{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T15:17:39Z","timestamp":1790090259714,"version":"4.0.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005609","name":"University of Turku","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005609","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Emerg Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Purpose<\/jats:title>\n            <jats:p>Missed fractures are the primary cause of interpretation errors in emergency radiology, and artificial intelligence has recently shown great promise in radiograph interpretation. This study compared the diagnostic performance of two AI algorithms, BoneView and RBfracture, in detecting traumatic abnormalities (fractures and dislocations) in MSK radiographs.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>AI algorithms analyzed 998 radiographs (585 normal, 413 abnormal), against the consensus of two MSK specialists. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and interobserver agreement (Cohen\u2019s Kappa) were calculated. 95% confidence intervals (CI) assessed robustness, and McNemar\u2019s tests compared sensitivity and specificity between the AI algorithms.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>BoneView demonstrated a sensitivity of 0.893 (95% CI: 0.860\u20130.920), specificity of 0.885 (95% CI: 0.857\u20130.909), PPV of 0.846, NPV of 0.922, and accuracy of 0.889. RBfracture demonstrated a sensitivity of 0.872 (95% CI: 0.836\u20130.901), specificity of 0.892 (95% CI: 0.865\u20130.915), PPV of 0.851, NPV of 0.908, and accuracy of 0.884. No statistically significant differences were found in sensitivity (<jats:italic>p<\/jats:italic>\u2009=\u20090.151) or specificity (<jats:italic>p<\/jats:italic>\u2009=\u20090.708). Kappa was 0.81 (95% CI: 0.77\u20130.84), indicating almost perfect agreement between the two AI algorithms. Performance was similar in adults and children. Both AI algorithms struggled more with subtle abnormalities, which constituted 66% and 70% of false negatives but only 20% and 18% of true positives for the two AI algorithms, respectively (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>BoneView and RBfracture exhibited high diagnostic performance and almost perfect agreement, with consistent results across adults and children, highlighting the potential of AI in emergency radiograph interpretation.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s10140-025-02353-2","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T02:17:14Z","timestamp":1749435434000},"page":"569-580","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Comparative accuracy of two commercial AI algorithms for musculoskeletal trauma detection in emergency radiographs"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5985-9985","authenticated-orcid":false,"given":"Jarno T.","family":"Huhtanen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2104-6302","authenticated-orcid":false,"given":"Mikko","family":"Nyman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0167-9639","authenticated-orcid":false,"given":"Roberto","family":"Blanco Sequeiros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9406-7676","authenticated-orcid":false,"given":"Seppo K.","family":"Koskinen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomi K.","family":"Pudas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sami","family":"Kajander","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pekka","family":"Niemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannu J.","family":"Aronen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0368-3746","authenticated-orcid":false,"given":"Jussi","family":"Hirvonen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,9]]},"reference":[{"key":"2353_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/S12873-019-0289-3","volume":"19","author":"F Hussain","year":"2019","unstructured":"Hussain F, Cooper A, Carson-Stevens A et al (2019) Diagnostic error in the emergency department: learning from national patient safety incident report analysis. 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