{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T12:16:09Z","timestamp":1778328969845,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Polish Medical Research Agency (ABM)","award":["2022\/ABM\/02\/00004."],"award-info":[{"award-number":["2022\/ABM\/02\/00004."]}]},{"name":"Medical University of Warsaw","award":["2022\/ABM\/02\/00004."],"award-info":[{"award-number":["2022\/ABM\/02\/00004."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Developmental dysplasia of the hip (DDH), if not treated, can lead to osteoarthritis and disability. Ultrasound (US) is a primary screening method for the detection of DDH, but its interpretation remains highly operator-dependent. We propose a supervised machine learning (ML) image segmentation model for the automated recognition of anatomical structures in hip US images. Methods: We conducted a retrospective observational analysis based on a dataset of 10,767 hip US images from 311 patients. All images were annotated for eight key structures according to the Graf method and split into training (75.0%), validation (9.5%), and test (15.5%) sets. Model performance was assessed using the Intersection over Union (IoU) and Dice Similarity Coefficient (DSC). Results: The best-performing model was based on the SegNeXt architecture with an MSCAN_L backbone. The model achieved high segmentation accuracy (IoU; DSC) for chondro-osseous border (0.632; 0.774), femoral head (0.916; 0.956), labrum (0.625; 0.769), cartilaginous (0.672; 0.804), and bony roof (0.725; 0.841). The average Euclidean distance for point-based landmarks (bony rim and lower limb) was 4.8 and 4.5 pixels, respectively, and the baseline deflection angle was 1.7 degrees. Conclusions: This ML-based approach demonstrates promising accuracy and may enhance the reliability and accessibility of US-based DDH screening. Future applications could integrate real-time angle measurement and automated classification to support clinical decision-making.<\/jats:p>","DOI":"10.3390\/jcm14176332","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T08:43:54Z","timestamp":1757493834000},"page":"6332","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence Algorithm Supporting the Diagnosis of Developmental Dysplasia of the Hip: Automated Ultrasound Image Segmentation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4953-0075","authenticated-orcid":false,"given":"\u0141ukasz","family":"Pulik","sequence":"first","affiliation":[{"name":"Department of Orthopedics and Traumatology, Medical University of Warsaw, Lindley 4 Str., 02-005 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pawe\u0142","family":"Czech","sequence":"additional","affiliation":[{"name":"Pentacomp Systemy Informatyczne S.A., Aleje Jerozolimskie 179 Str., 02-222 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jadwiga","family":"Kaliszewska","sequence":"additional","affiliation":[{"name":"Gustav\u2014Children\u2019s Clinic, Bia\u0142ej Floty 2 Str., 02-654 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bart\u0142omiej","family":"Mulewicz","sequence":"additional","affiliation":[{"name":"Pentacomp Systemy Informatyczne S.A., Aleje Jerozolimskie 179 Str., 02-222 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maciej","family":"Pykosz","sequence":"additional","affiliation":[{"name":"Pentacomp Systemy Informatyczne S.A., Aleje Jerozolimskie 179 Str., 02-222 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joanna","family":"Wiszniewska","sequence":"additional","affiliation":[{"name":"Pentacomp Systemy Informatyczne S.A., Aleje Jerozolimskie 179 Str., 02-222 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9799-5750","authenticated-orcid":false,"given":"Pawe\u0142","family":"\u0141\u0119gosz","sequence":"additional","affiliation":[{"name":"Department of Orthopedics and Traumatology, Medical University of Warsaw, Lindley 4 Str., 02-005 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pulik, \u0141., P\u0142oszka, K., Romaniuk, K., Sibilska, A., Jedynak, A., To\u0142wi\u0144ski, I., Kumi\u0119ga, P., Wojty\u0144ski, P., and \u0141\u0119gosz, P. 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