{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T03:23:09Z","timestamp":1791516189369,"version":"4.3.4"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100012940","name":"Universit\u00e4tsklinikum T\u00fcbingen","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100012940","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Radiol med"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Not diagnosed or mistreated scapholunate ligament (SL) tears represent a frequent cause of degenerative wrist arthritis. A newly developed deep learning (DL)-based automated assessment of the SL distance on radiographs may support clinicians in initial image interpretation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and Methods<\/jats:title>\n                <jats:p>A pre-trained DL algorithm was specifically fine-tuned on static and dynamic dorsopalmar wrist radiography (training data set <jats:italic>n<\/jats:italic>\u2009=\u2009201) for the automated assessment of the SL distance. Afterwards the DL algorithm was evaluated (evaluation data set <jats:italic>n<\/jats:italic>\u2009=\u2009364 patients with <jats:italic>n<\/jats:italic>\u2009=\u20091604 radiographs) and correlated with results of an experienced human reader and with arthroscopic findings.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The evaluation data set comprised arthroscopically diagnosed SL insufficiency according to Geissler\u2019s stages 0\u20134 (56.5%, 2.5%, 5.5%, 7.5%, 28.0%). Diagnostic accuracy of the DL algorithm on dorsopalmar radiography regarding SL integrity was close to that of the human reader (e.g. differentiation of Geissler\u2019s stages\u2009\u2264\u20092 versus\u2009&gt;\u20092 with a sensitivity of 74% and a specificity of 78% compared to 77% and 80%) with a correlation coefficient of 0.81 (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.01).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>A DL algorithm like this might become a valuable tool supporting clinicians\u2019 initial decision making on radiography regarding SL integrity and consequential triage for further patient management.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11547-023-01720-8","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T03:27:18Z","timestamp":1695180438000},"page":"1535-1541","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Evaluation of a newly designed deep learning-based algorithm for automated assessment of scapholunate distance in wrist radiography as a surrogate parameter for scapholunate ligament rupture and the correlation with arthroscopy"],"prefix":"10.1007","volume":"128","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6340-8343","authenticated-orcid":false,"given":"Gabriel","family":"Keller","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katarzyna","family":"Rachunek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabian","family":"Springer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathias","family":"Kraus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"1720_CR1","doi-asserted-by":"publisher","first-page":"S36","DOI":"10.1016\/j.metabol.2017.01.011","volume":"69S","author":"P Hamet","year":"2017","unstructured":"Hamet P, Tremblay J (2017) Artificial intelligence in medicine. 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