{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T09:01:03Z","timestamp":1794214863291,"version":"4.8.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Institute for Information & Communications Technology Planning & Evaluation","award":["IITP-2024-2020-0-01461"],"award-info":[{"award-number":["IITP-2024-2020-0-01461"]}]},{"name":"Institute for Information & Communications Technology Planning & Evaluation","award":["IITP-2024-2020-0-01461"],"award-info":[{"award-number":["IITP-2024-2020-0-01461"]}]},{"name":"Institute for Information & Communications Technology Planning & Evaluation","award":["IITP-2024-2020-0-01461"],"award-info":[{"award-number":["IITP-2024-2020-0-01461"]}]},{"name":"Ministry of Health & Welfare, Republic of Korea","award":["HR22C1734"],"award-info":[{"award-number":["HR22C1734"]}]},{"name":"Ministry of Health & Welfare, Republic of Korea","award":["HR22C1734"],"award-info":[{"award-number":["HR22C1734"]}]},{"name":"Korean Government Ministry of Science and ICT","award":["RS-2023-00253964"],"award-info":[{"award-number":["RS-2023-00253964"]}]},{"name":"Korean Government Ministry of Science and ICT","award":["RS-2023-00253964"],"award-info":[{"award-number":["RS-2023-00253964"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-77550-z","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T23:33:15Z","timestamp":1730763195000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Convolutional neural network-based classification of craniosynostosis and suture lines from multi-view cranial X-rays"],"prefix":"10.1038","volume":"14","author":[{"given":"Seung Min","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji Seung","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Woong","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyung Il","family":"Koo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tae Hoon","family":"Roh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soo Han","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"key":"77550_CR1","doi-asserted-by":"publisher","first-page":"14427","DOI":"10.1007\/s00521-022-07310-5","volume":"34","author":"L You","year":"2022","unstructured":"You, L. et al. A novel sagittal craniosynostosis classification system based on multi-view learning algorithm. Neural Comput. Appl. 34, 14427\u201314434 (2022).","journal-title":"Neural Comput. Appl."},{"key":"77550_CR2","doi-asserted-by":"publisher","first-page":"3117","DOI":"10.1109\/TMI.2023.3278493","volume":"42","author":"J Liu","year":"2023","unstructured":"Liu, J. et al. Joint cranial bone labeling and landmark detection in pediatric CT images using context encoding. IEEE Trans. Med. Imaging. 42, 3117\u20133126 (2023).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"77550_CR3","first-page":"2261","volume":"2017","author":"G Huang","year":"2017","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. IEEE Conf. Comput. Vis. Pattern Recognit. CVPR. 2017, 2261\u20132269 (2017).","journal-title":"IEEE Conf. Comput. Vis. Pattern Recognit. CVPR"},{"key":"77550_CR4","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1097\/01.prs.0000279477.56044.55","volume":"120","author":"JA Fearon","year":"2007","unstructured":"Fearon, J. A., Singh, D. J., Beals, S. P. & Yu, J. C. The diagnosis and treatment of single-sutural synostoses: are computed tomographic scans necessary? Plast. Reconstr. Surg. 120, 1327\u20131331 (2007).","journal-title":"Plast. Reconstr. Surg."},{"key":"77550_CR5","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.1038\/bjc.2014.103","volume":"110","author":"WY Huang","year":"2014","unstructured":"Huang, W. Y. et al. Paediatric head CT scan and subsequent risk of malignancy and benign brain tumour: a nation-wide population-based cohort study. Br. J. Cancer. 110, 2354\u20132360 (2014).","journal-title":"Br. J. Cancer"},{"key":"77550_CR6","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1093\/jnci\/djy104","volume":"111","author":"JM Meulepas","year":"2019","unstructured":"Meulepas, J. M. et al. Radiation exposure from pediatric CT scans and subsequent cancer risk in the Netherlands. J. Natl. Cancer Inst. 111, 256\u2013263 (2019).","journal-title":"J. Natl. Cancer Inst."},{"key":"77550_CR7","doi-asserted-by":"publisher","first-page":"289","DOI":"10.2214\/ajr.176.2.1760289","volume":"176","author":"DJ Brenner","year":"2001","unstructured":"Brenner, D. J., Elliston, C. D., Hall, E. J. & Berdon, W. E. Estimated risks of radiation-induced fatal cancer from pediatric CT. AJR Am. J. Roentgenol. 176, 289\u2013296 (2001).","journal-title":"AJR Am. J. Roentgenol."},{"key":"77550_CR8","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1136\/bmj.328.7430.19","volume":"328","author":"P Hall","year":"2004","unstructured":"Hall, P. et al. Effect of low doses of ionising radiation in infancy on cognitive function in adulthood: Swedish population based cohort study. BMJ. 328, 19 (2004).","journal-title":"BMJ"},{"key":"77550_CR9","unstructured":"Schaufelberger, M. et al. A Statistical Shape Model for Radiation-Free Assessment and Classification of Craniosynostosis\u00a0(2022)."},{"key":"77550_CR10","doi-asserted-by":"publisher","first-page":"3156","DOI":"10.1109\/TBME.2023.3278030","volume":"70","author":"M Schaufelberger","year":"2023","unstructured":"Schaufelberger, M. et al. 3D-2D Distance maps conversion enhances classification of craniosynostosis. IEEE Trans. Biomed. Eng. 70, 3156\u20133165 (2023).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"77550_CR11","doi-asserted-by":"publisher","first-page":"15346","DOI":"10.1038\/s41598-020-72143-y","volume":"10","author":"G de Jong","year":"2020","unstructured":"de Jong, G. et al. Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis. Sci. Rep. 10, 15346 (2020).","journal-title":"Sci. Rep."},{"key":"77550_CR12","first-page":"446","volume":"44th Annu. Int.","author":"M Schaufelberger","year":"2022","unstructured":"Schaufelberger, M. et al. CNN-based classification of craniosynostosis using 2D distance maps. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 44th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC 2022, 446\u2013449 (2022).","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"77550_CR13","unstructured":"Mashouri, P. et al. 3D photography based neural network craniosynostosis triaging system in Proc. Mach. Learn. Health NeurIPS Workshop, PMLR 226\u2013237. (2020)."},{"key":"77550_CR14","doi-asserted-by":"publisher","first-page":"E9","DOI":"10.3171\/2023.3.FOCUS2349","volume":"54","author":"MG Anderson","year":"2023","unstructured":"Anderson, M. G. et al. Incorporation of a biparietal narrowing metric to improve the ability of machine learning models to detect sagittal craniosynostosis with 2D photographs. Neurosurg. Focus. 54, E9 (2023).","journal-title":"Neurosurg. Focus"},{"key":"77550_CR15","first-page":"100292","volume":"8","author":"M Sabeti","year":"2022","unstructured":"Sabeti, M., Boostani, R., Moradi, E. & Shakoor, M. H. Machine learning-based identification of craniosynostosis in newborns. Mach. Learn. Appl. 8, 100292 (2022).","journal-title":"Mach. Learn. Appl."},{"key":"77550_CR16","doi-asserted-by":"publisher","first-page":"495","DOI":"10.3390\/diagnostics10070495","volume":"10","author":"CA Callejas Pastor","year":"2020","unstructured":"Callejas Pastor, C. A. et al. Two-dimensional image-based screening tool for infants with positional cranial deformities: a machine learning approach. Diagnostics (Basel). 10, 495 (2020).","journal-title":"Diagnostics (Basel)"},{"key":"77550_CR17","doi-asserted-by":"publisher","first-page":"101887","DOI":"10.1016\/j.inat.2023.101887","volume":"36","author":"M Sabeti","year":"2024","unstructured":"Sabeti, M., Boostani, R., Taheri, B. & Moradi, E. Image processing and machine learning for diagnosis and screening of craniosynostosis in children. Interdiscip Neurosurg. 36, 101887 (2024).","journal-title":"Interdiscip Neurosurg."},{"key":"77550_CR18","first-page":"100074","volume":"6","author":"M Sabeti","year":"2022","unstructured":"Sabeti, M., Boostani, R., Shakoor, M. H., Moradi, E. & Mohammadi, H. R. An efficient image segmentation scheme for determination of cranial index in scaphocephalic patients. Intell. -Based Med. 6, 100074 (2022).","journal-title":"Intell. -Based Med."},{"key":"77550_CR19","doi-asserted-by":"publisher","first-page":"581","DOI":"10.3171\/2020.9.PEDS20605","volume":"27","author":"MJ Bookland","year":"2021","unstructured":"Bookland, M. J., Ahn, E. S., Stoltz, P. & Martin, J. E. Image processing and machine learning for telehealth craniosynostosis screening in newborns. J. Neurosurg. Pediatr. 27, 581\u2013588 (2021).","journal-title":"J. Neurosurg. Pediatr."},{"key":"77550_CR20","doi-asserted-by":"crossref","unstructured":"Hr\u017ei\u0107, F. et al. Estimation of missing parameters for DICOM to 8-bit x-ray image export Int. Conf. Electr. Comput. Commun. Mechatron. Eng. ICECCME, 2022 1\u20136. (2022).","DOI":"10.1109\/ICECCME55909.2022.9988674"},{"key":"77550_CR21","unstructured":"Redmon, J. & Farhadi, A. YOLOv3: an Incremental Improvement, (2018)."},{"key":"77550_CR22","doi-asserted-by":"publisher","first-page":"106705","DOI":"10.1016\/j.cmpb.2022.106705","volume":"220","author":"KD Kim","year":"2022","unstructured":"Kim, K. D. et al. Enhancing deep learning based classifiers with inpainting anatomical side markers (L\/R markers) for multi-center trials. Comput. Methods Programs Biomed. 220, 106705 (2022).","journal-title":"Comput. Methods Programs Biomed."},{"key":"77550_CR23","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/10867651.2004.10487596","volume":"9","author":"A Telea","year":"2004","unstructured":"Telea, A. An image inpainting technique based on the fast marching method. J. Graph Tool. 9, 23\u201334 (2004).","journal-title":"J. Graph Tool."},{"key":"77550_CR24","unstructured":"Gedraite, E. S. & Hadad, M. Investigation on the effect of a gaussian blur in image filtering and segmentation in Proc. ELMAR-2011 393\u2013396. (2011)."},{"key":"77550_CR25","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/S0734-189X(87)80186-X","volume":"39","author":"SM Pizer","year":"1987","unstructured":"Pizer, S. M. et al. Adaptive histogram equalization and its variations. Comput. Vis. Graph Image Process. 39, 355\u2013368 (1987).","journal-title":"Comput. Vis. Graph Image Process."},{"key":"77550_CR26","doi-asserted-by":"publisher","first-page":"110170","DOI":"10.1016\/j.chaos.2020.110170","volume":"140","author":"S Hassantabar","year":"2020","unstructured":"Hassantabar, S., Ahmadi, M. & Sharifi, A. Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches. Chaos Solitons Fract. 140, 110170 (2020).","journal-title":"Chaos Solitons Fract."},{"key":"77550_CR27","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.3390\/biology10111174","volume":"10","author":"S Akter","year":"2021","unstructured":"Akter, S., Shamrat, F. M. J. M., Chakraborty, S., Karim, A. & Azam COVID-19 detection using deep learning algorithm on chest x-ray images. Biology. 10, 1174 (2021).","journal-title":"Biology"},{"key":"77550_CR28","doi-asserted-by":"publisher","first-page":"106833","DOI":"10.1016\/j.cmpb.2022.106833","volume":"221","author":"D Avola","year":"2022","unstructured":"Avola, D. et al. Study on transfer learning capabilities for pneumonia classification in chest-x-rays images. Comput. Methods Programs Biomed. 221, 106833 (2022).","journal-title":"Comput. Methods Programs Biomed."},{"key":"77550_CR29","doi-asserted-by":"crossref","unstructured":"Chandola, Y., Virmani, J., Bhadauria, H. S. & Kumar, P. Chapter 10. Comparative analysis of computer-aided classification systems designed for chest radiographs: conclusion and future scope in Deep Learn. Chest Radiogr (ed. Chandola, Y., Virmani, J., Bhadauria, H. S. & Kumar, P.) 205\u2013209 (Academic, 2021).","DOI":"10.1016\/B978-0-323-90184-0.00004-7"},{"key":"77550_CR30","doi-asserted-by":"publisher","first-page":"1984","DOI":"10.1109\/TIFS.2016.2569061","volume":"11","author":"M Haghighat","year":"2016","unstructured":"Haghighat, M., Abdel-Mottaleb, M. & Alhalabi, W. Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition. IEEE Trans. Inf. Forensics Secur. 11, 1984\u20131996 (2016).","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"77550_CR31","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (Curran Associates, Inc., 2012)."},{"key":"77550_CR32","unstructured":"Simonyan, K. & Zisserman, A. (eds), San Diego CA USA. Very deep convolutional networks for large-scale image recognition in Conf. Track Proc. 3rd Int. Conf. Learn. Represent. ICLR, 2015 (ed. Bengio, Y. & LeCun, Y.). (2015)."},{"key":"77550_CR33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition IEEE Conf. Comput. Vis. Pattern Recognit. CVPR, 2016 770\u2013778. (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"77550_CR34","unstructured":"Tan, M. & Le, Q. EfficientNet: rethinking model scaling for convolutional neural networks in Proc. 36th Int. Conf. Mach. Learn., PMLR 6105\u20136114. (2019)."},{"key":"77550_CR35","doi-asserted-by":"publisher","first-page":"177","DOI":"10.3171\/jns.1997.86.2.0177","volume":"86","author":"PD Chumas","year":"1997","unstructured":"Chumas, P. D., Cinalli, G., Arnaud, E., Marchac, D. & Renier, D. Classification of previously unclassified cases of craniosynostosis. J. Neurosurg. 86, 177\u2013181 (1997).","journal-title":"J. Neurosurg."},{"key":"77550_CR36","doi-asserted-by":"crossref","unstructured":"Trench, L., Bracken, S., McInerney, N., Caird, J. & Murray, D. J. OC7 nonsyndromic craniosynostosis: referral patterns for metopic, coronal and lambdoid craniosynostosis in Ireland; 2009\u20132018, a 10-year review. Arch. Dis. Child. 104, (2019). A3.2\u2013A3A3.","DOI":"10.1136\/archdischild-2019-epa.7"},{"key":"77550_CR37","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","volume":"85","author":"J Read","year":"2011","unstructured":"Read, J., Pfahringer, B., Holmes, G. & Frank, E. Classifier chains for multi-label classification. Mach. Learn. 85, 333\u2013359 (2011).","journal-title":"Mach. Learn."},{"key":"77550_CR38","unstructured":"Kingma, D. P. San Diego CA USA. Ba J. Adam: a method for stochastic optimization in Conf. Track Proc., San Diego, CA, USA. 3rd Int. Conf. Learn. Represent. ICLR, 2015 (ed. Bengio, Y. & LeCun, Y.). (2015)."},{"key":"77550_CR39","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1214\/aoms\/1177730491","volume":"18","author":"HB Mann","year":"1947","unstructured":"Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Statist. 18, 50\u201360 (1947).","journal-title":"Ann. Math. Statist"},{"key":"77550_CR40","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1080\/14786440009463897","volume":"50","author":"KX Pearson","year":"1900","unstructured":"Pearson, K. X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Lond. Edinb. Dublin Philos. Mag J. Sci. 50, 157\u2013175 (1900).","journal-title":"Lond. Edinb. Dublin Philos. Mag J. Sci."},{"key":"77550_CR41","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.procs.2019.12.112","volume":"163","author":"N Salem","year":"2019","unstructured":"Salem, N., Malik, H. & Shams, A. Medical image enhancement based on histogram algorithms. Procedia Comput. Sci. 163, 300\u2013311 (2019).","journal-title":"Procedia Comput. Sci."},{"key":"77550_CR42","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1002\/acm2.12172","volume":"18","author":"J Qiu","year":"2017","unstructured":"Qiu, J., Li, H., Zhang, H., Ma, T., Yang, D. & F. & Automatic x-ray image contrast enhancement based on parameter auto-optimization. J. Appl. Clin. Med. Phys. 18, 218\u2013223 (2017).","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"77550_CR43","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. R. et al. Grad-CAM: visual explanations from deep networks via gradient-based localization IEEE Int. Conf. Comput. Vis. ICCV, 2017 618\u2013626. (2017).","DOI":"10.1109\/ICCV.2017.74"},{"key":"77550_CR44","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1097\/SCS.0000000000002016","volume":"26","author":"IMJ Mathijssen","year":"2015","unstructured":"Mathijssen, I. M. J. Guideline for care of patients with the diagnoses of craniosynostosis: working group on craniosynostosis. J. Craniofac. Surg. 26, 1735\u20131807 (2015).","journal-title":"J. Craniofac. Surg."},{"key":"77550_CR45","doi-asserted-by":"crossref","unstructured":"Kyung, D., Jo, K., Choo, J., Lee, J. & Choi, E. Perspective projection-based 3d CT reconstruction from biplanar x-rays. ICASSP IEEE Int. Conf. Acoust. Speech Signal. Process. ICASSP, 2023\u201320231. (2023).","DOI":"10.1109\/ICASSP49357.2023.10096296"},{"key":"77550_CR46","doi-asserted-by":"crossref","unstructured":"Ying, X. et al. X2CT-GAN: reconstructing CT from biplanar x-rays with generative adversarial networks IEEECVF Conf. Comput. Vis. Pattern Recognit. CVPR, 2019 10611\u201310620. (2019).","DOI":"10.1109\/CVPR.2019.01087"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77550-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77550-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77550-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T03:06:17Z","timestamp":1731035177000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77550-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,5]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["77550"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-77550-z","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,5]]},"assertion":[{"value":"14 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"26729"}}