Evidence map›Paper›PMID 40691325›Full record

ArticleEuropean journal of trauma and emergency surgery : official publication of the European Trauma Society2025

Open-source convolutional neural network to classify distal radial fractures according to the AO/OTA classification on plain radiographs.

Koen D Oude Nijhuis, Jasper Prijs, Britt Barvelink, Hans van Luit, Yang Zhao, Zhibin Liao, Ruurd L Jaarsma, Frank F A IJpma, Mathieu M E Wijffels, Job N Doornberg and 2 more

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Article in European journal of trauma and emergency surgery : official publication of the European Trauma Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Koen D Oude NijhuisDepartment of Orthopedic Surgery, University Medical Centre Groningen and Groningen University, Groningen, The Netherlands. k.d.oude.nijhuis@umcg.nl.
Jasper PrijsDepartment of Orthopedic Surgery, University Medical Centre Groningen and Groningen University, Groningen, The Netherlands.
Britt BarvelinkDepartment of Orthopedics and Sports Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands.
Hans van LuitDepartment of Orthopedic Surgery, University Medical Centre Groningen and Groningen University, Groningen, The Netherlands.
Yang ZhaoAustralian Institute for Machine Learning, Adelaide, Australia.
Zhibin LiaoAustralian Institute for Machine Learning, Adelaide, Australia.
Ruurd L JaarsmaDepartment of Orthopedic Surgery, Flinders University and Medical Centre, Adelaide, South Australia, Australia.
Frank F A IJpmaDepartment of Trauma Surgery, University Medical Centre Groningen and Groningen University, Hanzeplein 1, 9713PZ, Groningen, The Netherlands.
Mathieu M E WijffelsTrauma Research Unit Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, Rotterdam, 3000, CA, The Netherlands.
Job N DoornbergDepartment of Orthopedic Surgery, University Medical Centre Groningen and Groningen University, Groningen, The Netherlands.
Joost W ColarisDepartment of Orthopedics and Sports Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands.
Machine Learning Consortium

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeConvolutional Neural Networks (CNNs) have shown promise in fracture detection, but their ability to improve surgeons' inconsistent fracture classification remains unstudied. Therefore, our aim was create and (externally) validate the performance of an open-source CNN algorithm to classify DRFs according to the AO/OTA classification system?

methodsPatients with postero-anterior, lateral and oblique radiographs were included. Radiographs were classified according to the AO/OTA-classification and were used to train a CNN algorithm. The algorithm was tested on an internal and external validation set (two other level 1 trauma centers), with the DRFs classified by three independent surgeons.

results659 radiographs were used to train the algorithm. Internal- and external validation sets contained 190 and 188 patients, respectively. Upon internal validation, the CNN had an accuracy of 62% and an area under receiving operating characteristic curve (AUC) of 0.63-0.93 (type 2R3A 0.84, type 2R3B 0.63, type 2R3C 0.75, and no DRF 0.93). On the external validation, the algorithm has an accuracy of 61% and an AUC of 0.56-0.88 (type 2R3A 0.82, type 2R3B 0.56, type 2R3C 0.75, and no DRF 0.88).

conclusionThe presented algorithm has demonstrated excellent accuracy in classifying type 2R3A DRFs and excluding DRFs. However, poor to moderate accuracy is observed in classifying 2R3B and 2R3C DRFs according to the AO/OTA system, similar to limited surgeons' inter-observer agreement. These results show that despite previous excellence in fracture detection, CNN-algorithms struggle with classifying; potentially showing the inherent problems with these classification systems.

Indexed as

Neural Networks, ComputerRadiographyRadius FracturesAlgorithmsConvolutional Neural NetworksHumansArtificial intelligenceDiagnostic accuracyTraumaWrist fractures

Identifiers

PMID40691325
PMCPMC12279608

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