Machine Learning Prediction of Therapeutic Outcome After Transforaminal Epidural Steroid Injection for Radiculopathy from Herniated Lumbar Disc

A deep learning-based predictive model demonstrated superior performance in predicting therapeutic outcomes after lumbar TFESI in patients with HLD-related radiculopathy. Integration of routine clinical and magnetic resonance imaging (MRI)-derived features into ML algorithms may enhance individualized prognostication and assist clinicians in optimizing patient selection for interventional procedures. To the best of our knowledge, this is the first study to develop an ML-based model integrating routine clinical variables with MRI findings for the prediction of TFESI outcomes in HLD-related radiculopathy. Nevertheless, the study is limited by its single-center retrospective design, lack of external validation, and reliance on MRI assessments performed by a single rater. Future multicenter studies are warranted to improve generalizability and confirm clinical utility.