{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T16:58:12Z","timestamp":1767027492410,"version":"3.48.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-02058-5","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T16:54:28Z","timestamp":1767027268000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing deep learning accuracy in the measurement of radiographic parameters in pediatric hip X-rays"],"prefix":"10.1186","volume":"25","author":[{"given":"Byoung-Dai","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin Young","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ki-Ryum","family":"Moon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mu Sook","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"key":"2058_CR1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13018-020-1543-9","volume":"15","author":"Y Yang","year":"2020","unstructured":"Yang Y, Porter D, Zhao L, Zhao X, Yang X, Chen S. How to judge pelvic malposition when assessing acetabular index in children? Three simple parameters can determine acceptability. J Orthop Surg Res. 2020;15:12.","journal-title":"J Orthop Surg Res"},{"key":"2058_CR2","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1007\/s11999-011-1781-6","volume":"469","author":"V der Bom Mj","year":"2011","unstructured":"der Bom Mj V, Groote ME, Vincken KL, Beek FJ, Bartels LW. Pelvic rotation and tilt can cause misinterpretation of the acetabular index measured on radiographs. Clin Orthop Relat Res. 2011;469:1743\u201349.","journal-title":"Clin Orthop Relat Res"},{"key":"2058_CR3","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1007\/s00264-023-05722-z","volume":"47","author":"GM Schwarz","year":"2023","unstructured":"Schwarz GM, Simon S, Mitterer JA, et al. Can an artificial intelligence powered software reliably assess pelvic radiograph? Int Orthop. 2023;47:945\u201353.","journal-title":"Int Orthop"},{"issue":"4","key":"2058_CR4","doi-asserted-by":"publisher","first-page":"612","DOI":"10.3348\/kjr.2020.0051","volume":"22","author":"HS Park","year":"2020","unstructured":"Park HS, Jeon K, Cho YJ, et al. Diagnostic performance of a new convolutional neural network algorithm for detecting developmental dysplasia of the hip on anteroposterior radiographs. Korean J Radiol. 2020;22(4):612\u201323.","journal-title":"Korean J Radiol"},{"key":"2058_CR5","first-page":"744","volume":"37","author":"J Chen","year":"2024","unstructured":"Chen J, Fan X, Chen Z, et al. Enhancing YOLO5 for the assessment of irregular pelvic radiographs with multimodal information. J Imag Inf Med. 2024;37:744\u201355.","journal-title":"J Imag Inf Med"},{"key":"2058_CR6","doi-asserted-by":"publisher","first-page":"025001","DOI":"10.1088\/2057-1976\/ac8ffa","volume":"9","author":"Y Pei","year":"2023","unstructured":"Pei Y, Mu L, Xu C, et al. Learning-based landmark detection in pelvis X-rays with attention mechanism: data from the osteoarthritis initiative. Biomed Phys Eng Express. 2023;9:025001.","journal-title":"Biomed Phys Eng Express"},{"key":"2058_CR7","doi-asserted-by":"publisher","first-page":"785480","DOI":"10.3389\/fped.2021.785480","volume":"9","author":"W Xu","year":"2022","unstructured":"Xu W, Shu L, Gong P, et al. A deep-learning aided diagnostic system in assessing developmental dysplasia of the hip on pediatric pelvic radiographs. Front Pediatr. 2022;9:785480.","journal-title":"Front Pediatr"},{"key":"2058_CR8","doi-asserted-by":"publisher","first-page":"1049575","DOI":"10.3389\/fped.2022.1049575","volume":"10","author":"Q Wu","year":"2023","unstructured":"Wu Q, Ma H, Sun J, et al. Application of deep-learning-based artificial intelligence in acetabular index measurement. Front Pediatr. 2023;10:1049575.","journal-title":"Front Pediatr"},{"key":"2058_CR9","unstructured":"Li Y, Li-Han Y, Tian H. Deep learning-based automatic diagnosis system for developmental dysplasia of the hip. arXiv preprint. 2022. https:\/\/arxiv.org\/abs\/2209.03440."},{"key":"2058_CR10","doi-asserted-by":"publisher","first-page":"109303","DOI":"10.1016\/j.ejrad.2020.109303","volume":"132","author":"W Yang","year":"2020","unstructured":"Yang W, Ye Q, Ming S, et al. Feasibility of automatic measurements of hip joints based on pelvic radiography and a deep learning algorithm. Eur J Radiol. 2020;132:109303.","journal-title":"Eur J Radiol"},{"key":"2058_CR11","doi-asserted-by":"publisher","first-page":"242","DOI":"10.3390\/jimaging9110242","volume":"9","author":"F Jan","year":"2023","unstructured":"Jan F, Rahman A, Busaleh R, et al. Assessing acetabular index angle in infants: a deep learning-based novel approach. J Imag. 2023;9:242.","journal-title":"J Imag"},{"key":"2058_CR12","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/s11280-022-01051-0","volume":"26","author":"J Xu","year":"2023","unstructured":"Xu J, Xie H, Tan Q, et al. Multi-task hourglass network for online automatic diagnosis of developmental dysplasia of the hip. World Wide Web. 2023;26:539\u201359.","journal-title":"World Wide Web"},{"key":"2058_CR13","first-page":"135","volume":"61","author":"JH Kim","year":"2018","unstructured":"Kim JH, Yun S, Hwang SS, Shim JO, Chae HW, Lee YJ, et al. The 2017 Korean national growth charts for children and adolescents: development, improvement, and prospects. Korean J Radiol. 2018;61:135\u201349.","journal-title":"Korean J Radiol"},{"key":"2058_CR14","doi-asserted-by":"publisher","first-page":"172","DOI":"10.2214\/AJR.20.23358","volume":"217","author":"V Sherman","year":"2021","unstructured":"Sherman V, Lalonde FD, Schlechter JA. Measuring the acetabular index: an accurate and reliable alternative method of measurement. AJR Am J Roentgenol. 2021;217:172\u201376.","journal-title":"AJR Am J Roentgenol"},{"key":"2058_CR15","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1302\/0301-620X.79B4.0790570","volume":"79","author":"FG Boniforti","year":"1997","unstructured":"Boniforti FG, Fujii G, Angliss RD, Benson MK. The reliability of measurements of pelvic radiographs in infants. J Bone Joint Surg Br. 1997;79:570\u201375.","journal-title":"J Bone Joint Surg Br"},{"key":"2058_CR16","doi-asserted-by":"publisher","first-page":"462","DOI":"10.5301\/hipint.5000374","volume":"26","author":"M Kanazawa","year":"2016","unstructured":"Kanazawa M, Nakashima Y, Arai T, et al. Quantification of pelvic tilt and rotation by width\/height ratio of obturator foramina on anteroposterior radiographs. Hip Int. 2016;26:462\u201367.","journal-title":"Hip Int"},{"key":"2058_CR17","doi-asserted-by":"publisher","first-page":"1324","DOI":"10.2214\/AJR.13.12449","volume":"203","author":"V Starr","year":"2014","unstructured":"Starr V, Ha B. Imaging update on developmental dysplasia of the hip with the role of mri. AJR Am J Roentgenol. 2014;203:1324\u201335.","journal-title":"AJR Am J Roentgenol"},{"key":"2058_CR18","unstructured":"Jocher G, Chaurasia A, Qiu J. Ultralytics yolo (version 8.0.0). (2023) Available via https:\/\/github.com\/ultralytics\/ultralytics. Accessed 29 May 2024."},{"key":"2058_CR19","unstructured":"Jocher G. YOLOv8 pose models (2023). Available via. https:\/\/github.com\/ultralytics\/ultralytics\/issues\/1915. Accessed 29 May 2024."},{"key":"2058_CR20","doi-asserted-by":"crossref","unstructured":"Sanderson E, Matuszewski BJ. FCN-transformer feature fusion for polyp segmentation. Annual Conference on Medical Image Understanding and Analysis. Cham: Springer International Publishing; 2022 892\u2013907.","DOI":"10.1007\/978-3-031-12053-4_65"},{"key":"2058_CR21","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.media.2016.02.004","volume":"31","author":"CW Wang","year":"2016","unstructured":"Wang CW, Huang CT, Lee JH, et al. A benchmark for comparison of dental radiography analysis algorithms. Med Image Anal. 2016;31:63\u201376.","journal-title":"Med Image Anal"},{"key":"2058_CR22","doi-asserted-by":"publisher","first-page":"103875","DOI":"10.1109\/ACCESS.2021.3099936","volume":"9","author":"M Kim","year":"2021","unstructured":"Kim M, Lee BD. A simple generic method for effective boundary extraction in medical image segmentation. IEEE Access. 2021;9:103875\u201384.","journal-title":"IEEE Access"},{"key":"2058_CR23","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1109\/TMI.2019.2930068","volume":"39","author":"D Karimi","year":"2020","unstructured":"Karimi D, Salcudean SE. Reducing the Hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans Med Imag. 2020;39:499\u2013513.","journal-title":"IEEE Trans Med Imag"},{"key":"2058_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BF03018603","volume":"22","author":"M Frechet","year":"1906","unstructured":"Frechet M. Sur quelques points du calcul fonctionnel. Rend Circ Mat Palermo. 1906;22:1\u201374.","journal-title":"Rend Circ Mat Palermo"},{"key":"2058_CR25","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/0010-4825(89)90036-X","volume":"19","author":"J Lee","year":"1989","unstructured":"Lee J, Koh D, Ong CN. Statistical evaluation of agreement between two methods for measuring a quantitative variable. Comput Biol Med. 1989;19:61\u201370.","journal-title":"Comput Biol Med"},{"key":"2058_CR26","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1057\/jt.2009.5","volume":"17","author":"B Ratner","year":"2009","unstructured":"Ratner B. The correlation coefficient: Its values range between +1\/\u22121, or do they? J Target Meas Anal Mark. 2009;17:139\u201342.","journal-title":"J Target Meas Anal Mark"},{"key":"2058_CR27","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/s00276-009-0478-y","volume":"31","author":"PG Carbonell","year":"2009","unstructured":"Carbonell PG, Puga D, Vincente-Franqueira J, Ortuno A. Radiographic study of the acetabulum and proximal femur between 1 and 3 years of age. Surg Radiol Anat. 2009;31:483\u201387.","journal-title":"Surg Radiol Anat"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-02058-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-02058-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-02058-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T16:54:30Z","timestamp":1767027270000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12880-025-02058-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,29]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2058"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-02058-5","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,29]]},"assertion":[{"value":"5 June 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":"29 December 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":"This retrospective study was approved by the Institutional Review Board of Keimyung University Dongsan Medical Center, waiving the need for written informed consent (DSMC-2022\u201308-007). All procedures were performed in accordance with the ethical standards of the Declaration of Helsinki. As the data used in this study were fully de-identified to protect patient confidentiality.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","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"}}],"article-number":"515"}}