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Automatic detection and classification of peri-prosthetic femur fracture
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 pro...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8948116/ https://www.ncbi.nlm.nih.gov/pubmed/35157227 http://dx.doi.org/10.1007/s11548-021-02552-5 |
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author | Alzaid, Asma Wignall, Alice Dogramadzi, Sanja Pandit, Hemant Xie, Sheng Quan |
author_facet | Alzaid, Asma Wignall, Alice Dogramadzi, Sanja Pandit, Hemant Xie, Sheng Quan |
author_sort | Alzaid, Asma |
collection | PubMed |
description | 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 [Formula: see text] accuracy and [Formula: see text] F1-score in the binary classification: fracture/normal. In addition, the Resnet50 showed [Formula: see text] 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. |
format | Online Article Text |
id | pubmed-8948116 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-89481162022-04-07 Automatic detection and classification of peri-prosthetic femur fracture Alzaid, Asma Wignall, Alice Dogramadzi, Sanja Pandit, Hemant Xie, Sheng Quan Int J Comput Assist Radiol Surg Original Article 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 [Formula: see text] accuracy and [Formula: see text] F1-score in the binary classification: fracture/normal. In addition, the Resnet50 showed [Formula: see text] 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. Springer International Publishing 2022-02-14 2022 /pmc/articles/PMC8948116/ /pubmed/35157227 http://dx.doi.org/10.1007/s11548-021-02552-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Alzaid, Asma Wignall, Alice Dogramadzi, Sanja Pandit, Hemant Xie, Sheng Quan Automatic detection and classification of peri-prosthetic femur fracture |
title | Automatic detection and classification of peri-prosthetic femur fracture |
title_full | Automatic detection and classification of peri-prosthetic femur fracture |
title_fullStr | Automatic detection and classification of peri-prosthetic femur fracture |
title_full_unstemmed | Automatic detection and classification of peri-prosthetic femur fracture |
title_short | Automatic detection and classification of peri-prosthetic femur fracture |
title_sort | automatic detection and classification of peri-prosthetic femur fracture |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8948116/ https://www.ncbi.nlm.nih.gov/pubmed/35157227 http://dx.doi.org/10.1007/s11548-021-02552-5 |
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