Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
570-06-204 ISMRM Abstract

Deep Learning Framework for Automated Reporting of Degenerative Changes in the Lumbar Spine from MRI

Primary: Analysis Methods - Multi-Modal Learning with LLMs/VLMs
Secondary: Analysis Methods - Software Tools
570-06-204 · Vision and Language Models in MRI · Wednesday, 13 May, 2:35 PM–3:30 PM · Traditional Posters | Exhibition Hall
Keywords: LLM Automatic reporting Lumbar spine Automated diagnosis Deep generative model
Accepted
Yao-Wen Liang 1,2, Cheng-Ru Yang1,3, Ssu-Ju Li1, Ching-Wen Chang1, Ting-Chun Lin1,4, Wei-Der Chung5, Chao-Hung Kuo1,6,7, You-Yin Chen1,2
1Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
2Ph.D. Program In Medical Neuroscience, Taipei Medical University, Taipei, Taiwan
3National Yang Ming Chiao Tung University, Taipei, Taiwan
4RadiRad Co., Ltd, Taiwan
5Industrial Technology Research Institute, Taiwan
6School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
7Department of Neurosurgery, Taipei Veterans General Hospital, Taipei, Taiwan
Presenting Author: Yao-Wen Liang

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References

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2. Järvinen, J., Lumbar degenerative Modic changes: association between bone turnover and low back symptoms, and the effect of a zoledronic acid treatment intervention. 2020.
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4. Kirnaz, S., et al., Fundamentals of intervertebral disc degeneration. World neurosurgery, 2022. 157: p. 264–273.
5. Saifee, T., et al., Spinal column and spinal cord disorders. Neurology: a queen square textbook, 2024: p. 463–498.
6. Kim, G.-U., et al., Diagnostic technology for spine pathology. Asian Spine Journal, 2022. 16(5): p. 764.
7. Yu, L.-P., et al., MRI assessment of lumbar intervertebral disc degeneration with lumbar degenerative disease using the Pfirrmann grading systems. PloS one, 2012. 7(12): p. e48074.
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11. Zou, Y., et al., Predicting anatomical variations in radiotherapy with a vector quantized variational autoencoder generative model. Medical Physics, 2025. 52(9): p. e18120.
12. Hu, R., et al., Realistic Tumor generation using 3D conditional latent diffusion model. 2024, Society of Nuclear Medicine.
13. Sudirman, S., et al., Lumbar spine MRI dataset. Mendeley Data, 2019. 2: p. 2019.
14. Hatamizadeh, A., et al. Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. in International MICCAI brainlesion workshop. 2021. Springer.
15. Brunet, D., E.R. Vrscay, and Z. Wang, On the mathematical properties of the structural similarity index. IEEE Transactions on Image Processing, 2011. 21(4): p. 1488–1499.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/570-06-204