{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T14:15:28Z","timestamp":1781878528667,"version":"3.54.5"},"reference-count":0,"publisher":"SRUMB - Romanian Society for Ultrasonography in Medicine and Biology","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Med Ultrason"],"abstract":"<jats:p>Aims: A computer-aided diagnosis (CAD) system for automated evaluation of developmental dysplasia of the hip (DDH) via ultrasound, integrating Deep Learning (DL) for anatomical segmentation and performing \u03b1&amp;\u03b2 angle calculations utilizing the Graf Method is presented. A custom image processing method excludes the inferior ilium\u2019s curvature during the baseline definition, enhancing accuracy and replicating radiologists\u2019 real-world workflow.Materials and methods: Our dataset comprised 452 raw images from 370 newborns. For {\u2018validation\u2019+\u201ctest\u201d}, {\u2018nv=91\u2019+\u201cnte=45\u201d}\u2261136 images were reserved (never augmented). Remaining 316 images were augmented to ntr=632 with (0%\u219425%) random brightness manipulation for training. Totally (632+136)=768 images were annotated and split with the following true numbers and percentage: {\u2018train\u2019,\u201cvalidation\u201d,test}\u2261{\u2018632\u2019,\u201c91\u201d,45}\u2261{\u201882%\u2019,\u201c12%\u201d,6%}. U-Net, MaskR-CNN, YOLOv8 and YOLOv11 were used for segmentation. \u03b1&amp;\u03b2 were measured using Method-I (centroid\/orientation) and Method-II (Hough transform). An extended set of performance metrics\u2014Precision, Recall, IoU, Dice, mAP\u2014was calculated. Bland-Altman and Intraclass Correlation Coefficient (ICC) analyses compared CAD outputs with expert measurements.Results: YOLOv11 showed the best segmentation performance (Precision:0.990, Recall:0.993, IoU:0.983, Dice:0.990, mAP:0.991). {ICC\u03b1, ICC\u03b2} calculated using Method-I and Method-II were {0.895, 0.907} and {0.929, 0.952}, respectively, with Method-II outperforming Method-I.Conclusion: A clinically-aligned-CAD-system that integrates anatomical segmentation and \u03b1&amp;\u03b2 measurement\u2014a combination rarely addressed in literature is introduced. By providing a comprehensive and standardized set of metrics, this work overcomes a common bottleneck in DL studies, namely heterogeneity in metric reporting, enabling better cross-study comparisons. Following curvature exclusion, obtained ICCs outperformed previous studies, demonstrating improved inter-rater reliability and strong agreement with expert radiologists, offering both technical robustness and clinical applicability in DDH assessment.<\/jats:p>","DOI":"10.11152\/mu-4535","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T05:29:30Z","timestamp":1753766970000},"page":"16","source":"Crossref","is-referenced-by-count":1,"title":["Computer-aided diagnosis of DDH using ultrasound: deep learning for segmentation and accurate angle measurement aligned with radiologist\u2019s clinical workflow"],"prefix":"10.11152","volume":"28","author":[{"given":"Muhammed Enes","family":"Yilmaz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evrim","family":"Colak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet Serdar","family":"Guzel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4840","published-online":{"date-parts":[[2026,3,18]]},"container-title":["Medical Ultrasonography"],"original-title":[],"link":[{"URL":"https:\/\/www.medultrason.ro\/medultrason\/index.php\/medultrason\/article\/viewFile\/4535\/2559","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.medultrason.ro\/medultrason\/index.php\/medultrason\/article\/viewFile\/4535\/2559","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T17:39:34Z","timestamp":1773855574000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medultrason.ro\/medultrason\/index.php\/medultrason\/article\/view\/4535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,3,18]]}},"URL":"https:\/\/doi.org\/10.11152\/mu-4535","relation":{},"ISSN":["2066-8643","1844-4172"],"issn-type":[{"value":"2066-8643","type":"electronic"},{"value":"1844-4172","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,18]]}}}