Interpretable machine learning models based on shear-wave elastography radiomics for predicting cardiovascular disease in diabetic kidney disease patients.

ABSTRACT:

Background

The risk of cardiovascular complications is significantly elevated in patients with diabetic kidney disease (DKD). Recognizing the link between the progression of DKD and an increased risk of cardiovascular disease (CVD), it is crucial to focus on the early prediction and management of CVD risk factors among these patients to potentially enhance their health outcomes.

Objective

This study sought to bridge the existing gap by developing and validating machine learning (ML) models that utilize clinical data and shear wave elastography (SWE) radiomics features to identify patients at risk of CVD, ultimately aiming to improve the management of DKD.

Materials and methods

This study conducted a retrospective analysis of 586 patients with DKD, dividing them into t

SUBMITTER: Dai R 

PROVIDER: S-EPMC11527807 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature