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A Computer-Assisted Diagnostic Method for Accurate Detection of Early Nondisplaced Fractures of the Femoral Neck

Nondisplaced femoral neck fractures are sometimes misdiagnosed by radiographs, which may deteriorate into displaced fractures. However, few efficient artificial intelligent methods have been reported. We developed an automatic detection method using deep learning networks to pinpoint femoral neck fr...

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Detalles Bibliográficos
Autores principales: Hsieh, S. L., Chiang, J. L., Chuang, C. H., Chen, Y. Y., Hsu, C. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669449/
https://www.ncbi.nlm.nih.gov/pubmed/38002100
http://dx.doi.org/10.3390/biomedicines11113100
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author Hsieh, S. L.
Chiang, J. L.
Chuang, C. H.
Chen, Y. Y.
Hsu, C. J.
author_facet Hsieh, S. L.
Chiang, J. L.
Chuang, C. H.
Chen, Y. Y.
Hsu, C. J.
author_sort Hsieh, S. L.
collection PubMed
description Nondisplaced femoral neck fractures are sometimes misdiagnosed by radiographs, which may deteriorate into displaced fractures. However, few efficient artificial intelligent methods have been reported. We developed an automatic detection method using deep learning networks to pinpoint femoral neck fractures on radiographs to assist physicians in making an accurate diagnosis in the first place. Our proposed accurate automatic detection method, called the direction-aware fracture-detection network (DAFDNet), consists of two steps, namely region-of-interest (ROI) segmentation and fracture detection. The first step removes the noise region and pinpoints the femoral neck region. The fracture-detection step uses a direction-aware deep learning algorithm to mark the exact femoral neck fracture location in the region detected in the first step. A total of 3840 femoral neck parts in anterior–posterior (AP) pelvis radiographs collected from the China Medical University Hospital database were used to test our method. The simulation results showed that DAFDNet outperformed the U-Net and DenseNet methods in terms of the IOU value, Dice value, and Jaccard value. Our proposed DAFDNet demonstrated over 94.8% accuracy in differentiating non-displaced Garden type I and type II femoral neck fracture cases. Our DAFDNet method outperformed the diagnostic accuracy of general practitioners and orthopedic surgeons in accurately locating Garden type I and type II fracture locations. This study can determine the feasibility of applying artificial intelligence in a clinical setting and how the use of deep learning networks assists physicians in improving correct diagnoses compared to the current traditional orthopedic manual assessments.
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spelling pubmed-106694492023-11-20 A Computer-Assisted Diagnostic Method for Accurate Detection of Early Nondisplaced Fractures of the Femoral Neck Hsieh, S. L. Chiang, J. L. Chuang, C. H. Chen, Y. Y. Hsu, C. J. Biomedicines Article Nondisplaced femoral neck fractures are sometimes misdiagnosed by radiographs, which may deteriorate into displaced fractures. However, few efficient artificial intelligent methods have been reported. We developed an automatic detection method using deep learning networks to pinpoint femoral neck fractures on radiographs to assist physicians in making an accurate diagnosis in the first place. Our proposed accurate automatic detection method, called the direction-aware fracture-detection network (DAFDNet), consists of two steps, namely region-of-interest (ROI) segmentation and fracture detection. The first step removes the noise region and pinpoints the femoral neck region. The fracture-detection step uses a direction-aware deep learning algorithm to mark the exact femoral neck fracture location in the region detected in the first step. A total of 3840 femoral neck parts in anterior–posterior (AP) pelvis radiographs collected from the China Medical University Hospital database were used to test our method. The simulation results showed that DAFDNet outperformed the U-Net and DenseNet methods in terms of the IOU value, Dice value, and Jaccard value. Our proposed DAFDNet demonstrated over 94.8% accuracy in differentiating non-displaced Garden type I and type II femoral neck fracture cases. Our DAFDNet method outperformed the diagnostic accuracy of general practitioners and orthopedic surgeons in accurately locating Garden type I and type II fracture locations. This study can determine the feasibility of applying artificial intelligence in a clinical setting and how the use of deep learning networks assists physicians in improving correct diagnoses compared to the current traditional orthopedic manual assessments. MDPI 2023-11-20 /pmc/articles/PMC10669449/ /pubmed/38002100 http://dx.doi.org/10.3390/biomedicines11113100 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hsieh, S. L.
Chiang, J. L.
Chuang, C. H.
Chen, Y. Y.
Hsu, C. J.
A Computer-Assisted Diagnostic Method for Accurate Detection of Early Nondisplaced Fractures of the Femoral Neck
title A Computer-Assisted Diagnostic Method for Accurate Detection of Early Nondisplaced Fractures of the Femoral Neck
title_full A Computer-Assisted Diagnostic Method for Accurate Detection of Early Nondisplaced Fractures of the Femoral Neck
title_fullStr A Computer-Assisted Diagnostic Method for Accurate Detection of Early Nondisplaced Fractures of the Femoral Neck
title_full_unstemmed A Computer-Assisted Diagnostic Method for Accurate Detection of Early Nondisplaced Fractures of the Femoral Neck
title_short A Computer-Assisted Diagnostic Method for Accurate Detection of Early Nondisplaced Fractures of the Femoral Neck
title_sort computer-assisted diagnostic method for accurate detection of early nondisplaced fractures of the femoral neck
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669449/
https://www.ncbi.nlm.nih.gov/pubmed/38002100
http://dx.doi.org/10.3390/biomedicines11113100
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