التفاصيل البيبلوغرافية
| العنوان: |
Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning |
| المؤلفون: |
Kang Hee Lee, Sang Tae Choi, Guen Young Lee, You Jung Ha, Sang-Il Choi |
| المصدر: |
Diagnostics, Vol 11, Iss 7, p 1156 (2021) |
| بيانات النشر: |
MDPI AG, 2021. |
| سنة النشر: |
2021 |
| المجموعة: |
LCC:Medicine (General) |
| مصطلحات موضوعية: |
axial spondyloarthritis, bone marrow edema, sacroiliitis, magnetic resonance imaging, deep learning, Medicine (General), R5-920 |
| الوصف: |
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. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2075-4418 |
| Relation: |
https://www.mdpi.com/2075-4418/11/7/1156; https://doaj.org/toc/2075-4418 |
| DOI: |
10.3390/diagnostics11071156 |
| URL الوصول: |
https://doaj.org/article/2107c2ca89cb4e3e9f6819d08a852f4b |
| رقم الانضمام: |
edsdoj.2107c2ca89cb4e3e9f6819d08a852f4b |
| قاعدة البيانات: |
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