Ischemic stroke prediction of patients with carotid atherosclerotic stenosis via multi-modality fused network

Abstract

Carotid atherosclerotic stenosis of the carotid artery is an important cause of ischemic cerebrovascular disease. The aim of this study was to predict the presence or absence of clinical symptoms in unknown patients by studying the existence or lack of symptoms of patients with carotid atherosclerotic stenosis. First, a deep neural network prediction model based on brain MRI imaging data of patients with multiple modalities is constructed; it uses the multi-modality features extracted from the neural network as inputs and the incidence of diagnosis as output to train the model. Then, a machine learning-based classification algorithm is developed to utilize the clinical features for comparison and evaluation. The experimental results showed that the deep learning model using imaging data could better predict the clinical symptom classification of patients. As part of preventive medicine, this study could help patients with carotid atherosclerosis narrowing to prepare for stroke prevention based on the prediction results.

A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke

“…ADC modality performed relatively worse, being 11.6% lower than the FLAIR modality, which exhibited better performance. The AUC values for the three algorithms were 50.6, 64.8, and 66.8%, with XGBoost achieving an AUC of 66.8% ( 92 ).…”

Quantitative Upper Limb Impairment Assessment for Stroke Rehabilitation: A Review

“…Although the introduction of multi-modal fusion has advanced the development of impairment assessment, it also brings some challenges, such as data heterogeneity and feature fusion. However, Lv et al [89] improved the multi-modal features fusion by representing data from different modules as low-dimensional semantic vectors, providing a potential solution for current challenges. In summary, the potential of multi-modal fusion from various sensors offers more effective rehabilitation assessments and personalized rehabilitation plans for stroke patients.…”