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Artificial Intelligence in Cardiovascular Diseases (2024)
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Artificial Intelligence in Cardiovascular Diseases (2024)

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 3591

Special Issue Editor

Dr. Jasjit S. Suri

E-Mail Website
Guest Editor
1. Stroke Diagnostic and Monitoring Division, AtheroPoint LLC, Roseville, CA 95661, USA
2. Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
Interests: AI (artificial intelligence); medical imaging (ultrasound, MRI, CT); computer-aided diagnosis; machine learning; deep learning; hybrid deep learning; cardiovascular/stroke risk
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cardiovascular disease (CVD) is the leading cause of global mortality and morbidity, which accounts for the deaths of 17.9 million people globally every year. It is generally accepted that cardiovascular diseases include a variety of diseases, such as coronary artery disease, coronary heart disease, cerebrovascular disease, and myocardial infarction. Several factors can contribute to the initiation and progression of CVD. These factors include traditional risk factors, image-based risk factors, and genetic factors. Early diagnostic methods and the implementation of appropriate treatment plans, as well as lifestyle modifications, are some of the most effective means of preventing CVD-related deaths. The field of artificial intelligence (AI), which has been gaining momentum over the past few years, is revolutionizing medical diagnosis and has demonstrated promising results in preventing cardiovascular diseases. A new generation of CVD prevention diagnostic tools can be developed through the use of AI-based algorithms that can be automated, accurate, and affordable. An understanding of both physiological signals and imaging techniques, such as invasive and non-invasive imaging modalities, is crucial in order to diagnose and prognose cardiovascular disease. Hence, we cordially invite you to share your innovations and observations in the field of AI and cardiovascular disease with the global medical and research communities. The goal of this Special Issue is to publish comprehensive reviews and original research, as well as information on recent advancements in artificial-intelligence-based diagnostic strategies for preventing cardiovascular disease.

Dr. Jasjit S. Suri
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • diagnosis of cardiovascular disease
  • atherosclerotic cardiovascular disease
  • physiological signals
  • medical imaging
  • machine learning
  • deep learning
  • cardiovascular disease risk assessment

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Published Papers (2 papers)

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Research

18 pages, 19699 KiB  
Article
Enhancing Left Ventricular Segmentation in Echocardiograms Through GAN-Based Synthetic Data Augmentation and MultiResUNet Architecture
by Vikas Kumar, Nitin Mohan Sharma, Prasant K. Mahapatra, Neeti Dogra, Lalit Maurya, Fahad Ahmad, Neelam Dahiya and Prashant Panda
Diagnostics 2025, 15(6), 663; https://doi.org/10.3390/diagnostics15060663 - 9 Mar 2025
Viewed by 852
Abstract
Background: Accurate segmentation of the left ventricle in echocardiograms is crucial for the diagnosis and monitoring of cardiovascular diseases. However, this process is hindered by the limited availability of high-quality annotated datasets and the inherent complexities of echocardiogram images. Traditional methods often [...] Read more.
Background: Accurate segmentation of the left ventricle in echocardiograms is crucial for the diagnosis and monitoring of cardiovascular diseases. However, this process is hindered by the limited availability of high-quality annotated datasets and the inherent complexities of echocardiogram images. Traditional methods often struggle to generalize across varying image qualities and conditions, necessitating a more robust solution. Objectives: This study aims to enhance left ventricular segmentation in echocardiograms by developing a framework that integrates Generative Adversarial Networks (GANs) for synthetic data augmentation with a MultiResUNet architecture, providing a more accurate and reliable segmentation method. Methods: We propose a GAN-based framework that generates synthetic echocardiogram images and their corresponding segmentation masks, augmenting the available training data. The synthetic data, along with real echocardiograms from the EchoNet-Dynamic dataset, were used to train the MultiResUNet architecture. MultiResUNet incorporates multi-resolution blocks, residual connections, and attention mechanisms to effectively capture fine details at multiple scales. Additional enhancements include atrous spatial pyramid pooling (ASPP) and scaled exponential linear units (SELUs) to further improve segmentation accuracy. Results: The proposed approach significantly outperforms existing methods, achieving a Dice Similarity Coefficient of 95.68% and an Intersection over Union (IoU) of 91.62%. This represents improvements of 2.58% in Dice and 4.84% in IoU over previous segmentation techniques, demonstrating the effectiveness of GAN-based augmentation in overcoming data scarcity and improving segmentation performance. Conclusions: The integration of GAN-generated synthetic data and the MultiResUNet architecture provides a robust and accurate solution for left ventricular segmentation in echocardiograms. This approach has the potential to enhance clinical decision-making in cardiovascular medicine by improving the accuracy of automated diagnostic tools, even in the presence of limited and complex training data. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiovascular Diseases (2024))
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32 pages, 4635 KiB  
Article
Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data
by Mrinalini Bhagawati, Sudip Paul, Laura Mantella, Amer M. Johri, Siddharth Gupta, John R. Laird, Inder M. Singh, Narendra N. Khanna, Mustafa Al-Maini, Esma R. Isenovic, Ekta Tiwari, Rajesh Singh, Andrew Nicolaides, Luca Saba, Vinod Anand and Jasjit S. Suri
Diagnostics 2024, 14(17), 1894; https://doi.org/10.3390/diagnostics14171894 - 28 Aug 2024
Cited by 3 | Viewed by 2266
Abstract
Background: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk [...] Read more.
Background: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD. We hypothesize that hybrid deep learning (HDL) will outperform unidirectional deep learning, bidirectional deep learning, and machine learning (ML) paradigms. Methodology: 500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0HDL was scientifically validated using seen and unseen datasets while the reliability and statistical tests were conducted using CST along with p-value significance. The performance of AtheroEdge™ 3.0HDL was evaluated by measuring the p-value and area-under-the-curve for both seen and unseen data. Results: The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the seen datasets. The ML feature extraction analysis showed 70% of common features among all three methods. The generalization of AtheroEdge™ 3.0HDL showed less than 1% (p-value < 0.001) difference between seen and unseen data, complying with regulatory standards. Conclusions: The hypothesis for AtheroEdge™ 3.0HDL was scientifically validated, and the model was tested for reliability and stability and is further adaptable clinically. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiovascular Diseases (2024))
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