{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T12:01:51Z","timestamp":1789646511268,"version":"4.0.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["114-2637-E-035-008-"],"award-info":[{"award-number":["114-2637-E-035-008-"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["114-2221-E-035-006-"],"award-info":[{"award-number":["114-2221-E-035-006-"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-02264-9","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T18:36:29Z","timestamp":1789583789000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiographic Classification of Low-Energy Distal Radius Fracture Status Using Automated Hand Segmentation"],"prefix":"10.1007","author":[{"given":"Y. S.","family":"Gan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yung-Cheng","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai-Xing Alvin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun-Hui","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gen-Bing","family":"Liong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3867-1964","authenticated-orcid":false,"given":"Sze-Teng","family":"Liong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"2264_CR1","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1007\/BF01622200","volume":"4","author":"JA Kanis","year":"1994","unstructured":"Kanis, J.A., Kanis, J.: Assessment of fracture risk and its application to screening for post-menopausal osteoporosis: synopsis of a who report. Osteoporosis international 4, 368\u2013381 (1994)","journal-title":"Osteoporosis international"},{"key":"2264_CR2","doi-asserted-by":"crossref","unstructured":"Panel, N.C.: Osteoporosis prevention, diagnosis, and therapy. Jama 285, 785\u2013795 (2001)","DOI":"10.1001\/jama.285.6.785"},{"key":"2264_CR3","doi-asserted-by":"publisher","first-page":"2359","DOI":"10.1007\/s00198-014-2794-2","volume":"25","author":"F Cosman","year":"2014","unstructured":"Cosman, F., Beur, S.J., LeBoff, M., Lewiecki, E., Tanner, B., Randall, S., Lindsay, R.: Clinician\u2019s guide to prevention and treatment of osteoporosis. Osteoporosis international 25, 2359\u20132381 (2014)","journal-title":"Osteoporosis international"},{"key":"2264_CR4","unstructured":"Ministry of Health and Welfare. https:\/\/dep.mohw.gov.tw\/ (2024)"},{"key":"2264_CR5","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1007\/s00198-010-1329-8","volume":"22","author":"S Cadarette","year":"2011","unstructured":"Cadarette, S., Jaglal, S., Raman-Wilms, L., Beaton, D., Paterson, J.: Osteoporosis quality indicators using healthcare utilization data. Osteoporosis international 22, 1335\u20131342 (2011)","journal-title":"Osteoporosis international"},{"issue":"9321","key":"2264_CR6","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1016\/S0140-6736(02)08761-5","volume":"359","author":"JA Kanis","year":"2002","unstructured":"Kanis, J.A.: Diagnosis of osteoporosis and assessment of fracture risk. The Lancet 359(9321), 1929\u20131936 (2002)","journal-title":"The Lancet"},{"issue":"1","key":"2264_CR7","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1007\/s11657-023-01229-7","volume":"18","author":"A Singer","year":"2023","unstructured":"Singer, A., McClung, M., Tran, O., Morrow, C., Goldstein, S., Kagan, R., McDermott, M., Yehoshua, A.: Treatment rates and healthcare costs of patients with fragility fracture by site of care: A real-world data analysis. Archives of Osteoporosis 18(1), 42 (2023)","journal-title":"Archives of Osteoporosis"},{"issue":"1","key":"2264_CR8","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1080\/03009740510017986","volume":"34","author":"T Jensen","year":"2005","unstructured":"Jensen, T., Hansen, M., Jensen, K., P\u00f8denphant, J., Hansen, T., Hyldstrup, L.: Comparison of dual x-ray absorptiometry (dxa), digital x-ray radiogrammetry (dxr), and conventional radiographs in the evaluation of osteoporosis and bone erosions in patients with rheumatoid arthritis. Scandinavian journal of rheumatology 34(1), 27\u201333 (2005)","journal-title":"Scandinavian journal of rheumatology"},{"issue":"Suppl 1","key":"2264_CR9","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/s43465-023-01059-2","volume":"57","author":"G Sangondimath","year":"2023","unstructured":"Sangondimath, G., Sen, R.K.: Dexa and imaging in osteoporosis. Indian Journal of Orthopaedics 57(Suppl 1), 82\u201393 (2023)","journal-title":"Indian Journal of Orthopaedics"},{"key":"2264_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106928","volume":"99","author":"J Selvaraj","year":"2025","unstructured":"Selvaraj, J., Umapathy, S., Rajesh, N.A.: Artificial intelligence based real time colorectal cancer screening study: Polyp segmentation and classification using multi-house database. Biomedical Signal Processing and Control 99, 106928 (2025)","journal-title":"Biomedical Signal Processing and Control"},{"key":"2264_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.109708","volume":"186","author":"CJ Ejiyi","year":"2025","unstructured":"Ejiyi, C.J., Qin, Z., Agbesi, V.K., Ejiyi, M.B., Chikwendu, I.A., Bamisile, O.F., Onyekwere, F.E., Bamisile, O.O.: Atedu-net: An attention-embedded deep unet for multi-disease diagnosis in chest x-ray images, breast ultrasound, and retina fundus. Computers in Biology and Medicine 186, 109708 (2025)","journal-title":"Computers in Biology and Medicine"},{"key":"2264_CR12","first-page":"82","volume":"62","author":"S-T Liong","year":"2018","unstructured":"Liong, S.-T., See, J., Wong, K., Phan, R.C.-W.: Less is more: Micro-expression recognition from video using apex frame. Signal Processing: Image Communication 62, 82\u201392 (2018)","journal-title":"Signal Processing: Image Communication"},{"key":"2264_CR13","doi-asserted-by":"crossref","unstructured":"Gan, Y., Liu, K.-H., Liong, G.-B., Liong, S.-T.: Micro-expression recognition in wild video environments: Latent feature-based ann (lfann) from 3d reconstructed faces. Neurocomputing, 129480 (2025)","DOI":"10.1016\/j.neucom.2025.129480"},{"issue":"12","key":"2264_CR14","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1080\/0951192X.2022.2048421","volume":"35","author":"YS Gan","year":"2022","unstructured":"Gan, Y.S., Liong, S.-T., Wang, S.-Y., Cheng, C.T.: An improved automatic defect identification system on natural leather via generative adversarial network. International Journal of Computer Integrated Manufacturing 35(12), 1378\u20131394 (2022)","journal-title":"International Journal of Computer Integrated Manufacturing"},{"issue":"10\u201311","key":"2264_CR15","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1080\/0951192X.2020.1795928","volume":"33","author":"S-T Liong","year":"2020","unstructured":"Liong, S.-T., Zheng, D., Huang, Y.-C., Gan, Y.S.: Leather defect classification and segmentation using deep learning architecture. International Journal of Computer Integrated Manufacturing 33(10-11), 1105\u20131117 (2020)","journal-title":"International Journal of Computer Integrated Manufacturing"},{"key":"2264_CR16","doi-asserted-by":"crossref","unstructured":"Liong, S.-T., Wu, Y.-L., Liong, G.-B., Gan, Y.: Moving towards agriculture 4.0: An ai-aoi carrot inspection system with accurate geometric properties. Journal of Food Engineering 357, 111632 (2023)","DOI":"10.1016\/j.jfoodeng.2023.111632"},{"issue":"9","key":"2264_CR17","doi-asserted-by":"publisher","first-page":"14069","DOI":"10.1111\/jfpe.14069","volume":"45","author":"Y-S Gan","year":"2022","unstructured":"Gan, Y.-S., Luo, S.-H., Li, C.-H., Chung, S.-W., Liong, S.-T., Tan, L.-K.: An automated cucumber inspection system based on neural network. Journal of Food Process Engineering 45(9), 14069 (2022)","journal-title":"Journal of Food Process Engineering"},{"issue":"1","key":"2264_CR18","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.jhsa.2024.09.008","volume":"50","author":"A Luan","year":"2025","unstructured":"Luan, A., Maan, Z., Lin, K.-Y., Yao, J.: Application of machine learning to osteoporosis and osteopenia screening using hand radiographs. The Journal of Hand Surgery 50(1), 43\u201350 (2025)","journal-title":"The Journal of Hand Surgery"},{"key":"2264_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.bone.2024.117317","volume":"190","author":"C-S Ho","year":"2025","unstructured":"Ho, C.-S., Fan, T.-Y., Kuo, C.-F., Yen, T.-Y., Chang, S.-Y., Pei, Y.-C., Chen, Y.-P.: Hardnet based deep learning model for osteoporosis screening and bone mineral density inference from hand radiographs. Bone 190, 117317 (2025)","journal-title":"Bone"},{"issue":"1","key":"2264_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.59461\/ijdiic.v4i1.158","volume":"4","author":"T Ramesh","year":"2025","unstructured":"Ramesh, T., Santhi, V.: Ensemble deep learning model using panoramic radiographs and clinical variables for osteoporosis disease detection. International Journal of Data Informatics and Intelligent Computing 4(1), 1\u201314 (2025)","journal-title":"International Journal of Data Informatics and Intelligent Computing"},{"key":"2264_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107031","volume":"100","author":"BSS Dias","year":"2025","unstructured":"Dias, B.S.S., Querrer, R., Figueiredo, P.T., Leite, A.F., Melo, N.S., Costa, L.R., Caetano, M.F., Farias, M.C.: Osteoporosis screening: Leveraging efficientnet with complete and cropped facial panoramic radiography imaging. Biomedical Signal Processing and Control 100, 107031 (2025)","journal-title":"Biomedical Signal Processing and Control"},{"key":"2264_CR22","unstructured":"Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M., Lee, J., Chang, W.-T., Hua, W., Georg, M., Grundmann, M.: Mediapipe: A framework for perceiving and processing reality. In: Third Workshop on Computer Vision for AR\/VR at IEEE Computer Vision and Pattern Recognition (CVPR) 2019 (2019). https:\/\/mixedreality.cs.cornell.edu\/s\/NewTitleM ay1M ediaP ipeC V PRC V 4ARV RW orkshop2019.pdf"},{"issue":"2","key":"2264_CR23","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1002\/jbmr.4477","volume":"37","author":"M Jang","year":"2020","unstructured":"Jang, M., Kim, M., Bae, S.J., Lee, S.H., Koh, J.-M., Kim, N.: Opportunistic osteoporosis screening using chest radiographs with deep learning: development and external validation with a cohort dataset. Journal of Bone and Mineral Research 37(2), 369\u2013377 (2020)","journal-title":"Journal of Bone and Mineral Research"},{"issue":"2","key":"2264_CR24","doi-asserted-by":"publisher","first-page":"392","DOI":"10.3390\/jcm9020392","volume":"9","author":"K-S Lee","year":"2020","unstructured":"Lee, K.-S., Jung, S.-K., Ryu, J.-J., Shin, S.-W., Choi, J.: Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. Journal of clinical medicine 9(2), 392 (2020)","journal-title":"Journal of clinical medicine"},{"issue":"1","key":"2264_CR25","doi-asserted-by":"publisher","first-page":"19997","DOI":"10.1038\/s41598-021-99549-6","volume":"11","author":"R Jang","year":"2021","unstructured":"Jang, R., Choi, J.H., Kim, N., Chang, J.S., Yoon, P.W., Kim, C.-H.: Prediction of osteoporosis from simple hip radiography using deep learning algorithm. Scientific reports 11(1), 19997 (2021)","journal-title":"Scientific reports"},{"key":"2264_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11657-021-00985-8","volume":"16","author":"C-S Ho","year":"2021","unstructured":"Ho, C.-S., Chen, Y.-P., Fan, T.-Y., Kuo, C.-F., Yen, T.-Y., Liu, Y.-C., Pei, Y.-C.: Application of deep learning neural network in predicting bone mineral density from plain x-ray radiography. Archives of Osteoporosis 16, 1\u201312 (2021)","journal-title":"Archives of Osteoporosis"},{"issue":"1","key":"2264_CR27","doi-asserted-by":"publisher","first-page":"5472","DOI":"10.1038\/s41467-021-25779-x","volume":"12","author":"C-I Hsieh","year":"2021","unstructured":"Hsieh, C.-I., Zheng, K., Lin, C., Mei, L., Lu, L., Li, W., Chen, F.-P., Wang, Y., Zhou, X., Wang, F., et al.: Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning. Nature communications 12(1), 5472 (2021)","journal-title":"Nature communications"},{"issue":"9","key":"2264_CR28","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.3390\/biomedicines10092323","volume":"10","author":"Y Sato","year":"2022","unstructured":"Sato, Y., Yamamoto, N., Inagaki, N., Iesaki, Y., Asamoto, T., Suzuki, T., Takahara, S.: Deep learning for bone mineral density and t-score prediction from chest x-rays: A multicenter study. Biomedicines 10(9), 2323 (2022)","journal-title":"Biomedicines"},{"key":"2264_CR29","doi-asserted-by":"publisher","first-page":"3452","DOI":"10.1016\/j.csbj.2023.06.017","volume":"21","author":"SM Ryu","year":"2023","unstructured":"Ryu, S.M., Lee, S., Jang, M., Koh, J.-M., Bae, S.J., Jegal, S.G., Shin, K., Kim, N.: Diagnosis of osteoporotic vertebral compression fractures and fracture level detection using multitask learning with u-net in lumbar spine lateral radiographs. Computational and Structural Biotechnology Journal 21, 3452\u20133458 (2023)","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"2264_CR30","doi-asserted-by":"crossref","unstructured":"Hwang, U., Lee, C.-H., Yoon, K.: Osteoporosis prediction from hand x-ray images using segmentation-for-classification and self-supervised learning. arXiv preprint arXiv:2412.05345 (2024)","DOI":"10.1038\/s41598-025-16860-2"},{"key":"2264_CR31","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"2264_CR32","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). Ieee","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2264_CR33","doi-asserted-by":"crossref","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, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2264_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104953","volume":"85","author":"T Yu","year":"2023","unstructured":"Yu, T., Wu, Q.: Hardnet-cps: colorectal polyp segmentation based on harmonic densely united network. Biomedical Signal Processing and Control 85, 104953 (2023)","journal-title":"Biomedical Signal Processing and Control"},{"key":"2264_CR35","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18, pp. 234\u2013241 (2015). Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2264_CR36","unstructured":"Hand x-ray dataset. https:\/\/universe.roboflow.com\/hf-w1rwi\/hand-xray (2022)"},{"key":"2264_CR37","unstructured":"Pham, H.H., Trung, H.N., Nguyen, H.Q.: Vindr-spinexr: A large annotated medical image dataset for spinal lesions detection and classification from radiographs. PhysioNet (2021)"},{"key":"2264_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106508","volume":"96","author":"G Amprimo","year":"2024","unstructured":"Amprimo, G., Masi, G., Pettiti, G., Olmo, G., Priano, L., Ferraris, C.: Hand tracking for clinical applications: validation of the google mediapipe hand (gmh) and the depth-enhanced gmh-d frameworks. Biomedical Signal Processing and Control 96, 106508 (2024)","journal-title":"Biomedical Signal Processing and Control"},{"key":"2264_CR39","doi-asserted-by":"publisher","first-page":"1052505","DOI":"10.3389\/fneur.2022.1052505","volume":"13","author":"F Gu","year":"2022","unstructured":"Gu, F., Fan, J., Cai, C., Wang, Z., Liu, X., Yang, J., Zhu, Q.: Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Frontiers in Neurology 13, 1052505 (2022)","journal-title":"Frontiers in Neurology"},{"key":"2264_CR40","doi-asserted-by":"crossref","unstructured":"Mahmood, N.T., Jabbar, M.S., Abdalrazak, M.: A real-time hand gesture recognition based on media-pipe and support vector machine. In: International Conference of Reliable Information and Communication Technology, pp. 263\u2013273 (2023). Springer","DOI":"10.1007\/978-3-031-59707-7_23"},{"key":"2264_CR41","doi-asserted-by":"crossref","unstructured":"Kwolek, B.: Continuous hand gesture recognition for human-robot collaborative assembly. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2000\u20132007 (2023)","DOI":"10.1109\/ICCVW60793.2023.00214"},{"key":"2264_CR42","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697\u20138710 (2018)","DOI":"10.1109\/CVPR.2018.00907"},{"key":"2264_CR43","unstructured":"Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114 (2019). PMLR"},{"key":"2264_CR44","unstructured":"Howard, A.G.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02264-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02264-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02264-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T11:02:33Z","timestamp":1789642953000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02264-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,16]]},"references-count":44,"alternative-id":["2264"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02264-9","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,16]]},"assertion":[{"value":"24 June 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study used anonymized hand radiograph images for automated osteoporosis classification. Ethical approval was obtained from the relevant institutional review board\/ethics committee. All procedures were conducted in accordance with applicable institutional and ethical guidelines.","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The need for informed consent was waived by the relevant institutional review board\/ethics committee because the study used anonymized retrospective radiographic data.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable. This manuscript does not contain identifiable personal information, images, or individual clinical details.","order":3,"name":"Ethics","label":"Consent to Publish","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}