Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion.

ABSTRACT: Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. In this study, we developed a fully computer-supported pipeline to predict both the cage height and the degree of lumbar lordosis subtraction from the pelvic incidence (PI-LL) after TLIF surgery, utilizing preoperative X-ray images. The automated pipeline comprised two primary stages. First, the pretrained BiLuNet deep learning model was employed to extract essential features from X-ray images. Subsequently, five machine learning algorithms were trained using a five-fold cross-validation technique on a dataset of 311 patients to identify the optimal models to predict interbody cage height and postoperative PI-LL. LASSO regression and support vector regression demonstrated s

SUBMITTER: Bui AT 

PROVIDER: S-EPMC10885900 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature