{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T05:12:01Z","timestamp":1788671521429,"version":"build-2803163510"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e4tsklinikum Leipzig"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pediatr Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Certain paediatric fractures carry a high medicolegal risk if overlooked. While artificial intelligence (AI)-based diagnostic tools have demonstrated promising results in general fracture detection, their accuracy in identifying rare but high-risk paediatric injuries remains insufficiently studied.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To assess the diagnostic performance of a commercial AI-based software (SmartUrgence, Milvue, Paris, France) in detecting a predefined set of medicolegally relevant paediatric fractures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>This retrospective, single-centre study analysed radiographs from 125 paediatric patients (ages 2 years to \u226417\u00a0years) with one of the following fracture types: lateral humeral condyle fractures, Monteggia fractures, trampoline fractures of the proximal tibia, or medial malleolar fractures. AI-generated results were compared against a reference standard established by two board-certified paediatric radiologists, using clinical, imaging, and intraoperative data. Sensitivity and specificity were calculated for each fracture type.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Sensitivity was highest for trampoline fractures of the proximal tibia (100%) and medial malleolar fractures (78%), with specificity at the knee and ankle approaching 100%. For lateral humeral condyle fractures, sensitivity reached 73%, while specificity remained high at 90%. The most significant limitation concerned Monteggia fractures: although ulnar fractures were detected with 81% sensitivity, only 2% of associated radial head dislocations were identified correctly.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>While the AI-based software demonstrated overall strong performance, its emphasis on specificity limits its utility in detecting high-risk fractures for which sensitivity is paramount. Future development should focus on enhancing sensitivity, particularly for the detection of elbow dislocations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00247-025-06456-3","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T08:49:07Z","timestamp":1762937347000},"page":"2078-2083","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Small lesion\u2013high risk: diagnostic performance of artificial intelligence in paediatric fractures with medicolegal impact"],"prefix":"10.1007","volume":"56","author":[{"given":"Johanna","family":"Pape","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver Johannes","family":"Deffaa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Zimmermann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Lacher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franz Wolfgang","family":"Hirsch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Gr\u00e4fe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,12]]},"reference":[{"key":"6456_CR1","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/s10039-012-1922-5","volume":"14","author":"L von Laer","year":"2012","unstructured":"von Laer L, Veigel B, Illian C (2012) Die Kadil\u00e4sionen. 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