeletal bone age assessment gained more attention from researchers for auxiliary diagnosis and prediction in medical issues. Hence, it is essential to undertake more than a few complications existing in the traditional techniques in order to enhance the age assessment efficiency. So, this research work presented an intellectual skeletal bone age estimation method with neural network learning. At first, the needed images are gathered as of the available online source. Further, from gathered images, three sets of features are extracted from Vision Transformer (ViT) features as F1, Local Weber Pattern (LWP) image as F2, and raw image as F3. Subsequently, the resultant features are fed into the Vision Transformer-based Multi-scale MobileNet (ViT-MMNet) designed for assessing a skeletal bone age. In this network, multi-scale MobileNet is considered to execute the prediction process, where the ViT-based features are given to the first convolution layer, LWP-based features are given to the second convolution layer, and finally, in third convolution layer, the original images collected from the datasets are directly given. Finally, detailed experimental validations are conducted for the designed skeletal bone age assessment framework through traditional methods to assure the efficacy of the method.
HeJJiangD. Fully automatic model based on SE-ResNet for bone age assessment. IEEE Access2021; 9: 62460–62466.
2.
HaoPYeTXieX, et al.Radiographs and texts fusion learning based deep networks for skeletal bone age assessment. Multimed Tools Appl2021; 80: 16347–16366.
3.
RassmannSKellerASkafK, et al.Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias. Pediatr Radiol2024; 54: 82–95.
4.
LeeJHKimYJKimKG. Bone age estimation using deep learning and hand X-ray images. Biomed Eng Lett2020; 10: 323–331.
5.
ReddyNERayanJCAnnapragadaAV, et al.Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists. Pediatr Radiol2020; 50: 516–523.
6.
SalimIHamzaAB. Ridge regression neural network for pediatric bone age assessment. Multimed Tools Appl2021; 80: 30461–30478.
7.
PanIBairdGLMutasaS, et al.Rethinking Greulich and Pyle: a deep learning approach to pediatric bone age assessment using pediatric trauma hand radiographs. Radiology: Artificial Intelligence2020; 2: e190198.
8.
SuLFuXHuQ. Generative adversarial network based data augmentation and gender-last training strategy with application to bone age assessment. Comput Methods Programs Biomed2021; 212: 106456.
9.
ZulkifleyMAAbdaniSRZulkifleyNH. Automated bone age assessment with image registration using hand X-ray images. Appl Sci2020; 10: 7233.
10.
LiXJiangYLiuY, et al.RAGCN: region aggregation graph convolutional network for bone age assessment from X-ray images. IEEE Trans Instrum Meas2022; 71: 1–12.
11.
PanXZhaoYChenH, et al.Fully automated bone age assessment on large-scale hand X-ray dataset. Int J Biomed Imaging2020; 2020: 8460493.
12.
KoitkaSKimMSQuM, et al.Mimicking the radiologists’ workflow: estimating pediatric hand bone age with stacked deep neural networks. Med Image Anal2020; 64: 101743.
13.
ChenCChenZJinX, et al.Attention-guided discriminative region localization and label distribution learning for bone age assessment. IEEE J Biomed Health Inform2021; 26: 1208–1218.
14.
Jasper Gnana ChandranJKarthickRRajagopalR, et al.Dual-channel capsule generative adversarial network optimized with golden eagle optimization for pediatric bone age assessment from hand X-ray image. Int J Pattern Recognit Artif Intell2023; 37: 2354001.
15.
ThodbergHHThodbergBAhlkvistJ, et al.Autonomous artificial intelligence in pediatric radiology: the use and perception of BoneXpert for bone age assessment. Pediatr Radiol2022; 52: 1338–1346.
16.
HuMWangYWangX, et al.A primary and secondary feature interactive learning network for bone age assessment. Biomed Signal Process Control2023; 85: 105083.
17.
WangSTongXFanY, et al.Combining deep learning and radiomics for automated, objective, comprehensive bone mineral density assessment from low-dose chest computed tomography. Acad Radiol2024; 31: 1180–1188.
18.
LiangBZhaiYTongC, et al.A deep automated skeletal bone age assessment model via region-based convolutional neural network. Future Gener Comput Syst2019; 98: 54–59.
19.
ZhengH-DYuLLuY-T, et al.KDALDL: knowledge distillation-based adaptive label distribution learning network for bone age assessment. IEEE Access2024; 12: 17679–17689.
20.
LiKZhangJSunY, et al.Automatic bone age assessment of adolescents based on weakly-supervised deep convolutional neural networks. IEEE Access2021; 9: 120078–120087.
21.
HanYWangG. Skeletal bone age prediction based on a deep residual network with spatial transformer. Comput Methods Programs Biomed2020; 197: 105754.
22.
ChenXLiJZhangY, et al.Automatic feature extraction in X-ray image based on deep learning approach for determination of bone age. Future Gener Comput Syst2020; 110: 795–801.
23.
DeshmukhSKhapardeA. Faster region-convolutional neural network oriented feature learning with optimal trained recurrent neural network for bone age assessment for pediatrics. Biomed Signal Process Control2022; 71: 103016.
24.
LiSLiuBLiS, et al.A deep learning-based computer-aided diagnosis method of X-ray images for bone age assessment. Complex & Intelligent Systems2022; 8: 1929–1939.
25.
GaoYZhuTXuX. Bone age assessment based on deep convolution neural network incorporated with segmentation. Int J Comput Assist Radiol Surg2020; 15: 1951–1962.
26.
ZhouLYiZZhouK, et al.Efficient and gender-adaptive graph vision mamba for pediatric bone age assessment. International conference on medical image computing and computer-assisted intervention. Cham: Springer Nature Switzerland, 2024.
27.
DengYGaoXTuT. Enhancing skeletal age estimation accuracy using support vector regression models. Leg Med2024; 66: 102362.
28.
ManzanaresABelloMNavarroF, et al.Age-at-death estimation from 3D bone models of the pubic symphysis by using a deep multi-view learning approach. Neurocomputing2025; 654: 131299.
29.
ChenC-FFanQPandaR. CrossViT: cross-attention multi-scale vision transformer for image prediction. Computer Vision and Pattern Recognition2021: 357–366.
30.
BilalHTianYUllahI, et al.M-CNN-RF: a hybrid deep learning model for accurate pediatric skeletal age estimation using hand bone radiographs. Alexandria Eng J2025; 129: 289–301.
31.
Sirati-AmshehMShabaniniaEChaparianA. AE-BoNet: a deep learning method for pediatric bone age estimation using an unsupervised Pre-trained model. J Biomed Phys Engineering2025; 15: 271–280.
32.
SharmaP. Bone age estimation with HS-optimized Resnet and Yolo for child growth disorder. Expert Syst Appl2025; 259: 125160.
33.
MaheshDBMurtyGSLakshmiDR. Optimized local weber and gradient pattern-based medical image retrieval and optimized convolutional neural network-based prediction. Biomed Signal Process Control2021; 70: 102971.
34.
YuWLvP. An End-to-End intelligent fault diagnosis application for rolling bearing based on MobileNet. IEEE Access2021; 9: 41925–41933.
