Proximal femur fracture detection on plain radiography via feature pyramid networks

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240–310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Fe...

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Published inScientific reports Vol. 14; no. 1; pp. 12046 - 14
Main Authors Yıldız Potter, İlkay, Yeritsyan, Diana, Mahar, Sarah, Kheir, Nadim, Vaziri, Aidin, Putman, Melissa, Rodriguez, Edward K., Wu, Jim, Nazarian, Ara, Vaziri, Ashkan
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 27.05.2024
Nature Publishing Group
Nature Portfolio
Subjects
692/308/53/2423
692/698/1671/63
692/700/1421/1770
Aged
Aged, 80 and over
Deep Learning
Female
Femoral Fractures - diagnostic imaging
Femur
Fracture
Fractures
Hip
Hip Fractures - diagnostic imaging
Hip joint
Humanities and Social Sciences
Humans
Localization
Male
Middle Aged
multidisciplinary
Neural Networks, Computer
Plain radiography
Proximal Femoral Fractures
Proximal femur
Radiography
Radiography - methods
Science
Science (multidisciplinary)
Sensitivity and Specificity
Deep learning
Plain radiography
Fracture
Proximal femur
Hip
Online AccessGet full text

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Abstract Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240–310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics. We used a dataset of 823 hip radiographs of 150 subjects with proximal femur fractures and 362 controls to develop and evaluate the deep learning model. Our model attained 0.94 specificity and 0.95 sensitivity in fracture detection over the diverse imaging dataset. We compared the performance of our model against five benchmark FPN models, demonstrating 6–14% sensitivity and 1–9% accuracy improvement. In addition, we demonstrated that our model outperforms a state-of-the-art transformer model based on DINO network by 17% sensitivity and 5% accuracy, while taking half the time on average to process a radiograph. The developed model can aid radiologists and support on-premise integration with hospital cloud services to enable automatic, opportunistic screening for hip fractures.
AbstractList Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240–310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics. We used a dataset of 823 hip radiographs of 150 subjects with proximal femur fractures and 362 controls to develop and evaluate the deep learning model. Our model attained 0.94 specificity and 0.95 sensitivity in fracture detection over the diverse imaging dataset. We compared the performance of our model against five benchmark FPN models, demonstrating 6–14% sensitivity and 1–9% accuracy improvement. In addition, we demonstrated that our model outperforms a state-of-the-art transformer model based on DINO network by 17% sensitivity and 5% accuracy, while taking half the time on average to process a radiograph. The developed model can aid radiologists and support on-premise integration with hospital cloud services to enable automatic, opportunistic screening for hip fractures.
Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics. We used a dataset of 823 hip radiographs of 150 subjects with proximal femur fractures and 362 controls to develop and evaluate the deep learning model. Our model attained 0.94 specificity and 0.95 sensitivity in fracture detection over the diverse imaging dataset. We compared the performance of our model against five benchmark FPN models, demonstrating 6-14% sensitivity and 1-9% accuracy improvement. In addition, we demonstrated that our model outperforms a state-of-the-art transformer model based on DINO network by 17% sensitivity and 5% accuracy, while taking half the time on average to process a radiograph. The developed model can aid radiologists and support on-premise integration with hospital cloud services to enable automatic, opportunistic screening for hip fractures.Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics. We used a dataset of 823 hip radiographs of 150 subjects with proximal femur fractures and 362 controls to develop and evaluate the deep learning model. Our model attained 0.94 specificity and 0.95 sensitivity in fracture detection over the diverse imaging dataset. We compared the performance of our model against five benchmark FPN models, demonstrating 6-14% sensitivity and 1-9% accuracy improvement. In addition, we demonstrated that our model outperforms a state-of-the-art transformer model based on DINO network by 17% sensitivity and 5% accuracy, while taking half the time on average to process a radiograph. The developed model can aid radiologists and support on-premise integration with hospital cloud services to enable automatic, opportunistic screening for hip fractures.
Abstract Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240–310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics. We used a dataset of 823 hip radiographs of 150 subjects with proximal femur fractures and 362 controls to develop and evaluate the deep learning model. Our model attained 0.94 specificity and 0.95 sensitivity in fracture detection over the diverse imaging dataset. We compared the performance of our model against five benchmark FPN models, demonstrating 6–14% sensitivity and 1–9% accuracy improvement. In addition, we demonstrated that our model outperforms a state-of-the-art transformer model based on DINO network by 17% sensitivity and 5% accuracy, while taking half the time on average to process a radiograph. The developed model can aid radiologists and support on-premise integration with hospital cloud services to enable automatic, opportunistic screening for hip fractures.
ArticleNumber 12046
Author Nazarian, Ara
Yeritsyan, Diana
Putman, Melissa
Vaziri, Ashkan
Kheir, Nadim
Vaziri, Aidin
Rodriguez, Edward K.
Wu, Jim
Yıldız Potter, İlkay
Mahar, Sarah
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Issue 1
Keywords Deep learning
Plain radiography
Fracture
Proximal femur
Hip
Language English
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SSID ssj0000529419
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Snippet Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240–310% by 2050. Hip fractures are...
Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fractures are...
Abstract Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240–310% by 2050. Hip...
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StartPage 12046
SubjectTerms 692/308/53/2423
692/698/1671/63
692/700/1421/1770
Aged
Aged, 80 and over
Deep Learning
Female
Femoral Fractures - diagnostic imaging
Femur
Fracture
Fractures
Hip
Hip Fractures - diagnostic imaging
Hip joint
Humanities and Social Sciences
Humans
Localization
Male
Middle Aged
multidisciplinary
Neural Networks, Computer
Plain radiography
Proximal Femoral Fractures
Proximal femur
Radiography
Radiography - methods
Science
Science (multidisciplinary)
Sensitivity and Specificity
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Title Proximal femur fracture detection on plain radiography via feature pyramid networks
URI https://link.springer.com/article/10.1038/s41598-024-63001-2
https://www.ncbi.nlm.nih.gov/pubmed/38802519
https://www.proquest.com/docview/3060641043
https://www.proquest.com/docview/3061137110
https://pubmed.ncbi.nlm.nih.gov/PMC11130146
https://doaj.org/article/053762e6a1ad42c18963ba8d7fd29f41
Volume 14
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