1. An open source convolutional neural network to detect and localize distal radius fractures on plain radiographs.
- Author
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Oude Nijhuis KD, Barvelink B, Prijs J, Zhao Y, Liao Z, Jaarsma RL, IJpma FFA, Colaris JW, Doornberg JN, and Wijffels MME
- Subjects
- Humans, Retrospective Studies, Male, Female, Middle Aged, Adult, Aged, Wrist Injuries diagnostic imaging, Trauma Centers, Wrist Fractures, Radius Fractures diagnostic imaging, Neural Networks, Computer, Radiography, Algorithms
- Abstract
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., Competing Interests: Declarations. Conflict of interest: Jasper Prijs has received the following personal grants: An amount less than USD 15,000 from the Michael van Vloten Foundation (Rotterdam, The Netherlands), an amount less than USD 10,000 from ZonMw (Den Haag, The Netherlands), an amount less than USD 10,000 from the Prins Bernhard Cultuur Fonds (Amsterdam, The Netherlands) Ruurd Jaarsma is unpaid board member of the Australian Orthopaedic Association (SA/NT) and the SOS Fracture Alliance Australia. Ruurd Jaarsma is unpaid board member of the Australian Orthopaedic Association (SA/NT) and the SOS Fracture Alliance Australia. Ethical approval: Ethical approval was granted by the ethics committee (CALHN 13991)., (© 2025. The Author(s).)
- Published
- 2025
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