Automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified YOLO framework
BackgroundThe high prevalence of low back pain has led to an increasing demand for the analysis of lumbar magnetic resonance (MR) images. This study aimed to develop and evaluate a deep-learning-assisted automated system for diagnosing and grading lumbar intervertebral disc degeneration based on lum...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1526478/full |
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| author | Aobo Wang Tianyi Wang Xingyu Liu Xingyu Liu Xingyu Liu Ning Fan Shuo Yuan Peng Du Congying Zou Ruiyuan Chen Yu Xi Zhao Gu Hongxing Song Qi Fei Yiling Zhang Yiling Zhang Lei Zang |
| author_facet | Aobo Wang Tianyi Wang Xingyu Liu Xingyu Liu Xingyu Liu Ning Fan Shuo Yuan Peng Du Congying Zou Ruiyuan Chen Yu Xi Zhao Gu Hongxing Song Qi Fei Yiling Zhang Yiling Zhang Lei Zang |
| author_sort | Aobo Wang |
| collection | DOAJ |
| description | BackgroundThe high prevalence of low back pain has led to an increasing demand for the analysis of lumbar magnetic resonance (MR) images. This study aimed to develop and evaluate a deep-learning-assisted automated system for diagnosing and grading lumbar intervertebral disc degeneration based on lumbar T2-weighted sagittal and axial MR images.MethodsThis study included a total of 472 patients who underwent lumbar MR scans between January 2021 and November 2023, with 420 in the internal dataset and 52 in the external dataset. The MR images were evaluated and labeled by experts according to current guidelines, and the results were considered the ground truth. The annotations included the Pfirrmann grading of disc degeneration, disc herniation, and high-intensity zones (HIZ). The automated diagnostic model was based on the YOLOv5 network, modified by adding an attention module in the Cross Stage Partial part and a residual module in the Spatial Pyramid Pooling-Fast part. The model’s diagnostic performance was evaluated by calculating the precision, recall, F1 score, and area under the receiver operating characteristic curve.ResultsIn the internal test set, the model achieved precisions of 0.78–0.91, 0.90–0.92, and 0.82 and recalls of 0.86–0.91, 0.90–0.93, and 0.81–0.88 for disc degeneration grading, disc herniation diagnosis, and HIZ detection, respectively. In the external test set, the precision values for disc degeneration grading, herniation diagnosis, and HIZ detection were 0.73–0.87, 0.86–0.92, and 0.74–0.84 and recalls were 0.79–0.87, 0.88–0.91, and 0.77–0.78, respectively.ConclusionThe proposed model demonstrated a relatively high diagnostic and classification performance and exhibited considerable consistency with expert evaluation. |
| format | Article |
| id | doaj-art-187396c7727844babdbad8ec53654919 |
| institution | Kabale University |
| issn | 2296-4185 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj-art-187396c7727844babdbad8ec536549192025-01-22T07:12:01ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852025-01-011310.3389/fbioe.2025.15264781526478Automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified YOLO frameworkAobo Wang0Tianyi Wang1Xingyu Liu2Xingyu Liu3Xingyu Liu4Ning Fan5Shuo Yuan6Peng Du7Congying Zou8Ruiyuan Chen9Yu Xi10Zhao Gu11Hongxing Song12Qi Fei13Yiling Zhang14Yiling Zhang15Lei Zang16Department of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, ChinaDepartment of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, ChinaSchool of Life Sciences, Tsinghua University, Beijing, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, ChinaInstitute of Biomedical and Health Engineering (iBHE), Tsinghua Shenzhen International Graduate School, Shenzhen, ChinaDepartment of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, ChinaDepartment of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, ChinaDepartment of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, ChinaDepartment of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, ChinaDepartment of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, ChinaDepartment of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, ChinaLongwood Valley Medical Technology Co. Ltd, Beijing, ChinaDepartment of Orthopedics, Beijing Shijitan Hospital, Capital Medical University, Beijing, ChinaDepartment of Orthopedics, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, ChinaLongwood Valley Medical Technology Co. Ltd, Beijing, ChinaDepartment of Orthopedics, Beijing Chaoyang Hospital, Capital Medical University, Beijing, ChinaBackgroundThe high prevalence of low back pain has led to an increasing demand for the analysis of lumbar magnetic resonance (MR) images. This study aimed to develop and evaluate a deep-learning-assisted automated system for diagnosing and grading lumbar intervertebral disc degeneration based on lumbar T2-weighted sagittal and axial MR images.MethodsThis study included a total of 472 patients who underwent lumbar MR scans between January 2021 and November 2023, with 420 in the internal dataset and 52 in the external dataset. The MR images were evaluated and labeled by experts according to current guidelines, and the results were considered the ground truth. The annotations included the Pfirrmann grading of disc degeneration, disc herniation, and high-intensity zones (HIZ). The automated diagnostic model was based on the YOLOv5 network, modified by adding an attention module in the Cross Stage Partial part and a residual module in the Spatial Pyramid Pooling-Fast part. The model’s diagnostic performance was evaluated by calculating the precision, recall, F1 score, and area under the receiver operating characteristic curve.ResultsIn the internal test set, the model achieved precisions of 0.78–0.91, 0.90–0.92, and 0.82 and recalls of 0.86–0.91, 0.90–0.93, and 0.81–0.88 for disc degeneration grading, disc herniation diagnosis, and HIZ detection, respectively. In the external test set, the precision values for disc degeneration grading, herniation diagnosis, and HIZ detection were 0.73–0.87, 0.86–0.92, and 0.74–0.84 and recalls were 0.79–0.87, 0.88–0.91, and 0.77–0.78, respectively.ConclusionThe proposed model demonstrated a relatively high diagnostic and classification performance and exhibited considerable consistency with expert evaluation.https://www.frontiersin.org/articles/10.3389/fbioe.2025.1526478/fulldeep learningdiagnosismagnetic resonance imagingartificial intelligenceintervertebral disc degeneration |
| spellingShingle | Aobo Wang Tianyi Wang Xingyu Liu Xingyu Liu Xingyu Liu Ning Fan Shuo Yuan Peng Du Congying Zou Ruiyuan Chen Yu Xi Zhao Gu Hongxing Song Qi Fei Yiling Zhang Yiling Zhang Lei Zang Automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified YOLO framework Frontiers in Bioengineering and Biotechnology deep learning diagnosis magnetic resonance imaging artificial intelligence intervertebral disc degeneration |
| title | Automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified YOLO framework |
| title_full | Automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified YOLO framework |
| title_fullStr | Automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified YOLO framework |
| title_full_unstemmed | Automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified YOLO framework |
| title_short | Automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified YOLO framework |
| title_sort | automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified yolo framework |
| topic | deep learning diagnosis magnetic resonance imaging artificial intelligence intervertebral disc degeneration |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1526478/full |
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