Prediction of recurrent ischaemic stroke using radiomics data and machine learning methods in patients with acute ischaemic stroke: protocol for a multicentre, large sample, prospective observational cohort study in China.

ABSTRACT:

Introduction

Stroke is a leading cause of mortality and disability worldwide. Recurrent strokes result in prolonged hospitalisation and worsened functional outcomes compared with the initial stroke. Thus, it is critical to identify patients who are at high risk of stroke recurrence. This study is positioned to develop and validate a prediction model using radiomics data and machine learning methods to identify the risk of stroke recurrence in patients with acute ischaemic stroke (AIS).

Methods and analysis

A total of 1957 patients with AIS will be needed. Enrolment at participating hospitals will continue until the required sample size is reached, and we will recruit as many participants as possible. Multiple indicators including basic clinical data, image data, laboratory d

SUBMITTER: Li J 

PROVIDER: S-EPMC10565242 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature