ArticleCardiovascular diagnosis and therapy2019
Ultrasound-based carotid stenosis measurement and risk stratification in diabetic cohort: a deep learning paradigm.
Article in Cardiovascular diagnosis and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Current Applications and Future Perspectives of Artificial and Biomimetic Intelligence in Vascular Surgery and Peripheral Artery Disease.Biomimetics (Basel, Switzerland) · 2024Review
- COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.Frontiers in artificial intelligence · 2024Article
- Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.Diagnostics (Basel, Switzerland) · 2023Article
- Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0.Computers in biology and medicine · 2022Article
- COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans.Diagnostics (Basel, Switzerland) · 2022Article
- COVLIAS 1.0Diagnostics (Basel, Switzerland) · 2022Article
- Unseen Artificial Intelligence-Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study.Diagnostics (Basel, Switzerland) · 2021Article
- Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography.Diagnostics (Basel, Switzerland) · 2021Article
- COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models.Diagnostics (Basel, Switzerland) · 2021Article
- Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application.Annals of translational medicine · 2021Review
- Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes.Circulation research · 2021Review
- Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants study.The international journal of cardiovascular imaging · 2021Article
- Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review.World journal of diabetes · 2021Review
- Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D optimization paradigm on a weak training brain magnetic resonance imaging datasets: a supercomputer application.Medical & biological engineering & computing · 2021Article
- Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort.Journal of medical systems · 2020Article
- Significance of high resolution MRI in the identification of carotid plaque.Experimental and therapeutic medicine · 2020Article
- COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review.Computers in biology and medicine · 2020Review
- Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models.Cardiovascular diagnosis and therapy · 2020Article
- Ultrasound-based stroke/cardiovascular risk stratification using Framingham Risk Score and ASCVD Risk Score based on "Integrated Vascular Age" instead of "Chronological Age": a multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts.Cardiovascular diagnosis and therapy · 2020Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStroke is in the top three leading causes of death worldwide. Non-invasive monitoring of stroke can be accomplished via stenosis measurements. The current conventional image-based methods for these measurements are not accurate and reliable. They do not incorporate shape and intelligent learning component in their design.
methodsIn this study, we propose a deep learning (DL)-based methodology for accurate measurement of stenosis in common carotid artery (CCA) ultrasound (US) scans using a class of AtheroEdge system from AtheroPoint, USA. Three radiologists manually traced the lumen-intima (LI) for the near and the far walls, respectively, which served as a gold standard (GS) for training the DL-based model. Three DL-based systems were developed based on three types of GS.
resultsIRB approved (Toho University, Japan) 407 US scans from 204 patients were collected. The risk was characterized into three classes: low, moderate, and high-risk. The area-under-curve (AUC) corresponding to three DL systems using receiver operating characteristic (ROC) analysis computed were: 0.90, 0.94 and 0.86, respectively.
conclusionsNovel DL-based strategy showed reliable, accurate and stable stenosis severity index (SSI) measurements.
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