{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T20:05:05Z","timestamp":1778702705174,"version":"3.51.4"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Guangdong High-level Hospital Construction Fund and Sanming Project of Medicine in Shenzhen","award":["No.SZSM202011005"],"award-info":[{"award-number":["No.SZSM202011005"]}]},{"name":"the Shenzhen Municipal Science and Technology Plan Project","award":["No.JCYJ20230807093815031"],"award-info":[{"award-number":["No.JCYJ20230807093815031"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"DOI":"10.1007\/s10916-025-02306-9","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T07:19:27Z","timestamp":1764141567000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automated Bone Age Assessment and Adult Height Prediction from Pediatric Hand Radiographs via a Cascaded Deep Learning Framework"],"prefix":"10.1007","volume":"49","author":[{"given":"Nihui","family":"Pei","sequence":"first","affiliation":[]},{"given":"Yijiang","family":"Zhuang","sequence":"additional","affiliation":[]},{"given":"Zhe","family":"Su","sequence":"additional","affiliation":[]},{"given":"Fangjing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yansong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xianglei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Huiping","family":"Su","sequence":"additional","affiliation":[]},{"given":"Hongwu","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"2306_CR1","doi-asserted-by":"publisher","unstructured":"Nelson, C.A., Sullivan, E., Engelstad, A.-M.: Annual research review: Early intervention viewed through the lens of developmental neuroscience. Journal of Child Psychology and Psychiatry 65(4), 435\u2013455 (2024) https:\/\/doi.org\/10.1111\/jcpp.13858","DOI":"10.1111\/jcpp.13858"},{"key":"2306_CR2","doi-asserted-by":"publisher","unstructured":"Cao, L., et\u00a0al: Hand skeletal features of children and adolescents with different growth statuses and periods. Quant Imaging Med Surg 14(3), 2528\u20132538 (2024) https:\/\/doi.org\/10.21037\/qims-23-26","DOI":"10.21037\/qims-23-26"},{"key":"2306_CR3","doi-asserted-by":"publisher","unstructured":"Lee, M., et\u00a0al: Retrospective clinical trial to evaluate the effectiveness of a new tanner\u2013whitehouse-based bone age assessment algorithm trained with a deep neural network system. Diagnostics 15(8), 8 (2025) https:\/\/doi.org\/10.3390\/diagnostics15080993","DOI":"10.3390\/diagnostics15080993"},{"key":"2306_CR4","doi-asserted-by":"publisher","unstructured":"Nivedita, Solanki, S.: Enhancing the accuracy of automatic bone age estimation using optimized cnn model on x-ray images. In: Khurana, M., Thakur, A., Kantha, P., Shieh, C.-S., Shukla, R.K. (eds.) Machine Learning Algorithms, pp. 329\u2013340. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-75861-4_29","DOI":"10.1007\/978-3-031-75861-4_29"},{"key":"2306_CR5","doi-asserted-by":"publisher","unstructured":"Leeuwen, K.G., et\u00a0al: Comparison of commercial ai software performance for radiograph lung nodule detection and bone age prediction. Radiology 310(1), 230981 (2024) https:\/\/doi.org\/10.1148\/radiol.230981","DOI":"10.1148\/radiol.230981"},{"key":"2306_CR6","doi-asserted-by":"publisher","unstructured":"Kim, J.K., Park, D., Chang, M.C.: Assessment of bone age based on hand radiographs using regression-based multi-modal deep learning. Life 14(6), 6 (2024) https:\/\/doi.org\/10.3390\/life14060774","DOI":"10.3390\/life14060774"},{"key":"2306_CR7","doi-asserted-by":"publisher","unstructured":"Alrawi, R.M.S., Basheer, N.M.: Pediatric radiology: An analysis of ai-powered bone age determination methods. NTU Journal of Engineering and Technology 4(1), 1 (2025) https:\/\/doi.org\/10.56286\/a82tjh48","DOI":"10.56286\/a82tjh48"},{"key":"2306_CR8","doi-asserted-by":"publisher","unstructured":"Alzubaidi, L., et\u00a0al: Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion. Artificial Intelligence in Medicine 155, 102935 (2024) https:\/\/doi.org\/10.1016\/j.artmed.2024.102935","DOI":"10.1016\/j.artmed.2024.102935"},{"key":"2306_CR9","doi-asserted-by":"publisher","unstructured":"Pape, J., Hirsch, F.W., Deffaa, O.J., DiFranco, M.D., Rosolowski, M., Gr\u00e4fe, D.: Applicability and robustness of an artificial intelligence-based assessment for greulich and pyle bone age in a german cohort. R\u00f6Fo 196, 600\u2013606 (2023) https:\/\/doi.org\/10.1055\/a-2203-2997","DOI":"10.1055\/a-2203-2997"},{"key":"2306_CR10","doi-asserted-by":"publisher","unstructured":"Mart\u00edn\u00a0P\u00e9rez, S.E., Mart\u00edn\u00a0P\u00e9rez, I.M., Molina\u00a0Su\u00e1rez, R., Vega\u00a0Gonz\u00e1lez, J.M., Garc\u00eda\u00a0Hern\u00e1ndez, A.M.: The validation of the tanner\u2013whitehouse 3 method for radiological bone assessments in a pediatric population from the canary islands. Osteology 5(1), 1 (2025) https:\/\/doi.org\/10.3390\/osteology5010006","DOI":"10.3390\/osteology5010006"},{"key":"2306_CR11","doi-asserted-by":"publisher","unstructured":"Liang, Y., et\u00a0al: Validation of an ai-powered automated x-ray bone age analyzer in chinese children and adolescents: A comparison with the tanner\u2013whitehouse 3 method. Adv Ther 41(9), 3664\u20133677 (2024) https:\/\/doi.org\/10.1007\/s12325-024-02944-4","DOI":"10.1007\/s12325-024-02944-4"},{"key":"2306_CR12","doi-asserted-by":"publisher","unstructured":"Jung, H.W., et\u00a0al: Comparison of adult height prediction using bone age and body composition for growth assessment in korean children. Sci Rep 15(1), 10581 (2025) https:\/\/doi.org\/10.1038\/s41598-025-94685-9","DOI":"10.1038\/s41598-025-94685-9"},{"key":"2306_CR13","doi-asserted-by":"publisher","unstructured":"Tanwar, V.: Transfer learning models for automated bone age prediction: Evaluating efficiency and clinical applicability. In: 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS), pp. 185\u2013190 (2024). https:\/\/doi.org\/10.1109\/ICTACS62700.2024.10840640","DOI":"10.1109\/ICTACS62700.2024.10840640"},{"key":"2306_CR14","doi-asserted-by":"publisher","unstructured":"Hamd, Z.Y., et\u00a0al: Deep learning-based automated bone age estimation for saudi patients on hand radiograph images: a retrospective study. BMC Med Imaging 24(1), 199 (2024) https:\/\/doi.org\/10.1186\/s12880-024-01378-2","DOI":"10.1186\/s12880-024-01378-2"},{"key":"2306_CR15","doi-asserted-by":"publisher","unstructured":"Lei, L., Qile, P., Guang, C., Zhipeng, L.: Consistency between artificial intelligence and expert greulich-pyle atlas method for bone age assessment. Chinese Journal of Tissue Engineering Research 28(28), 4436 (2024) https:\/\/doi.org\/10.12307\/2024.466","DOI":"10.12307\/2024.466"},{"key":"2306_CR16","doi-asserted-by":"publisher","unstructured":"Yuan, W., Fan, P., Zhang, L., Pan, W., Zhang, L.: Bone age assessment using various medical imaging techniques enhanced by artificial intelligence. Diagnostics 15(3), 3 (2025) https:\/\/doi.org\/10.3390\/diagnostics15030257","DOI":"10.3390\/diagnostics15030257"},{"key":"2306_CR17","doi-asserted-by":"publisher","unstructured":"Wang, S., et\u00a0al: A pediatric bone age assessment method for hand bone x-ray images based on dual-path network. Neural Comput and Applic 36(17), 9737\u20139752 (2024) https:\/\/doi.org\/10.1007\/s00521-023-09098-4","DOI":"10.1007\/s00521-023-09098-4"},{"key":"2306_CR18","doi-asserted-by":"publisher","unstructured":"Rassmann, S., et\u00a0al: Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias. Pediatr Radiol 54(1), 82\u201395 (2024) https:\/\/doi.org\/10.1007\/s00247-023-05789-1","DOI":"10.1007\/s00247-023-05789-1"},{"key":"2306_CR19","doi-asserted-by":"publisher","unstructured":"Xiao, J., Huang, Y., Guan, J., Ma, S., Zhang, D.: Boncc: A lightweight tw3-based bone age assessment coordinate classification model. In: 2024 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20136 (2024). https:\/\/doi.org\/10.1109\/IJCNN60899.2024.10650982","DOI":"10.1109\/IJCNN60899.2024.10650982"},{"key":"2306_CR20","doi-asserted-by":"publisher","unstructured":"Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: Yolact: Real-time instance segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9157\u20139166 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00926","DOI":"10.1109\/ICCV.2019.00926"},{"key":"2306_CR21","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"2306_CR22","doi-asserted-by":"publisher","unstructured":"Bu, J., et\u00a0al: A multi-element identification system based on deep learning for the visual field of percutaneous endoscopic spine surgery. JOIO 58(5), 587\u2013597 (2024) https:\/\/doi.org\/10.1007\/s43465-024-01134-2","DOI":"10.1007\/s43465-024-01134-2"},{"key":"2306_CR23","doi-asserted-by":"publisher","unstructured":"Wang, R., Zhou, X., Liu, Y., Liu, D., Lu, Y., Su, M.: Identification of the surface cracks of concrete based on resnet-18 depth residual network. Applied Sciences 14(8), 8 (2024) https:\/\/doi.org\/10.3390\/app14083142","DOI":"10.3390\/app14083142"},{"key":"2306_CR24","doi-asserted-by":"publisher","unstructured":"Devi, S., Thopalli, K., Dayana, R., Malarvezhi, P., Thiagarajan, J.J.: Improving object detectors by exploiting bounding boxes for augmentation design. IEEE Access 11, 108356\u2013108364 (2023) https:\/\/doi.org\/10.1109\/ACCESS.2023.3320638","DOI":"10.1109\/ACCESS.2023.3320638"},{"key":"2306_CR25","doi-asserted-by":"publisher","unstructured":"Sun, Y., Guo, X., Zhou, X., Shi, C., Yang, H., Pan, H.: Application status and research progress of imaging assessment of skeletal maturity in adolescents and children. xhyxzz 15(6), 1456\u20131462 (2024) https:\/\/doi.org\/10.12290\/xhyxzz.2024-0217","DOI":"10.12290\/xhyxzz.2024-0217"},{"key":"2306_CR26","doi-asserted-by":"publisher","unstructured":"Alzyoud, J.A.M., Rababah, E., Almuhaisen, M.H.O., Al-Qtaitat, A.I.: Bone age determination of epiphyseal fusion at knee joint and its correlation with chronological age. Medicina 60(5), 5 (2024) https:\/\/doi.org\/10.3390\/medicina60050779","DOI":"10.3390\/medicina60050779"},{"key":"2306_CR27","doi-asserted-by":"publisher","unstructured":"Gr\u00e4fe, D., Beeskow, A.B., Pf\u00e4ffle, R., Rosolowski, M., Chung, T.S., DiFranco, M.D.: Automated bone age assessment in a german pediatric cohort: agreement between an artificial intelligence software and the manual greulich and pyle method. Eur Radiol 34(7), 4407\u20134413 (2024) https:\/\/doi.org\/10.1007\/s00330-023-10543-0","DOI":"10.1007\/s00330-023-10543-0"},{"key":"2306_CR28","doi-asserted-by":"publisher","unstructured":"Suh, J., et\u00a0al: Bone age estimation and prediction of final adult height using deep learning. Yonsei Medical Journal 64(11), 679\u2013686 (2023) https:\/\/doi.org\/10.3349\/ymj.2023.0244","DOI":"10.3349\/ymj.2023.0244"},{"key":"2306_CR29","doi-asserted-by":"publisher","unstructured":"Kim, J.R., et\u00a0al: Computerized bone age estimation using deep learning based program: Evaluation of the accuracy and efficiency. American Journal of Roentgenology 209(6), 1374\u20131380 (2017) https:\/\/doi.org\/10.2214\/AJR.17.18224","DOI":"10.2214\/AJR.17.18224"},{"key":"2306_CR30","doi-asserted-by":"publisher","unstructured":"Pape, J., Rosolowski, M., Pf\u00e4ffle, R., Beeskow, A.B., Gr\u00e4fe, D.: A critical comparative study of the performance of three ai-assisted programs for bone age determination. Eur Radiol 35(3), 1190\u20131196 (2025) https:\/\/doi.org\/10.1007\/s00330-024-11169-6","DOI":"10.1007\/s00330-024-11169-6"},{"key":"2306_CR31","doi-asserted-by":"publisher","unstructured":"Han, Y., Wang, G.: Skeletal bone age prediction based on a deep residual network with spatial transformer. Computer Methods and Programs in Biomedicine 197, 105754 (2020) https:\/\/doi.org\/10.1016\/j.cmpb.2020.105754","DOI":"10.1016\/j.cmpb.2020.105754"},{"key":"2306_CR32","doi-asserted-by":"publisher","unstructured":"Gitto, S., et\u00a0al: Ai applications in musculoskeletal imaging: a narrative review. Eur Radiol Exp 8(1), 22 (2024) https:\/\/doi.org\/10.1186\/s41747-024-00422-8","DOI":"10.1186\/s41747-024-00422-8"},{"key":"2306_CR33","doi-asserted-by":"publisher","unstructured":"Athar, M.: Potentials of artificial intelligence in familial hypercholesterolemia: Advances in screening, diagnosis, and risk stratification for early intervention and treatment. International Journal of Cardiology 412, 132315 (2024) https:\/\/doi.org\/10.1016\/j.ijcard.2024.132315","DOI":"10.1016\/j.ijcard.2024.132315"},{"key":"2306_CR34","doi-asserted-by":"publisher","unstructured":"Qiao, Y., Lv, P., Hong, K., Zhao, Y., Feng, Q., Zhang, C.: Use of the ultrasound bone maturity indexes to assess whether children have reached their final height. Ultrasound in Medicine and Biology 51(5), 903\u2013908 (2025) https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2025.02.004","DOI":"10.1016\/j.ultrasmedbio.2025.02.004"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02306-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-025-02306-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02306-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T07:19:31Z","timestamp":1764141571000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-025-02306-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2306"],"URL":"https:\/\/doi.org\/10.1007\/s10916-025-02306-9","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,26]]},"assertion":[{"value":"13 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Shenzhen Children\u2019s Hospital Ethics Committee Review (Scientific Research) Opinion No. 202501702. The study utilized retrospective, de-identified, and anonymized imaging data, and the committee granted a waiver of individual informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Consent to Participate"}},{"value":"The manuscript does not contain any personally identifiable features, images, or information. All authors have read and approved the final version of this manuscript for publication in the journal.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"This study is an investigator-initiated, observational clinical study, designed as a retrospective cohort study. The study has been filed with the relevant institutional body under the Filing Number: MR-44-25-051722. The implementing institution for the project is Shenzhen Children\u2019s Hospital, and the practicing registration authority is the Health Commission of Shenzhen Municipality, Guangdong Province.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical Trial Number"}},{"value":"In this study, to ensure the fairness and transparency of the research, our research team hereby declares that there are no conflicts of interest that could affect the impartiality or objectivity of the manuscript during its preparation and submission.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Respected Editors and Readers"}},{"value":"There are no special interest relationships between any of the authors and the funding institutions that could influence the interpretation of the research results or the writing of the paper. All authors conducted their research independently, ensuring the objectivity and fairness of the study. All authors and related institutions confirm that there are no conflicts of interest regarding the publication of this paper. We guarantee that the contents of this statement are true and accurate. If any falsehood is found, we are willing to bear the corresponding responsibilities. We will strictly adhere to academic ethical standards to ensure the objectivity and reliability of the research results. Should you have any questions, please feel free to contact us. Thank you very much.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Interest Relationships"}}],"article-number":"170"}}