{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:31:08Z","timestamp":1792794668257,"version":"4.5.0"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"2","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"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Fractures in children are common in emergency care, and accurate diagnosis is crucial to avoid complications affecting skeletal development. Limited access to pediatric radiology specialists emphasizes the potential of artificial intelligence (AI)-based diagnostic tools. This study evaluates the performance of the AI software BoneView\u00ae for detecting fractures of the upper extremity in children aged 2\u201318 years.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>A retrospective analysis was conducted using radiographic data from 826 pediatric patients presenting to the university\u2019s pediatric emergency department. Independent assessments by two experienced pediatric radiologists served as reference standard. The diagnostic accuracy of the AI tool compared to the reference standard was evaluated and performance parameters, e.g., sensitivity, specificity, positive and negative predictive values were calculated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The AI tool achieved an overall sensitivity of 89% and specificity of 91% for detecting fractures of the upper extremities. Significantly poorer performance compared to the reference standard was observed for the shoulder, elbow, hand, and fingers, while no significant difference was found for the wrist, clavicle, upper arm, and forearm. The software performed best for wrist fractures (sensitivity: 96%; specificity: 94%) and worst for elbow fractures (sensitivity: 87%; specificity: 65%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The software assessed provides diagnostic support in pediatric emergency radiology. While its overall performance is robust, limitations in specific anatomical regions underscore the need for further training of the underlying algorithms. The results suggest that AI can complement clinical expertise but should not replace radiological assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>There is no comprehensive analysis of an AI-based tool for the diagnosis of pediatric fractures focusing on the upper extremities.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>The AI-based software demonstrated solid overall diagnostic accuracy in the detection of upper limb fractures in children, with performance differing by anatomical region.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>AI-based fracture detection can support pediatric emergency radiology, especially where expert interpretation is limited. However, further algorithm training is needed for certain anatomical regions and for detecting associated findings such as joint effusions to maximize clinical benefit.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-11947-w","type":"journal-article","created":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T10:47:30Z","timestamp":1755946050000},"page":"1253-1260","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Diagnostic value of artificial intelligence-based software for the detection of pediatric upper extremity fractures"],"prefix":"10.1007","volume":"36","author":[{"given":"Federico","family":"Mollica","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Corona","family":"Metz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias Stephan","family":"Anders","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kim Kathrin","family":"Wismayer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Schmid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan M.","family":"Niehues","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3585-5442","authenticated-orcid":false,"given":"Simon","family":"Veldhoen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,23]]},"reference":[{"key":"11947_CR1","doi-asserted-by":"crossref","unstructured":"Monget F, Sapienza M, McCracken KL et al (2022) Clinical characteristics and distribution of pediatric fractures at a tertiary hospital in Northern France: a 20-year-distance comparative analysis (1999\u20132019). Medicina (Kaunas) 58:610","DOI":"10.3390\/medicina58050610"},{"key":"11947_CR2","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1016\/j.injury.2021.01.014","volume":"52","author":"HS Jung","year":"2021","unstructured":"Jung HS, Park MS, Lee KM, Choi KJ, Choi WY, Sung KH (2021) Growth arrest and its risk factors after physeal fracture of the distal tibia in children and adolescents. Injury 52:844\u2013848","journal-title":"Injury"},{"key":"11947_CR3","doi-asserted-by":"crossref","unstructured":"Hooper N, Johnson L, Banting N et al (2024) Risk factor analysis for growth arrest in paediatric physeal fractures\u2014a prospective study. J Clin Med 13:2946","DOI":"10.3390\/jcm13102946"},{"key":"11947_CR4","doi-asserted-by":"crossref","unstructured":"Granata C, Sofia C, Francavilla M et al (2025) Let\u2019s talk about radiation dose and radiation protection in children. Pediatr Radiol 55:386\u2013396","DOI":"10.1007\/s00247-024-06009-0"},{"key":"11947_CR5","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/s00247-023-05822-3","volume":"54","author":"I Altmann-Schneider","year":"2024","unstructured":"Altmann-Schneider I, Kellenberger CJ, Pistorius S-M et al (2024) Artificial intelligence-based detection of paediatric appendicular skeletal fractures: performance and limitations for common fracture types and locations. Pediat Radiol 54:136\u2013145","journal-title":"Pediat Radiol"},{"key":"11947_CR6","doi-asserted-by":"publisher","first-page":"20150914","DOI":"10.1259\/bjr.20150914","volume":"89","author":"A Pinto","year":"2016","unstructured":"Pinto A, Reginelli A, Pinto F et al (2016) Errors in imaging patients in the emergency setting. Br J Radiol 89:20150914","journal-title":"Br J Radiol"},{"key":"11947_CR7","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20221031","volume":"96","author":"C Mello-Thoms","year":"2023","unstructured":"Mello-Thoms C, Mello CAB (2023) Clinical applications of artificial intelligence in radiology. Br J Radiol 96:20221031","journal-title":"Br J Radiol"},{"key":"11947_CR8","doi-asserted-by":"publisher","unstructured":"Ashworth E, Allan E, Pauling C, Laidlow-Singh H, Arthurs OJ, Shelmerdine SC (2025) Artificial intelligence (AI) in radiological paediatric fracture assessment: an updated systematic review. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-025-11449-9","DOI":"10.1007\/s00330-025-11449-9"},{"key":"11947_CR9","doi-asserted-by":"publisher","first-page":"3797","DOI":"10.1007\/s00330-021-07892-z","volume":"31","author":"KG van Leeuwen","year":"2021","unstructured":"van Leeuwen KG, Schalekamp S, Rutten M, van Ginneken B, de Rooij M (2021) Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol 31:3797\u20133804","journal-title":"Eur Radiol"},{"key":"11947_CR10","doi-asserted-by":"publisher","unstructured":"Huhtanen JT, Nyman M, Blanco Sequeiros R et al (2025) Comparative accuracy of two commercial AI algorithms for musculoskeletal trauma detection in emergency radiographs. Emerg Radiol. https:\/\/doi.org\/10.1007\/s10140-025-02353-2","DOI":"10.1007\/s10140-025-02353-2"},{"key":"11947_CR11","doi-asserted-by":"publisher","first-page":"2118","DOI":"10.1016\/j.acra.2023.06.016","volume":"30","author":"V Bousson","year":"2023","unstructured":"Bousson V, Attan\u00e9 G, Benoist N et al (2023) Artificial intelligence for detecting acute fractures in patients admitted to an emergency department: real-life performance of three commercial algorithms. Acad Radiol 30:2118\u20132139","journal-title":"Acad Radiol"},{"key":"11947_CR12","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1148\/radiol.2021203886","volume":"300","author":"L Duron","year":"2021","unstructured":"Duron L, Ducarouge A, Gillibert A et al (2021) Assessment of an AI aid in detection of adult appendicular skeletal fractures by emergency physicians and radiologists: a multicenter cross-sectional diagnostic study. Radiology 300:120\u2013129","journal-title":"Radiology"},{"key":"11947_CR13","doi-asserted-by":"publisher","first-page":"205","DOI":"10.3109\/00016925409174478","volume":"42","author":"HG Norell","year":"1954","unstructured":"Norell HG (1954) Roentgenologic visualization of the extracapsular fat; its importance in the diagnosis of traumatic injuries to the elbow. Acta Radiol 42:205\u2013210","journal-title":"Acta Radiol"},{"key":"11947_CR14","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1148\/73.5.717","volume":"73","author":"RC Bledsoe","year":"1959","unstructured":"Bledsoe RC, Izenstark JL (1959) Displacement of fat pads in diseases and injury of the elbow: a new radiographic sign. Radiology 73:717\u2013724","journal-title":"Radiology"},{"key":"11947_CR15","doi-asserted-by":"crossref","unstructured":"Oppenheimer J, L\u00fcken S, Hamm B, Niehues SM (2023) A prospective approach to integration of AI fracture detection software in radiographs into clinical workflow. Life 13:223","DOI":"10.3390\/life13010223"},{"key":"11947_CR16","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.1007\/s00247-022-05496-3","volume":"52","author":"T Nguyen","year":"2022","unstructured":"Nguyen T, Maarek R, Hermann AL et al (2022) Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists. Pediatr Radiol 52:2215\u20132226","journal-title":"Pediatr Radiol"},{"key":"11947_CR17","doi-asserted-by":"crossref","unstructured":"Meetschen M, Salh\u00f6fer L, Beck N et al (2024) AI-assisted X-ray fracture detection in residency training: evaluation in pediatric and adult trauma patients. Diagnostics (Basel) 14:596","DOI":"10.3390\/diagnostics14060596"},{"key":"11947_CR18","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1148\/radiol.210937","volume":"302","author":"A Guermazi","year":"2022","unstructured":"Guermazi A, Tannoury C, Kompel AJ et al (2022) Improving radiographic fracture recognition performance and efficiency using artificial intelligence. Radiology 302:627\u2013636","journal-title":"Radiology"},{"key":"11947_CR19","doi-asserted-by":"publisher","first-page":"e180001","DOI":"10.1148\/ryai.2019180001","volume":"1","author":"YL Thian","year":"2019","unstructured":"Thian YL, Li Y, Jagmohan P, Sia D, Chan VEY, Tan RT (2019) Convolutional neural networks for automated fracture detection and localization on wrist radiographs. Radiol Artif Intell 1:e180001","journal-title":"Radiol Artif Intell"},{"key":"11947_CR20","doi-asserted-by":"crossref","unstructured":"Kappelhof B, Roorda BL, Poppelaars MA et al (2022) Occult fractures in children with a radiographic fat pad sign of the elbow: a meta-analysis of 10 published studies. JBJS Rev 10:e.22.00114","DOI":"10.2106\/JBJS.RVW.22.00114"},{"key":"11947_CR21","doi-asserted-by":"crossref","unstructured":"Khanna NN, Maindarkar MA, Viswanathan V et al (2022) Economics of artificial intelligence in healthcare: diagnosis vs. treatment. Healthcare 10:2493","DOI":"10.3390\/healthcare10122493"},{"key":"11947_CR22","doi-asserted-by":"crossref","unstructured":"Lobig F, Graham J, Damania A, Sattin B, Reis J, Bharadwaj P (2024) Enhancing patient outcomes: the role of clinical utility in guiding healthcare providers in curating radiology AI applications. Front Digit Health 6:1359383","DOI":"10.3389\/fdgth.2024.1359383"},{"key":"11947_CR23","doi-asserted-by":"crossref","unstructured":"Ziegelmayer S, Graf M, Makowski M, Gawlitza J, Gassert F (2022) Cost-effectiveness of artificial intelligence support in computed tomography-based lung cancer screening. Cancers (Basel) 14:1729","DOI":"10.3390\/cancers14071729"},{"key":"11947_CR24","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1007\/s00256-022-04070-0","volume":"51","author":"D Hayashi","year":"2022","unstructured":"Hayashi D, Kompel AJ, Ventre J et al (2022) Automated detection of acute appendicular skeletal fractures in pediatric patients using deep learning. Skelet Radiol 51:2129\u20132139","journal-title":"Skelet Radiol"},{"key":"11947_CR25","doi-asserted-by":"publisher","first-page":"2386","DOI":"10.1007\/s00247-023-05754-y","volume":"53","author":"JR Zech","year":"2023","unstructured":"Zech JR, Jaramillo D, Altosaar J, Popkin CA, Wong TT (2023) Artificial intelligence to identify fractures on pediatric and young adult upper extremity radiographs. Pediatr Radiol 53:2386\u20132397","journal-title":"Pediatr Radiol"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11947-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-025-11947-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11947-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T09:25:31Z","timestamp":1772443531000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-025-11947-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,23]]},"references-count":25,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["11947"],"URL":"https:\/\/doi.org\/10.1007\/s00330-025-11947-w","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,23]]},"assertion":[{"value":"7 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The scientific guarantor of this publication is Simon Veldhoen.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"None of the study subjects or cohorts has been previously reported.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}