CT-based subchondral bone and clinical predictors of long-term total ankle arthroplasty outcomes.

ABSTRACT:

Objective

This study aimed to develop a machine learning-based predictive model for personalized long-term prognosis assessment in patients undergoing total ankle arthroplasty (TAA) by integrating preoperative computed tomography (CT)-derived subchondral bone structural parameters with clinical indicators.

Methods

A retrospective cohort study involving 340 TAA patients was divided into training (n = 238, 70%) and validation (n = 102, 30%) sets through stratified random sampling, ensuring the outcome distribution was preserved. Radiographic features and clinical metrics were systematically collected. Univariate analysis was conducted to identify variables associated with poor prognosis in the training set, followed by feature reduction using the least absolute s

SUBMITTER: Ji W 

PROVIDER: S-EPMC12832619 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature