{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T20:55:59Z","timestamp":1790974559442,"version":"4.1.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objectives<\/jats:title>\n                <jats:p>Scaphoid fractures are usually diagnosed using X-rays, a low-sensitivity modality. Artificial intelligence (AI) using Convolutional Neural Networks (CNNs) has been explored for diagnosing scaphoid fractures in X-rays. The aim of this systematic review and meta-analysis is to evaluate the use of AI for detecting scaphoid fractures on X-rays and analyze its accuracy and usefulness.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>This study followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and PRISMA-Diagnostic Test Accuracy. A literature search was conducted in the PubMed database for original articles published until July 2023. The risk of bias and applicability were evaluated using the QUADAS-2 tool. A bivariate diagnostic random-effects meta-analysis was conducted, and the results were analyzed using the Summary Receiver Operating Characteristic (SROC) curve.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Ten studies met the inclusion criteria and were all retrospective. The AI\u2019s diagnostic performance for detecting scaphoid fractures ranged from AUC 0.77 to 0.96. Seven studies were included in the meta-analysis, with a total of 3373 images. The meta-analysis pooled sensitivity and specificity were 0.80 and 0.89, respectively. The meta-analysis overall AUC was 0.88. The QUADAS-2 tool found high risk of bias and concerns about applicability in 9 out of 10 studies.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The current results of AI\u2019s diagnostic performance for detecting scaphoid fractures in X-rays show promise. The results show high overall sensitivity and specificity and a high SROC result. Further research is needed to compare AI\u2019s diagnostic performance to human diagnostic performance in a clinical setting.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Clinical relevance statement<\/jats:title>\n                <jats:p>Scaphoid fractures are prone to be missed secondary to assessment with a low sensitivity modality and a high occult fracture rate. AI systems can be beneficial for clinicians and radiologists to facilitate early diagnosis, and avoid missed injuries.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p><jats:italic>\u2022 Scaphoid fractures are common and some can be easily missed in X-rays.<\/jats:italic><\/jats:p>\n                <jats:p><jats:italic>\u2022 Artificial intelligence (AI) systems demonstrate high diagnostic performance for the diagnosis of scaphoid fractures in X-rays.<\/jats:italic><\/jats:p>\n                <jats:p><jats:italic>\u2022 AI systems can be beneficial in diagnosing both obvious and occult scaphoid fractures.<\/jats:italic><\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-023-10473-x","type":"journal-article","created":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T02:02:20Z","timestamp":1702605740000},"page":"4341-4351","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0789-2828","authenticated-orcid":false,"given":"Matan","family":"Kraus","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roi","family":"Anteby","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eli","family":"Konen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iris","family":"Eshed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eyal","family":"Klang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"10473_CR1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/1865-1380-4-4","volume":"4","author":"SJ Rhemrev","year":"2011","unstructured":"Rhemrev SJ, Ootes D, Beeres FJ et al (2011) Current methods of diagnosis and treatment of scaphoid fractures. 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