Deep learning model for automated diagnosis of moyamoya disease based on magnetic resonance angiography.

ABSTRACT:

Background

This study explores the potential of the deep learning-based convolutional neural network (CNN) to automatically recognize MMD using MRA images from atherosclerotic disease (ASD) and normal control (NC).

Methods

In this retrospective study in China, 600 participants (200 MMD, 200 ASD and 200 NC) were collected from one institution as an internal dataset for training and 60 from another institution were collected as external testing set for validation. All participants were divided into training (N = 450) and validation sets (N = 90), internal testing set (N = 60), and external testing set (N = 60). The input to the CNN models comprised preprocessed MRA images, while the output was a tripartite classification label that identified the patient's diagnostic group. Th

SUBMITTER: Lu M 

PROVIDER: S-EPMC11570825 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature