Abstract
Background
The increasing demand for lumbar spine MRI exams, combined with radiologist shortages, has underscored the need for automated tools to assist in diagnosing degenerative spinal conditions. Despite the proliferation of AI tools in radiology, no CE or FDA-approved applications currently focus on pathologies in the lumbar spine. This study aimed to develop a pipeline for automatic identification of intervertebral disc herniation, contributing to more efficient clinical decision-making.
Methods
A dataset of 484 lumbar spine MRI examinations acquired from four manufacturers was used to develop a multi-stage deep learning pipeline. An overview of the proposed automated lumbar spine MRI analysis framework is presented, illustrating the main stages of the pipeline including dataset preparation, disc segmentation, feature extraction, and classification. A subset of 165 studies (5,200 sagittal slices) was manually annotated by radiologists and used for training and validation of vertebrae and intervertebral disc segmentation models, including U-Net, DeepLabV3+, and MA-Net with EfficientNet-B2 backbones. Anatomically informed post-processing was applied to assign disc-level labels and extract disc-centered volumes. Disc-level binary classification (herniated vs. non-herniated) was then performed using 3D patch-based models, including ResNet, SENet, EfficientNet-B2, and Vision Transformer (ViT) architectures, with all dataset splits performed at the patient level.
Results
The best segmentation performance was achieved by MA-Net with EfficientNet-B2, yielding a Dice Score of 0.898 for three-class segmentation (background, vertebrae, discs). For classification, the evaluated models achieved F1-scores between 0.66 and 0.73. The best performance was obtained with a ResNet18-based model, indicating that once anatomical localization and disc extraction are addressed by the segmentation stage, lower-capacity architectures can be effective for binary classification. The simplified three-class segmentation improved model robustness, and post-processing enabled disc-level precision essential for downstream classification, which achieved consistent performance across models under the available data regime.
Conclusion
The proposed AI pipeline successfully automates the identification of lumbar disc herniation in MRI exams. This approach has the potential to enhance diagnostic efficiency, reduce radiologist workload, and improve reproducibility in identifying disc pathologies. While limited by sample size and class imbalance, the methodology lays a strong foundation for scalable annotation and integration of AI in routine spinal diagnostics.