Alzaid, A, Wignall, A, Dogramadzi, S et al. (2 more authors) (2022) Automatic detection and classification of peri-prosthetic femur fracture. International Journal of Computer Assisted Radiology and Surgery, 17 (4). pp. 649-660. ISSN 1861-6410
Abstract
Purpose
Object classification and localization is a key task of computer-aided diagnosis (CAD) tool. Although there have been numerous generic deep learning (DL) models developed for CAD, there is no work in the literature to evaluate their effectiveness when utilized in diagnosing fractures in proximity of joint implants. In this work, we aim to assess the performance of existing classification systems on binary and multi-class problems (fracture types) using plain radiographs. In addition, we evaluated the performance of object detection systems using the one- and two-stage DL architectures.
Methods
A data set of 1272 X-ray images of Peri-prosthetic Femur Fracture PFF was collected. The fractures were annotated with bounding boxes and classified according to the Vancouver Classification System (type A, B, C) by two clinical specialists. Four classification models such as Densenet161, Resnet50, Inception, VGG and two object detection models such as Faster RCNN and RetinaNet were evaluated, and their performance compared. Six confusion matrix-based measures were reported to evaluate fracture classification. For localization of the fracture, Average Precision and localization accuracy were reported.
Results
The Resnet50 showed the best performance with 95% accuracy and 94% F1-score in the binary classification: fracture/normal. In addition, the Resnet50 showed 90% accuracy in multi-classification (normal, Vancouver type A, B and C).
Conclusions
A large data set of PFF images and the annotations of fracture features by two independent assessments were created to implement a DL-based approach for detecting, classifying and localizing PFFs. It was shown that this approach could be a promising diagnostic tool of fractures in proximity of joint implants.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2022. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) |
Keywords: | Bone fracture; Computer aided diagnostics; Deep learning ·; Medical imaging; Surgical planning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Institute of Rheumatology & Musculoskeletal Medicine (LIRMM) (Leeds) > Orthopaedics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Feb 2022 13:33 |
Last Modified: | 27 Jun 2022 09:20 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s11548-021-02552-5 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183892 |
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