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In spinal patients with spondylolisthesis, timely diagnosis and early surgical intervention can enhance the overall treatment course and attenuate possible complications. The diagnosis of spondylolisthesis cases typically involves the assessment of MRI scans by a trained radiologist. With the escalating volume of medical images, automating radiology report reading becomes indispensable in identifying spondylolisthesis cases effectively and rapidly creating ground truth for further research studies. Therefore, we present a novel framework leveraging a large language model (LLM), i.e., ChatGPT (GPT4-based), for automated assessment of the presence of spinal spondylolisthesis by parsing spinal MRI radiology reports. Through rigorous evaluation on a cohort of 166 subjects undergoing spinal fusion surgery, comprising 83 cases of spondylolisthesis and 83 non-spondylolisthesis cases, our proposed framework demonstrated exceptional performance metrics with 94% sensitivity, 98.8% specificity, 97.5% precision, and 96.4% accuracy. The implementation of this algorithm effectively recognizes spondylolisthesis diagnoses by automatically reading radiology reports.
Moallem et al. (2024) studied this question.