Clinical Trial: Development and Validation of Interpretable Machine Learning Models Incorporating Paraspinal Muscle Quality for to Predict Cage Subsidence Risk Followingposterior Lumbar Interbody Fusion

U.S., March 22 -- ClinicalTrials.gov registry received information related to the study (NCT06888739) titled 'Development and Validation of Interpretable Machine Learning Models Incorporating Paraspinal Muscle Quality for to Predict Cage Subsidence Risk Followingposterior Lumbar Interbody Fusion' on March 17.

Brief Summary: The study focuses on identifying risk factors for cage subsidence after posterior lumbar interbody fusion (PLIF) and developing an interpretable machine learning model to predict these risks. It analyzes patients from two large teaching hospitals, using clinical, radiographic, and surgical parameters, including paraspinal muscle indices and bone density markers. A web-based application was developed to facilitate real-t...