Aobo Wang ¹ Tianyi Wang ^1* Xingyu Liu ^2,3,4* Ning Fan ¹ Shuo Yuan ¹ Peng Du ^1* Congying Zou ^1* Ruiyuan Chen ¹ Yu Xi ¹ Zhao Gu ^5* Hongxing Song ^6* Qi Fei ⁷ Yiling Zhang ^3,5* Lei Zang ^1*

• ¹ Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
• ² School of Life Sciences, Tsinghua University, Beijing, Beijing Municipality, China
• ³ Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
• ⁴ Institute of Biomedical and Health Engineering (iBHE), Tsinghua Shenzhen International Graduate School, Beijing, China
• ⁵ Longwood Valley Medical Technology Co. Ltd, Beijing, China
• ⁶ Beijing Shijitan Hospital, Capital Medical University, Beijing, Beijing Municipality, China
• ⁷ Beijing Friendship Hospital, Capital Medical University, Beijing, Beijing Municipality, China

Background: The 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. Methods: This 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. Results: In 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. Conclusions: The proposed model demonstrated a relatively high diagnostic and classification performance and exhibited considerable consistency with expert evaluation.