A Novel Dual-Output Deep Learning Model Based on InceptionV3 for Radiographic Bone Age and Gender Assessment

Olivares, LAF, De León, LG, Fragoso, ML. Skeletal age prediction model from percentage of adult height in children and adolescents. Scientific Reports. 2020;10(1):15768. https://doi.org/10.1038/s41598-020-72835-5

Ferrillo, M, et al. Reliability of cervical vertebral maturation compared to hand-wrist for skeletal maturation assessment in growing subjects: A systematic review. Journal of Back and Musculoskeletal Rehabilitation. 2021;34:925-936. https://doi.org/10.3233/BMR-210003

Schmeling, A, et al. Forensic age estimation: methods, certainty, and the law. Dtsch Arztebl International. 2016;113(4):44-50. https://doi.org/10.3238/arztebl.2016.0044

Greulich, WW, Pyle, SI. Radiographic atlas of skeletal development of the hand and wrist. Stanford University Press; 1959.

Pinchi, V, et al. Skeletal age estimation for forensic purposes: A comparison of GP, TW2 and TW3 methods on an Italian sample. Forensic Science International. 2014;238:83-90. https://doi.org/10.1016/j.forsciint.2014.02.030

Fishman, LS. Radiographic evaluation of skeletal maturation: a clinically oriented method based on hand-wrist films. The Angle Orthodontist. 1982;52(2):88-112.

Baccetti, T, Franchi, L, McNamara, JA. The cervical vertebral maturation (CVM) method for the assessment of optimal treatment timing in dentofacial orthopedics. Seminars in Orthodontics. 2005;11(3):119-129. https://doi.org/10.1053/j.sodo.2005.04.005

McNamara, JA, Franchi, L. The cervical vertebral maturation method: A user’s guide. The Angle Orthodontist. 2018;88(2):133-143.

Sella Tunis, T, Masarwa, M, Finkelstein, T, et al. The reliability of a modified three-stage cervical vertebrae maturation method for estimating skeletal growth in males and females. BMC Oral Health. 2024;24:1255. https://doi.org/10.1186/s12903-024-05028-5

Schoretsaniti, L, Mitsea, A, Karayianni, K, Sifakakis, I. Cervical vertebral maturation method: Reproducibility and efficiency of chronological age estimation. Applied Sciences. 2021; 11(7):3160. https://doi.org/10.3390/app11073160

Szemraj, A, Wojtaszek-Słomińska, A, Racka-Pilszak, B. Is the cervical vertebral maturation (CVM) method effective enough to replace the hand-wrist maturation (HWM) method in determining skeletal maturation?—A systematic review. European Journal of Radiology. 2018;102:125-128. https://doi.org/10.1016/j.ejrad.2018.03.012

Ibrahim, RSM, Shaker, CW, Mira, MF, et al. Clinical, laboratory and radiological assessment of skeletal maturation in children and adolescents with obesity. Egypt Pediatric Association Gaz 2020;68:13. https://doi.org/10.1186/s43054-020-00024-0

Khade, D.M., Bhad, W.A., Chavan, S.J., Muley, A., Shekokar, S. Reliability of salivary biomarkers as skeletal maturity indicators: A systematic review. International Orthodontics. 2023;21(1). https://doi.org/10.1016/j.ortho.2022.100716

Turing, A.M. Can a machine think. The World of Mathematics. 1956;4:2099–2123.

Orhan, K., Amasya, H. Artificial intelligence from medicine to dentistry. In: Orhan, K., Jagtap, R., editors. Artificial Intelligence in Dentistry. Springer International Publishing; 2023. pp. 33–42. https://doi.org/10.1007/978-3-031-43827-1_3

Wang, S., Summers, R.M. Machine learning and radiology. Medical Image Analysis. 2012;16(5):933–951. https://doi.org/10.1016/j.media.2012.02.005

Tajmir, S.H., et al. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiology. 2019;48(2):275–283. https://doi.org/10.1007/s00256-018-3033-2

Tanner, J.M., Gibbons, R.D. Automatic bone age measurement using computerized image analysis. 1994;7(2):141–146.

Halabi, S.S., et al. The RSNA pediatric bone age machine learning challenge. Radiology. 2019;290(2):498–503. https://doi.org/10.1148/radiol.2018180736

Pan, I., et al. Improving automated pediatric bone age estimation using ensembles of models from the 2017 RSNA machine learning challenge. Radiology: Artificial Intelligence. 2019;1(6):e190053. https://doi.org/10.1148/ryai.2019190053

Larson, D.B., et al. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2018;287(1):313–322. https://doi.org/10.1148/radiol.2017170236

Zhang, H. Snake image recognition based on InceptionV3 model. Electronic Technology and Software Engineering. 2019;10:58–61.

Zhao, J.D., Bai, Z.M., Chen, H.B. Research on road traffic sign recognition based on video image. In: 10th International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE; 2017. pp. 110–113.

Li, J., et al. Transfer learning of pre-trained Inception-v3 model for colorectal cancer lymph node metastasis classification. In: 2018 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE; 2018. pp. 1650–1654.

Mednikov, Y., et al. Transfer representation learning using Inception-V3 for the detection of masses in mammography. In: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2018. pp. 2587–2590.

Abadi, M., et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016. https://doi.org/10.48550/arXiv.1603.04467

Iparraguirre-Villanueva O., Orozco-Arias S., Muñoz W., et al. Convolutional neural networks with transfer learning for pneumonia detection. International Journal of Advanced Computer Science and Applications. 2022;13(9):544–551. https://doi.org/10.14569/IJACSA.2022.0130963

Idress, W.M., Zhao, Y., Abouda, K.A., Ahmed, A.A., Hassan, W., Abdalla, O., & Elhindi, T. Enhanced onychomycosis diagnosis using a dynamic weighted ensemble classifier: Integrating light GBM and transfer learning with a game theory approach. 15th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2025;1209–1216.

Lee, H., Tajmir, S., Lee, J., et al. Fully automated deep learning system for bone age assessment. Journal of Digital Imaging. 2017;30:427–441. https://doi.org/10.1007/s10278-017-9955-8

Gisev, N., Bell, J.S., Chen, T.F. Interrater agreement and interrater reliability: Key concepts, approaches, and applications. Research in Social and Administrative Pharmacy. 2013;9(3):330–338. https://doi.org/10.1016/j.sapharm.2012.04.004

Cavlak, N., Cınarer, G., Erkoç, M.F., et al. Sex estimation with convolutional neural networks using the patella magnetic resonance image slices. Forensic Science, Medicine and Pathology. 2025. https://doi.org/10.1007/s12024-025-00943-7

Pereira, C.P., Correia, M., Augusto, D., et al. Forensic sex classification by convolutional neural network approach by VGG16 model: accuracy, precision and sensitivity. International Journal of Legal Medicine. 2025;139:1381–1393. https://doi.org/10.1007/s00414-025-03416-2

Knecht, S., Santos, F., Ardagna, Y., et al. Sex estimation from long bones: a machine learning approach. International Journal of Legal Medicine. 2023;137:1887–1895. https://doi.org/10.1007/s00414-023-03072-4

Dallora, A.L., Anderberg, P., Kvist, O., Mendes, E., Diaz, R.S., Sanmartin, B.J. Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis. PLoS ONE. 2019;14(7):e0220242. https://doi.org/10.1371/journal.pone.0220242

Yilmaz, E., Görürgöz, C., Kış, H.C., et al. Forensic dental age estimation with deep learning: a modified Xception model for panoramic X-Ray images. Forensic Science, Medicine and Pathology. 2025. https://doi.org/10.1007/s12024-025-00962-4

Matthijs, L., Delande, L., De Tobel, J., et al. Artificial intelligence and dental age estimation: development and validation of an automated stage allocation technique on all mandibular tooth types in panoramic radiographs. International Journal of Legal Medicine. 2024;138:2469–2479. https://doi.org/10.1007/s00414-024-03298-w

Li, S., Liu, B., Li, S., et al. A deep learning-based computer-aided diagnosis method of X-ray images for bone age assessment. Complex & Intelligent Systems. 2022;8:1929–1939. https://doi.org/10.1007/s40747-021-00376-z

Lashin H.I., Sharif A.F., Ghaly M.S., et al. Bridging gaps in age estimation: a cross-sectional comparative study of skeletal maturation using Fishman method and dental development using Nolla method among Egyptians. International Journal of Legal Medicine. 2025;139:695-714. https://doi.org/10.1007/s00414-024-03394-x

Amasya H., Aydogan T., Cesur E., et al. Using artificial intelligence models to evaluate envisaged points initially: A pilot study. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine. 2023;237(6):706-718. https://doi.org/10.1177/09544119231173165

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