Publications by authors named "Chune Ma"
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).
Article Synopsis
- Deep learning (DL) has the potential to enhance the diagnosis of cerebral aneurysms by utilizing large multicenter data sets for better segmentation and detection in CT angiography (CTA) images.
- A retrospective collection of CTA images from eight hospitals was used to develop and test a DL model, with performance assessed against radiology reports using the Dice similarity coefficient and receiver operating characteristic curves.
- The model demonstrated a high accuracy with a DSC of 0.87 for segmentation and 85.7% sensitivity in detecting aneurysms, performing comparably to traditional radiology assessments and processing scans relatively quickly in 1.76 minutes.
Background: Progression of non-target lesions (NTLs) after stenting has been reported and is associated with the triggering of an inflammatory response. The perivascular fat attenuation index (FAI) may be used as a novel imaging biomarker for the direct quantification of coronary inflammation.
Objectives: To investigate whether FAI values can help identify changes in inflammation status in patients undergoing stent implantation, especially in NTLs.
Objective: To investigate the diagnostic performance of deep learning (DL)-based vascular extraction and stenosis detection technology in assessing coronary artery disease (CAD).
Methods: The diagnostic performance of DL technology was evaluated by retrospective analysis of coronary computed tomography angiography in 124 suspected CAD patients, using invasive coronary angiography as reference standard. Lumen diameter stenosis ≥50% was considered obstructive, and the diagnostic performances were evaluated at per-patient, per-vessel and per-segment levels.