Open-source convolutional neural network to classify distal radial fractures according to the AO/OTA classification on plain radiographs.
Purpose: Convolutional 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 D...
| Published in: | European Journal of Trauma & Emergency Surgery Vol. 51; no. 1; pp. 1 - 11 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
7/21/2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186782146&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186782146 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: 7/21/2025 vid: 51 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 186782146 186782146 186782146 10.1007/s00068-025-02931-6 186782146 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Open-source convolutional neural network to classify distal radial fractures according to the AO/OTA classification on plain radiographs. aug: au: Oude Nijhuis, Koen D. Prijs, Jasper Barvelink, Britt van Luit, Hans Zhao, Yang Liao, Zhibin Jaarsma, Ruurd L. IJpma, Frank F. A. Wijffels, Mathieu M. E. Doornberg, Job N. Colaris, Joost W. Laane, Charlotte Canta, Olga Hoeksema, Sanne Aksakal, Kaan Mhmud, Haras Jutte, Paul Mallee, Wouter Duckworth, Andrew Schep, Niels affil: https://ror.org/03cv38k47 Department of Orthopedic Surgery, University Medical Centre Groningen and Groningen University, Groningen, The Netherlands sug: subj: Radius Fractures, Distal Classification Radius Fractures, Distal Radiography Radiography Methods Convolutional Neural Networks Evaluation Classification Algorithms Evaluation Validity Severity of Injury Human Validation Studies Radius Fractures, Distal Pathology Machine Learning Algorithms Trauma Centers Surgeons ROC Curve Descriptive Statistics Interrater Reliability Intrarater Reliability Wrist Fractures Radius Fractures, Distal Diagnosis Predictive Value of Tests Sensitivity and Specificity ab: Purpose: Convolutional 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? Methods: Patients 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. Results: 659 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). Conclusion: The 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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