Abstract #0380

Deep learning-based computer-aided diagnostic system for lumbar degenerative diseases classification using MRI

Yueyao Chen¹, Qiangtai Huang^2,3, Chu Zhang^2,3, Junfeng Li¹, Wensheng Huang⁴, Peiyin Luo¹, Qiuyi Chen¹, Ruirui Qi¹, Yuxuan Wan², Bingsheng Huang^2,3, Zhenhua Gao⁵, Xiaofeng Lin⁶, Songxiong Wu⁷, and Xianfen Diao³

¹Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital (The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine), Shenzhen, China, ²Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, ³National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Medical School, Shenzhen University, Shenzhen, China, ⁴Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China, ⁵Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China, ⁶Department of Nuclear Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China, ⁷Radiology Department, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Shenzhen, China

Synopsis

Keywords: Diagnosis/Prediction, AI/ML Software, Spine, Lumbar degenerative diseases

Motivation: Assisting radiologists in the diagnosis of degenerative diseases of the lumbar spine and reducing the workload of physicians.

Goal(s): Building a deep learning-based CAD system for lumbar degenerative diseases to address the limitations of existing models and provide a more powerful and clinically relevant tool.

Approach: Retrospective analysis of lumbar magnetic resonance imaging data and development of a lumbar CAD system for lumbar disc localization, binary classification, and multi-label diagnosis of degenerative diseases.

Results: This lumbar CAD system achieves high disc localization success and classification accuracy in seven lumbar spine lesions.

Impact: Our study demonstrates the feasibility of using deep learning to classify multiple lumbar spine diseases with strong performance, highlighting the potential of our CAD system to reduce physician workload in clinical applications.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.