Journal
DIAGNOSTICS
Volume 11, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/diagnostics11071156
Keywords
axial spondyloarthritis; bone marrow edema; sacroiliitis; magnetic resonance imaging; deep learning
Categories
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education, Korea [2018R1D1A1B07049248]
- National Research Foundation of Korea through the Korean Government (MSIT) [2021R1A2B5B01001412]
- National Research Foundation of Korea [2018R1D1A1B07049248, 2021R1A2B5B01001412] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This study developed a method for detecting bone marrow edema in the sacroiliac joints using MR imaging and a deep-learning network, achieving excellent detection performance in validation results.
Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the sacroiliac joints. In this study, we develop a method for detecting bone marrow edema by magnetic resonance (MR) imaging of the sacroiliac joints and a deep-learning network. A total of 815 MR images of the sacroiliac joints were obtained from 60 patients diagnosed with axSpA and 19 healthy subjects. Gadolinium-enhanced fat-suppressed T1-weighted oblique coronal images were used for deep learning. Active sacroiliitis was defined as bone marrow edema, and the following processes were performed: setting the region of interest (ROI) and normalizing it to a size suitable for input to a deep-learning network, determining bone marrow edema using a convolutional-neural-network-based deep-learning network for individual MR images, and determining sacroiliac arthritis in subject examinations based on the classification results of individual MR images. About 70% of the patients and normal subjects were randomly selected for the training dataset, and the remaining 30% formed the test dataset. This process was repeated five times to calculate the average classification rate of the five-fold sets. The gradient-weighted class activation mapping method was used to validate the classification results. In the performance analysis of the ResNet18-based classification network for individual MR images, use of the ROI showed excellent detection performance of bone marrow edema with 93.55 +/- 2.19% accuracy, 92.87 +/- 1.27% recall, and 94.69 +/- 3.03% precision. The overall performance was additionally improved using a median filter to reflect the context information. Finally, active sacroiliitis was diagnosed in individual subjects with 96.06 +/- 2.83% accuracy, 100% recall, and 94.84 +/- 3.73% precision. This is a pilot study to diagnose bone marrow edema by deep learning based on MR images, and the results suggest that MR analysis using deep learning can be a useful complementary means for clinicians to diagnose bone marrow edema.
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