{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T14:15:58Z","timestamp":1781878558961,"version":"3.54.5"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Ministry of Science and ICT","award":["KMDF_PR_20200901_0039"],"award-info":[{"award-number":["KMDF_PR_20200901_0039"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF- 2022R1A2C2092726"],"award-info":[{"award-number":["NRF- 2022R1A2C2092726"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korea University Anam Hospital, Seoul","award":["K2209761"],"award-info":[{"award-number":["K2209761"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>We aimed to propose a deep-learning neural network model for automatically detecting five landmarks during a two-dimensional (2D) ultrasonography (US) scan to develop a standard plane for developmental dysplasia of the hip (DDH) screening.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Method<\/jats:title>\n            <jats:p>A model of global and local networks was developed to detect five landmarks for DDH screening during 2D US. Patients (<jats:italic>N<\/jats:italic>\u2009=\u2009532) who underwent hip US for DDH screening from January 2016 to December 2021 at a tertiary medical center were enrolled. All datasets were randomly split into training, validation, and test sets in a 70:10:20 ratio for the final assessment of landmark detection. The performance of this model for detecting five landmarks for guiding DDH was analyzed using the root mean square error (RMSE) and dice similarity coefficient.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The RMSE value for the five landmarks for diagnosing and classifying DDH using global and local networks was 4.023\u2009\u00b1\u20093.723. The point results using EfficientNetB2 were 1.69\u2009\u00b1\u20091.26 (first point), 3.34\u2009\u00b1\u20092.37 (second point), 2.54\u2009\u00b1\u20091.61 (third point), 5.92\u2009\u00b1\u20094.25 (fourth point), and 6.61\u2009\u00b1\u20094.82 (fifth point).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Our deep-learning network model is feasible for detecting five landmarks for DDH using ultrasound images. The primary parameters to determine DDH will be significantly detected by applying the deep-learning model in clinical settings.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12911-025-02926-8","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T16:10:56Z","timestamp":1739895056000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning-based automated guide for defining a standard imaging plane for developmental dysplasia of the hip screening using ultrasonography: a retrospective imaging analysis"],"prefix":"10.1186","volume":"25","author":[{"given":"Kyung-Sik","family":"Ahn","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji Hye","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heejou","family":"Kwon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seoyeon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongwon","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woo Young","family":"Jang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,18]]},"reference":[{"key":"2926_CR1","first-page":"804","volume":"56","author":"TG Barlow","year":"1963","unstructured":"Barlow TG. Early diagnosis and treatment of congenital dislocation of the hip. Proc R Soc Med. 1963;56:804\u20136.","journal-title":"Proc R Soc Med"},{"key":"2926_CR2","doi-asserted-by":"crossref","unstructured":"Ortolani M. Congenital hip dysplasia in the light of early and very early diagnosis. Clin Orthop Relat Res. 1976:6\u201310.","DOI":"10.1097\/00003086-197609000-00003"},{"key":"2926_CR3","doi-asserted-by":"publisher","first-page":"e23562","DOI":"10.1097\/MD.0000000000023562","volume":"99","author":"HW Jung","year":"2020","unstructured":"Jung HW, Jang WY. Effectiveness of different types of ultrasonography screening for developmental dysplasia of the hip: a meta-analysis. Med (Baltim). 2020;99:e23562.","journal-title":"Med (Baltim)"},{"key":"2926_CR4","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.ocl.2005.11.002","volume":"37","author":"M Synder","year":"2006","unstructured":"Synder M, Harcke HT, Domzalski M. 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Ultrasonographic screening for developmental dysplasia of the hip in infants. Bone Joint J. 2003;85(5):726\u201330.","journal-title":"Bone Joint J"},{"key":"2926_CR10","volume-title":"Deep learning and data labeling for medical applications","author":"D Golan","year":"2016","unstructured":"Golan D, Donner Y, Mansi C, Jaremko J, Ramachandran M. Fully automating Graf\u2019s method for DDH diagnosis using deep convolutional neural networks. In: Carneiro G, Mateus D, Peter L, Tavares JM, Belagiannis V, Papa JP, et al. editors. Deep learning and data labeling for medical applications. Cham: Springer; 2016.;130\u2013\u200941."},{"issue":"2","key":"2926_CR11","doi-asserted-by":"publisher","first-page":"e132","DOI":"10.1097\/BPO.0000000000002294","volume":"43","author":"H Atalar","year":"2003","unstructured":"Atalar H, \u00dcreten K, Tokdemir G, Tolunay T, \u00c7i\u00e7eklida\u011f M, Atik O\u015e. 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Total datasets were deidentified for preserving patient privacy. All processes were performed according to relevant regulations and guidelines.","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":"91"}}