Degenerative conditions of the lumbar spine, such as neural foraminal narrowing, subarticular stenosis, and spinal canal stenosis, are common and can lead to chronic pain and disability. Detecting and assessing the severity of these conditions is a key task in radiology, often requiring considerable expertise. In this work, we aim to develop automated models that aid radiologists in diagnosing these conditions using lumbar spine MR images. We utilize state-of-the-art deep learning methods for both detection and classification tasks, focusing on severity classification across five intervertebral disc levels and three different imaging modalities. The dataset and methodology used in this study are part of the RSNA 2024 Lumbar Spine Degenerative Classification competition. Our approach involves a two-step process, combining object detection with YOLO v8 and severity classification using a DeepScoreNet architecture. Through careful cross-validation and model evaluation, we demonstrate the potential of these models to improve diagnostic accuracy and efficiency. The DeepScoreNet model, introduced in this article, comprises 2,787,651 parameters and achieved accuracies of 81% for score(severity) prediction in cases of Neural Foraminal Narrowing, 89% for Spinal Canal Stenosis, and 74% for Subarticular Stenosis.

The code for this approach is available in the GitHub repository.(https://github.com/liamirpy/Lumbar-Spine-Degenerative-Classification)

The authors have declared no competing interest.

This study did not receive any funding

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