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Article: A deep neural network for MRI spinal inflammation in axial spondyloarthritis

TitleA deep neural network for MRI spinal inflammation in axial spondyloarthritis
Authors
KeywordsAnkylosing spondylitis
Artificial intelligence
Axial spondyloarthritis
Deep learning
Inflammation
MRI
Spine
Issue Date8-Jan-2024
PublisherSpringer
Citation
European Spine Journal, 2024 How to Cite?
Abstract

Objective

To develop a deep neural network for the detection of inflammatory spine in short tau inversion recovery (STIR) sequence of magnetic resonance imaging (MRI) on patients with axial spondyloarthritis (axSpA).

Methods

A total 330 patients with axSpA were recruited. STIR MRI of the whole spine and clinical data were obtained. Regions of interests (ROIs) were drawn outlining the active inflammatory lesion consisting of bone marrow edema (BME). Spinal inflammation was defined by the presence of an active inflammatory lesion on the STIR sequence. The 'fake-color' images were constructed. Images from 270 and 60 patients were randomly separated into the training/validation and testing sets, respectively. Deep neural network was developed using attention UNet. The neural network performance was compared to the image interpretation by a radiologist blinded to the ground truth.

Results

Active inflammatory lesions were identified in 2891 MR images and were absent in 14,590 MR images. The sensitivity and specificity of the derived deep neural network were 0.80 ± 0.03 and 0.88 ± 0.02, respectively. The Dice coefficient of the true positive lesions was 0.55 ± 0.02. The area under the curve of the receiver operating characteristic (AUC-ROC) curve of the deep neural network was 0.87 ± 0.02. The performance of the developed deep neural network was comparable to the interpretation of a radiologist with similar sensitivity and specificity.

Conclusion

The developed deep neural network showed similar sensitivity and specificity to a radiologist with four years of experience. The results indicated that the network can provide a reliable and straightforward way of interpreting spinal MRI. The use of this deep neural network has the potential to expand the use of spinal MRI in managing axSpA.


Persistent Identifierhttp://hdl.handle.net/10722/340750
ISSN
2023 Impact Factor: 2.6
2023 SCImago Journal Rankings: 1.042
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLin, Yingying-
dc.contributor.authorChan, Shirley Chiu Wai-
dc.contributor.authorChung, Ho Yin-
dc.contributor.authorLee, Kam Ho-
dc.contributor.authorCao, Peng-
dc.date.accessioned2024-03-11T10:46:50Z-
dc.date.available2024-03-11T10:46:50Z-
dc.date.issued2024-01-08-
dc.identifier.citationEuropean Spine Journal, 2024-
dc.identifier.issn0940-6719-
dc.identifier.urihttp://hdl.handle.net/10722/340750-
dc.description.abstract<h3>Objective</h3><p>To develop a deep neural network for the detection of inflammatory spine in short tau inversion recovery (STIR) sequence of magnetic resonance imaging (MRI) on patients with axial spondyloarthritis (axSpA).</p><h3>Methods</h3><p>A total 330 patients with axSpA were recruited. STIR MRI of the whole spine and clinical data were obtained. Regions of interests (ROIs) were drawn outlining the active inflammatory lesion consisting of bone marrow edema (BME). Spinal inflammation was defined by the presence of an active inflammatory lesion on the STIR sequence. The 'fake-color' images were constructed. Images from 270 and 60 patients were randomly separated into the training/validation and testing sets, respectively. Deep neural network was developed using attention UNet. The neural network performance was compared to the image interpretation by a radiologist blinded to the ground truth.</p><h3>Results</h3><p>Active inflammatory lesions were identified in 2891 MR images and were absent in 14,590 MR images. The sensitivity and specificity of the derived deep neural network were 0.80 ± 0.03 and 0.88 ± 0.02, respectively. The Dice coefficient of the true positive lesions was 0.55 ± 0.02. The area under the curve of the receiver operating characteristic (AUC-ROC) curve of the deep neural network was 0.87 ± 0.02. The performance of the developed deep neural network was comparable to the interpretation of a radiologist with similar sensitivity and specificity.</p><h3>Conclusion</h3><p>The developed deep neural network showed similar sensitivity and specificity to a radiologist with four years of experience. The results indicated that the network can provide a reliable and straightforward way of interpreting spinal MRI. The use of this deep neural network has the potential to expand the use of spinal MRI in managing axSpA.</p>-
dc.languageeng-
dc.publisherSpringer-
dc.relation.ispartofEuropean Spine Journal-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectAnkylosing spondylitis-
dc.subjectArtificial intelligence-
dc.subjectAxial spondyloarthritis-
dc.subjectDeep learning-
dc.subjectInflammation-
dc.subjectMRI-
dc.subjectSpine-
dc.titleA deep neural network for MRI spinal inflammation in axial spondyloarthritis-
dc.typeArticle-
dc.identifier.doi10.1007/s00586-023-08099-0-
dc.identifier.scopuseid_2-s2.0-85181713326-
dc.identifier.eissn1432-0932-
dc.identifier.isiWOS:001137673100001-
dc.identifier.issnl0940-6719-

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