ArticleJournal of cardiovascular development and disease2022
Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm.
Article in Journal of cardiovascular development and disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 57 citations in OpenAlex.
- Twelve Fused Models by Fusing Four Types of Transformer-Based Tuners on Three Base UNet Architectures for Carotid Wall Segmentation and Plaque Burden/Intima-Media Thickness Measurements in Ultrasound Scans: A Scientific Validation Study.Diagnostics (Basel, Switzerland) · 2026Article
- An open B-mode ultrasound database for deep learning-based atherosclerotic plaque segmentation.Scientific data · 2026Article
- Angio-fusion net: dual-stream enhanced VGG16 attention U-Net for vessel morphology preservation in XCA segmentation.Frontiers in cardiovascular medicine · 2026Article
- Performance Comparison of U-Net and Its Variants for Carotid Intima-Media Segmentation in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2025Article
- MHAHF-UNet: a multi-scale hybrid attention hierarchy fusion network for carotid artery segmentation.International journal of computer assisted radiology and surgery · 2025Article
- Machine learning-based classification of carotid plaques via ultrasound: a systematic review and meta-analysis of diagnostic performance.International journal of emergency medicine · 2025Review
- Automatic quantitative analysis of atherosclerotic aortic plaques in patients with embolic cerebral infarction using deep learning.The Korean journal of internal medicine · 2025Article
- Image guided construction of a common coordinate framework for spatial transcriptome data.Scientific reports · 2025Article
- Transformer and Attention-Based Architectures for Segmentation of Coronary Arterial Walls in Intravascular Ultrasound: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning-based multimodal risk stratification for atherosclerosis management.Archives of medical science : AMS · 2025Article
- Deep Learning-Based Carotid Plaque Ultrasound Image Detection and Classification Study.Reviews in cardiovascular medicine · 2024Article
- Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.EClinicalMedicine · 2024Article
- Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.The international journal of cardiovascular imaging · 2024Article
- COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.Frontiers in artificial intelligence · 2024Article
- Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.Journal of cardiovascular development and disease · 2023Review
- Integrative Approaches in Acute Ischemic Stroke: From Symptom Recognition to Future Innovations.Biomedicines · 2023Review
- Cardiovascular disease/stroke risk stratification in deep learning framework: a review.Cardiovascular diagnosis and therapy · 2023Review
- Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm.Diagnostics (Basel, Switzerland) · 2023Article
- Research of segmentation recognition of small disease spots on apple leaves based on hybrid loss function and CBAM.Frontiers in plant science · 2023Article
- Artificial intelligence in atherosclerotic disease: Applications and trends.Frontiers in cardiovascular medicine · 2022Review
Corrections and comments
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Authors and funding
9 authors at 7 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.
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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.