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Updated: Jan 11, 2026
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GPT4LFS (generative pretrained transformer 4 omni for lumbar foramina stenosis): enhancing lumbar foraminal stenosis
Elzat Elham-Yilizati Yilihamu1, Fan-Shuo Zeng2, Jun Shang3
1Orthopedic Research Center of Shandong University & Advanced Medical Research Institute, Shandong University, Jinan 250000, China.
Background Context:
Lumbar foraminal stenosis (LFS) is a common spinal condition that requires accurate assessment. Current magnetic resonance imaging (MRI) reporting processes are often inefficient, and while deep learning has potential for improvement, challenges in generalization and interpretability limit its diagnostic effectiveness compared to physician expertise.
Purpose:
The present study aimed to leverage a multimodal large language model to improve the accuracy and efficiency of LFS image classification, thereby enabling rapid and precise automated diagnosis, reducing the dependence on manually annotated data, and enhancing diagnostic efficiency.
Study Design/Setting:
Retrospective study conducted from April 2017 to March 2023.
Patient Sample:
Sagittal T1-weighted MRI data for the lumbar spine were collected from 1,200 patients across 3 medical centers. A total of 810 patient cases were included in the final analysis, with data collected from 7 different MRI devices.
Outcome Measures:
Automated classification of LFS using the multi modal large language model. Accuracy, sensitivity, Specificity and Cohen's Kappa coefficient were calculated.
Methods:
An advanced multimodal fusion framework GPT4LFS was developed with the primary objective of integrating imaging data and natural language descriptions to comprehensively capture the complex LFS features. The model employed a pretrained ConvNeXt as the image processing module for extracting high-dimensional imaging features. Concurrently, medical descriptive texts generated by the multimodal large language model GPT-4o and encoded and feature-extracted using RoBERTa were utilized to optimize the model's contextual understanding capabilities. The Mamba architecture was implemented during the feature fusion stage, effectively integrating imaging and textual features and thereby enhancing the performance of the classification task. Finally, the stability of the model's detection results was validated by evaluating classification task metrics, such as the accuracy, sensitivity, specificity, and Kappa coefficients.
Results:
The training set comprised 6,299 images from 635 patients, the internal test set included 820 images from 82 patients, and the external test set was composed of 930 images from 93 patients. The GPT4LFS model demonstrated an overall accuracy of 93.7%, sensitivity of 95.8%, and specificity of 94.5% in the internal test set (Kappa=0.89, 95% confidence interval (CI): 0.84-0.96, p<.001). In the external test set, the overall accuracy was 92.2%, with a sensitivity of 92.2% and a specificity of 97.4% (Kappa=0.88, 95% CI: 0.84-0.89, p<.001). Both the internal and external test sets showed excellent consistency in the model. The code is freely accessible on GitHub at the following repository: https://github.com/ElzatElham/GPT4LFS.
Conclusions:
Using the GPT4LFS model for LFS image categorization demonstrated accuracy and the capacity for feature description at a level commensurate with that of professional clinicians.
