Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study
| Title: | Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study |
|---|---|
| Authors: | Pan, Qiong, Zhang, Kai, He, Lin, Dong, Zhou, Zhang, Lei, Wu, Xiaohang, Wu, Yi, Gao, Yanjun |
| Source: | JMIR Medical Informatics, Vol 9, Iss 5, p e14755 (2021) |
| Publisher Information: | JMIR Publications, 2021. |
| Publication Year: | 2021 |
| Collection: | LCC:Computer applications to medicine. Medical informatics |
| Subject Terms: | Computer applications to medicine. Medical informatics, R858-859.7 |
| More Details: | BackgroundDisk herniation and disk bulge are two common disorders of lumbar intervertebral disks (IVDs) that often result in numbness, pain in the lower limbs, and lower back pain. Magnetic resonance (MR) imaging is one of the most efficient techniques for detecting lumbar diseases and is widely used for making clinical diagnoses at hospitals. However, there is a lack of efficient tools for effectively interpreting massive amounts of MR images to meet the requirements of many radiologists. ObjectiveThe aim of this study was to present an automatic system for diagnosing disk bulge and herniation that saves time and can effectively and significantly reduce the workload of radiologists. MethodsThe diagnosis of lumbar vertebral disorders is highly dependent on medical images. Therefore, we chose the two most common diseases—disk bulge and herniation—as research subjects. This study is mainly about identifying the position of IVDs (lumbar vertebra [L] 1 to L2, L2-L3, L3-L4, L4-L5, and L5 to sacral vertebra [S] 1) by analyzing the geometrical relationship between sagittal and axial images and classifying axial lumbar disk MR images via deep convolutional neural networks. ResultsThis system involved 4 steps. In the first step, it automatically located vertebral bodies (including the L1, L2, L3, L4, L5, and S1) in sagittal images by using the faster region-based convolutional neural network, and our fourfold cross-validation showed 100% accuracy. In the second step, it spontaneously identified the corresponding disk in each axial lumbar disk MR image with 100% accuracy. In the third step, the accuracy for automatically locating the intervertebral disk region of interest in axial MR images was 100%. In the fourth step, the 3-class classification (normal disk, disk bulge, and disk herniation) accuracies for the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 IVDs were 92.7%, 84.4%, 92.1%, 90.4%, and 84.2%, respectively. ConclusionsThe automatic diagnosis system was successfully built, and it could classify images of normal disks, disk bulge, and disk herniation. This system provided a web-based test for interpreting lumbar disk MR images that could significantly improve diagnostic efficiency and standardized diagnosis reports. This system can also be used to detect other lumbar abnormalities and cervical spondylosis. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2291-9694 |
| Relation: | https://medinform.jmir.org/2021/5/e14755; https://doaj.org/toc/2291-9694 |
| DOI: | 10.2196/14755 |
| Access URL: | https://doaj.org/article/a1ea330775584b29ba6891557eb56bad |
| Accession Number: | edsdoj.1ea330775584b29ba6891557eb56bad |
| Database: | Directory of Open Access Journals |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pan%2C+Qiong%22">Pan, Qiong</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Kai%22">Zhang, Kai</searchLink><br /><searchLink fieldCode="AR" term="%22He%2C+Lin%22">He, Lin</searchLink><br /><searchLink fieldCode="AR" term="%22Dong%2C+Zhou%22">Dong, Zhou</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Lei%22">Zhang, Lei</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Xiaohang%22">Wu, Xiaohang</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Yi%22">Wu, Yi</searchLink><br /><searchLink fieldCode="AR" term="%22Gao%2C+Yanjun%22">Gao, Yanjun</searchLink> – Name: TitleSource Label: Source Group: Src Data: JMIR Medical Informatics, Vol 9, Iss 5, p e14755 (2021) – Name: Publisher Label: Publisher Information Group: PubInfo Data: JMIR Publications, 2021. – Name: DatePubCY Label: Publication Year Group: Date Data: 2021 – Name: Subset Label: Collection Group: HoldingsInfo Data: LCC:Computer applications to medicine. Medical informatics – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+applications+to+medicine%2E+Medical+informatics%22">Computer applications to medicine. Medical informatics</searchLink><br /><searchLink fieldCode="DE" term="%22R858-859%2E7%22">R858-859.7</searchLink> – Name: Abstract Label: Description Group: Ab Data: BackgroundDisk herniation and disk bulge are two common disorders of lumbar intervertebral disks (IVDs) that often result in numbness, pain in the lower limbs, and lower back pain. Magnetic resonance (MR) imaging is one of the most efficient techniques for detecting lumbar diseases and is widely used for making clinical diagnoses at hospitals. However, there is a lack of efficient tools for effectively interpreting massive amounts of MR images to meet the requirements of many radiologists. ObjectiveThe aim of this study was to present an automatic system for diagnosing disk bulge and herniation that saves time and can effectively and significantly reduce the workload of radiologists. MethodsThe diagnosis of lumbar vertebral disorders is highly dependent on medical images. Therefore, we chose the two most common diseases—disk bulge and herniation—as research subjects. This study is mainly about identifying the position of IVDs (lumbar vertebra [L] 1 to L2, L2-L3, L3-L4, L4-L5, and L5 to sacral vertebra [S] 1) by analyzing the geometrical relationship between sagittal and axial images and classifying axial lumbar disk MR images via deep convolutional neural networks. ResultsThis system involved 4 steps. In the first step, it automatically located vertebral bodies (including the L1, L2, L3, L4, L5, and S1) in sagittal images by using the faster region-based convolutional neural network, and our fourfold cross-validation showed 100% accuracy. In the second step, it spontaneously identified the corresponding disk in each axial lumbar disk MR image with 100% accuracy. In the third step, the accuracy for automatically locating the intervertebral disk region of interest in axial MR images was 100%. In the fourth step, the 3-class classification (normal disk, disk bulge, and disk herniation) accuracies for the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 IVDs were 92.7%, 84.4%, 92.1%, 90.4%, and 84.2%, respectively. ConclusionsThe automatic diagnosis system was successfully built, and it could classify images of normal disks, disk bulge, and disk herniation. This system provided a web-based test for interpreting lumbar disk MR images that could significantly improve diagnostic efficiency and standardized diagnosis reports. This system can also be used to detect other lumbar abnormalities and cervical spondylosis. – Name: TypeDocument Label: Document Type Group: TypDoc Data: article – Name: Format Label: File Description Group: SrcInfo Data: electronic resource – Name: Language Label: Language Group: Lang Data: English – Name: ISSN Label: ISSN Group: ISSN Data: 2291-9694 – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://medinform.jmir.org/2021/5/e14755; https://doaj.org/toc/2291-9694 – Name: DOI Label: DOI Group: ID Data: 10.2196/14755 – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://doaj.org/article/a1ea330775584b29ba6891557eb56bad" linkWindow="_blank">https://doaj.org/article/a1ea330775584b29ba6891557eb56bad</link> – Name: AN Label: Accession Number Group: ID Data: edsdoj.1ea330775584b29ba6891557eb56bad |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2196/14755 Languages: – Text: English PhysicalDescription: Pagination: StartPage: e14755 Subjects: – SubjectFull: Computer applications to medicine. Medical informatics Type: general – SubjectFull: R858-859.7 Type: general Titles: – TitleFull: Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pan, Qiong – PersonEntity: Name: NameFull: Zhang, Kai – PersonEntity: Name: NameFull: He, Lin – PersonEntity: Name: NameFull: Dong, Zhou – PersonEntity: Name: NameFull: Zhang, Lei – PersonEntity: Name: NameFull: Wu, Xiaohang – PersonEntity: Name: NameFull: Wu, Yi – PersonEntity: Name: NameFull: Gao, Yanjun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 22919694 Numbering: – Type: volume Value: 9 – Type: issue Value: 5 Titles: – TitleFull: JMIR Medical Informatics Type: main |
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