Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Alzaid, Asma, Wignall, Alice, Dogramadzi, Sanja, Pandit, Hemant, Xie, Sheng Quan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
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
_version_ 1784674596207722496
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
work_keys_str_mv AT alzaidasma automaticdetectionandclassificationofperiprostheticfemurfracture
AT wignallalice automaticdetectionandclassificationofperiprostheticfemurfracture
AT dogramadzisanja automaticdetectionandclassificationofperiprostheticfemurfracture
AT pandithemant automaticdetectionandclassificationofperiprostheticfemurfracture
AT xieshengquan automaticdetectionandclassificationofperiprostheticfemurfracture