{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T00:34:48Z","timestamp":1751762088239,"version":"3.37.0"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100017160","name":"Beni Suef University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100017160","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Ulnar variance is a relative length difference in the wrist between the ulna and radius bones. It is a critical factor in helping to diagnose wrist disorders. The typical standard classification of length difference (ulnar variance) is divided into three major types: positive ulnar variance, negative ulnar variance, and neutral ulnar variance. Conventional or manual methods of measuring ulnar variance are long and time-consuming. With the urgent need for high efficiency and high speed, achieving more accurate diagnoses has become essential. In this paper, a deep learning-based methodology is used to automatically detect ulnar variance from radiographic images. Advanced Convolutional Neural Networks are exploited instead of traditional manual methods. Specifically, U-Net is used in the segmentation of ulna and radius bones, while DenseNets are applied to classify the type of ulnar variance. The essential contribution of this work is collecting a dataset of fully annotated wrist radiographs that are specific to this topic, which can be used as a resource to train and validate our models. Another contribution of this paper is optimizing the DenseNets model's hyperparameters to enhance its performance. Our model achieved a segmentation accuracy of 97.7% and an ulna variance classification accuracy of 92.1%. It outperformed previous deep learning-based methods in automatically segmenting the ulna and radius. This advancement not only reduces diagnosis time but also improves result reliability.<\/jats:p>","DOI":"10.1186\/s40537-025-01072-2","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T12:24:10Z","timestamp":1738844650000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Ulnar variance detection from radiographic images using deep learning"],"prefix":"10.1186","volume":"12","author":[{"given":"Sahar","family":"Nooh","sequence":"first","affiliation":[]},{"given":"Abdelrahim","family":"Koura","sequence":"additional","affiliation":[]},{"given":"Mohammed","family":"Kayed","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"issue":"1","key":"1072_CR1","first-page":"1","volume":"60","author":"L De Smet","year":"1994","unstructured":"De Smet L. Ulnar variance: facts and fiction review article. 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All ethical standards regarding patient privacy protection were adhered to, and no personally identifiable information was included in the dataset.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human ethics and consent"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"26"}}