ArticleScientific reports2025
An algorithm for cardiac disease detection based on the magnetic resonance imaging.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Standardizing Vegetation Size Measurement in Native Left-Sided Infective Endocarditis Using Artificial Intelligence.Journal of clinical medicine · 2026Article
- Automated detection of fetal vascular malperfusion via data augmentation and algorithm improvement.Scientific reports · 2026Article
- Surface defect detection method for shawo radishes based on improved RT-DETR.Frontiers in nutrition · 2026Article
- In Vivo Dynamic Coronary Arteries Blood Flow Imaging Based on Multi-Cycle Phase Clustering Ultrafast Ultrasound.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Enhancing cardiac disease detection via a fusion of machine learning and medical imaging.Scientific reports · 2025Article
- AI-Assisted surgical vision: evaluating YOLOv8 and YOLOv12 for real-time detection in colon cancer surgery.Frontiers in surgery · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
In experiments to detect heart disease on cardiac magnetic resonance imaging (MRI) medical images, existing object detection models face several challenges including low accuracy and unreliable detection results. To tackle these issues, this article proposes an innovative method for Object Detection in cardiac MRI medical images called SA-YOLO. This method is based on the YOLOv8 model but introduces several key modifications. Firstly, the standard Spatial Pyramid Pooling Fast module is replaced with a Multi-Channel Spatial Pyramid Pooling module. Secondly, an attention mechanism combining the ideas of Squeeze-Excitation and Coordinate Attention designed, and integrated into the Neck part of the baseline model. Subsequently, the bounding box regression loss function CIoU loss of the model was replaced with the iSD-IoU loss that combines shape loss and distance loss. Finally, comparative experiments were conducted on the Automated Cardiac Diagnosis Challenge cardiac MRI image dataset where it was found that SA-YOLOv8 achieved better results in detecting cardiac pathologies, and improvement of 7.4% in mAP0.5 value and 5.1% in mAP0.5-0.95 value compared to the baseline model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.