{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T07:11:01Z","timestamp":1792480261478,"version":"4.5.0"},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:00:00Z","timestamp":1755907200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:00:00Z","timestamp":1755907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004769","name":"Universit\u00e0 degli Studi di Pavia","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004769","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>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>We evaluated the diagnostic performance of two AI software programs (BoneView and RBfracture) in assisting non-specialist radiologists (NSRs) in detecting scaphoid fractures using conventional wrist radiographs (X-rays).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed 724 radiographs from 264 patients with wrist trauma. Patients were classified into two groups: Group 1 included cases with a definitive diagnosis by a specialist radiologist (SR) based on X-rays (either scaphoid fracture or not), while Group 2 comprised indeterminate cases for the SRs requiring a CT scan for a final diagnosis. Indeterminate cases were defined as negative or doubtful X-rays in patients with persistent clinical symptoms. The X-rays were evaluated by AI and two NSRs, independently and in combination. We compared their diagnostic performances using sensitivity, specificity, area under the curve (AUC), and Cohen\u2019s kappa for diagnostic agreement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Group 1 included 174 patients, with 80 cases (45.97%) of scaphoid fractures. Group 2 had 90 patients, of which 44 with uncertain diagnoses and 46 negative cases with persistent symptoms. Scaphoid fractures were identified in 51 patients (56.67%) in Group 2 after further CT imaging. In Group 1, AI performed similarly to NSRs (AUC: BoneView 0.83, RBfracture 0.84, NSR1 0.88, NSR2 0.90), without significant contribution of AI to the performance of NSRs. In Group 2, performances were lower (AUC: BoneView 0.62, RBfracture 0.65, NSR1 0.46, NSR2 0.63), but AI assistance significantly improved NSR performance (NSR2\u2009+\u2009BoneView AUC\u2009=\u20090.75,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.003; NSR2\u2009+\u2009RBfracture AUC\u2009=\u20090.72,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.030). Diagnostic agreement between NSR1 with AI support and SR was moderate (kappa\u2009=\u20090.576), and substantial for NSR2 (kappa\u2009=\u20090.712).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI tools may effectively assist NSRs, especially in complex scaphoid fracture cases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11547-025-02028-5","type":"journal-article","created":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T12:03:14Z","timestamp":1755950594000},"page":"1633-1641","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Application of artificial intelligence in the diagnosis of scaphoid fractures: impact of automated detection of scaphoid fractures in a real-life study"],"prefix":"10.1007","volume":"130","author":[{"given":"Ana Isabel","family":"Hern\u00e1iz Ferrer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chandra","family":"Bortolotto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luisa","family":"Carone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8624-6673","authenticated-orcid":false,"given":"Emma Maria","family":"Preda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Fichera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alice","family":"Lionetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulia","family":"Gambini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleonora","family":"Fresi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federico Alberto","family":"Grassi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorenzo","family":"Preda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,23]]},"reference":[{"issue":"1","key":"2028_CR1","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ocl.2012.09.003","volume":"44","author":"R Sendher","year":"2013","unstructured":"Sendher R, Ladd AL (2013) The scaphoid. Orthop Clin North Am 44(1):107\u2013120. https:\/\/doi.org\/10.1016\/j.ocl.2012.09.003. (PMID: 23174330)","journal-title":"Orthop Clin North Am"},{"issue":"2","key":"2028_CR2","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1530\/EOR-21-0108.PMID:35192505;PMCID:PMC8897568","volume":"7","author":"JK Andersson","year":"2022","unstructured":"Andersson JK, Gustafson P, Kopylov P (2022) Misdiagnosed and maltreated scaphoid fractures - costly both for society and patients: a review of filed claims to the Swedish National Patient Insurance Company 2011\u20132018. EFORT Open Rev 7(2):129\u2013136. https:\/\/doi.org\/10.1530\/EOR-21-0108.PMID:35192505;PMCID:PMC8897568","journal-title":"EFORT Open Rev"},{"issue":"7","key":"2028_CR3","doi-asserted-by":"publisher","first-page":"4341","DOI":"10.1007\/s00330-023-10473-x","volume":"34","author":"M Kraus","year":"2024","unstructured":"Kraus M, Anteby R, Konen E, Eshed I, Klang E (2024) Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis. Eur Radiol 34(7):4341\u20134351. https:\/\/doi.org\/10.1007\/s00330-023-10473-x","journal-title":"Eur Radiol"},{"key":"2028_CR4","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.hcl.2019.03.002","volume":"35","author":"MD Sabbagh","year":"2019","unstructured":"Sabbagh MD, Morsy M, Moran SL (2019) Diagnosis and management of acute scaphoid fractures. Hand Clin 35:259\u2013269","journal-title":"Hand Clin"},{"issue":"22","key":"2028_CR5","doi-asserted-by":"publisher","first-page":"1860","DOI":"10.2106\/JBJS.O.00099","volume":"97","author":"JW Karl","year":"2015","unstructured":"Karl JW, Swart E, Strauch RJ (2015) Diagnosis of occult scaphoid fractures: a cost-effectiveness analysis. J Bone Joint Surg Am 97(22):1860\u20131868. https:\/\/doi.org\/10.2106\/JBJS.O.00099. (PMID: 26582616)","journal-title":"J Bone Joint Surg Am"},{"issue":"1","key":"2028_CR6","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1148\/radiol.211785","volume":"304","author":"RYL Kuo","year":"2022","unstructured":"Kuo RYL, Harrison C, Curran TA, Jones B, Freethy A, Cussons D, Stewart M, Collins GS, Furniss D (2022) Artificial intelligence in fracture detection: a systematic review and meta-analysis. 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There are no financial relationships with the companies that developed the AI software used in this study (Gleamer and Radiobotics).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study was approved by our Institutional Review Board and carried out in accordance with the ethical standards of the Helsinki Declaration.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}