An open source convolutional neural network to detect and localize distal radius fractures on plain radiographs.
Purpose: Distal radius fractures (DRFs) are often initially assessed by junior doctors under time constraints, with limited supervision, risking significant consequences if missed. Convolutional Neural Networks (CNNs) can aid in diagnosing fractures. This study aims to internally and externally vali...
| Publicado en: | European Journal of Trauma & Emergency Surgery Vol. 51; no. 1; pp. 1 - 9 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182633001&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182633001 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18639933 3C05 jtl: European Journal of Trauma & Emergency Surgery issn: 18639933 maglogo: N pubinfo: dt: 2025 vid: 51 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182633001 10.1007/s00068-024-02731-4 182633001 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An open source convolutional neural network to detect and localize distal radius fractures on plain radiographs. aug: au: Oude Nijhuis, Koen D. Barvelink, Britt Prijs, Jasper Zhao, Yang Liao, Zhibin Jaarsma, Ruurd L. IJpma, Frank F. A. Colaris, Joost W. Doornberg, Job N. Wijffels, Mathieu M. E. On behalf of the Machine Learning Consortium van Luit, Hans Canta, Olga Hoeksema, Sanne Laane, Charlotte L.E. Aksakal, Kaan Mhmud, Haras Jutte, Paul Gordon, Max Mallee, Wouter affil: https://ror.org/03cv38k47 Department of Orthopaedic Surgery, University Medical Centre Groningen and Groningen University, Hanzeplein 1, 9713PZ, Groningen, the Netherlands sug: ab: Purpose: Distal radius fractures (DRFs) are often initially assessed by junior doctors under time constraints, with limited supervision, risking significant consequences if missed. Convolutional Neural Networks (CNNs) can aid in diagnosing fractures. This study aims to internally and externally validate an open source algorithm for the detection and localization of DRFs. Methods: Patients from a level 1 trauma center from Adelaide, Australia that presented between 2016 and 2020 with wrist trauma were retrospectively included. Radiographs were reviewed confirming the presence or absence of a fracture, as well as annotating radius, ulna, and fracture location. An internal validation dataset from the same hospital was created. An external validation set was created with two other level 1 trauma centers, from Groningen and Rotterdam, the Netherlands. Three surgeons reviewed both sets for DRFs. Results: The algorithm was trained on 659 radiographs. The internal validation set included 190 patients, showing an accuracy of 87% and an AUC of 0.93 for DRF detection. The external validation set consisted of 188 patients, with an accuracy and AUC were 82% and 0.88 respectively. Radial and ulnar bone segmentation on the internal validation was excellent with an AP50 of 99 and 98, but moderate for fracture segmentation with an AP50 of 29. For external validation the AP50 was 92, 89 and 25 for radius, ulna, and fracture respectively. Conclusion: This open-source algorithm effectively detects DRFs with high accuracy and localizes them with moderate accuracy. It can assist clinicians in diagnosing suspected DRFs and is the first radiograph-based CNN externally validated on patients from multiple hospitals. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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