# The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset

arXiv: https://arxiv.org/abs/2506.09162 · HTML: https://mlsanity.com/paper/2506.09162/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 8 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Built on by (most cited first, 6 of 6)

- [A multi-agent system for spine MRI report generation from multi-sequence imaging](https://mlsanity.com/paper/2606.08897/) (2026)
- [SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus](https://mlsanity.com/paper/2510.03160/) (2025)
- [PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis](https://mlsanity.com/paper/2607.19696/) (2026)
- [Disc-Centric Contrastive Learning for Lumbar Spine Severity Grading](https://mlsanity.com/paper/2602.05738/) (2026)
- [Rendering Novel Views of MRI Using 3D Gaussian Splatting](https://mlsanity.com/paper/2606.26236/) (2026)
- [Beyond Fluency: A Clinical Benchmark and Anomaly-Enhanced Baseline for Spine MRI Report Generation](https://mlsanity.com/paper/2608.07117/) (2026)

## Abstract (arXiv)

The Radiological Society of North America (RSNA) Lumbar Degenerative Imaging Spine Classification (LumbarDISC) dataset is the largest publicly available dataset of adult MRI lumbar spine examinations annotated for degenerative changes. The dataset includes 2,697 patients with a total of 8,593 image series from 8 institutions across 6 countries and 5 continents. The dataset is available for free for non-commercial use via Kaggle and RSNA Medical Imaging Resource of AI (MIRA). The dataset was created for the RSNA 2024 Lumbar Spine Degenerative Classification competition where competitors developed deep learning models to grade degenerative changes in the lumbar spine. The degree of spinal canal, subarticular recess, and neural foraminal stenosis was graded at each intervertebral disc level in the lumbar spine. The images were annotated by expert volunteer neuroradiologists and musculoskeletal radiologists from the RSNA, American Society of Neuroradiology, and the American Society of Spine Radiology. This dataset aims to facilitate research and development in machine learning and lumbar spine imaging to lead to improved patient care and clinical efficiency.