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

Lumbar degenerative diseases (LDDs) are prevalent orthopedic conditions worldwide, presenting significant diagnostic challenges due to high patient volumes in many healthcare institutions. To address this challenge, we developed Lumbar CAD, a deep learning-based computer-aided diagnostic (CAD) system for classifying LDDs using dual-view MRI. A lumbar disc localization and region extraction method based on nnUNetv2 was implemented to extract disc-level images from patient-level MRI data. Subsequently, sagittal lumbar disc images were analyzed using a binary classification model to predict degenerative discs. Axial images were then processed using a multi-label classification model to classify seven distinct types of lumbar disc lesions. To enhance system development efficiency, labels for lumbar degenerative diseases were obtained from clinical diagnostic reports instead of requiring re-annotation by radiologists. Data from two medical centers were collected for training and validation of the Lumbar CAD system. The results showed that the Lumbar CAD system achieved patient-level disc localization success rates of 96.7 % and 98.7 %, and disc-level multi-label classification accuracies of 0.890 and 0.879 on the internal and external test sets, respectively. Additionally, on the internal test set, the system per-patient LDD diagnoses approximately 120 times faster than experienced radiologists (1.09 ± 0.01 s vs. 132.41 ± 63.22 s per case). These findings highlight the potential of Lumbar CAD to assist clinicians in the accurate and efficient diagnosis of LDDs.