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Diagnostics
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Diagnostic AI and Cardiac Diseases
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Diagnostic AI and Cardiac Diseases

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 (31 October 2024) | Viewed by 13439

Special Issue Editors

Dr. Dilber Uzun Ozsahin

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Guest Editor
1. Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
2. Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
Interests: AI in healthcare; decision making in healthcare; medical imaging; nuclear medicine imaging devices
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tahir Yagdi

E-Mail Website
Guest Editor
Department of Cardiovascular Surgery, Ege University, Izmir, Turkey
Interests: heart transplantation; artificial heart; peripheral vascular surgery; heart support devices
Dr. Ilker Ozsahin

E-Mail Website
Guest Editor
1. Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
2.Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey;
Interests: medical imaging; radiology; operational research; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Worldwide, cardiac diseases are the leading cause of mortality. These health conditions affect the heart, as well as the vascular system that supports the heart, brain, and other organs. Unhealthy eating, inactivity, cigarette and alcohol usage are the most significant behavioral risk factors for heart disease. Individuals with these conditions may experience hypertension, hyperglycemia, and elevated cholesterol as a result of behavioral risk factors. These factors can be assessed in primary care settings and point to an elevated risk of consequences, such as heart attack, stroke, and heart failure. Chest pain, angina, respiratory problems, cyanosis, exhaustion, dizziness, and swelling from fluid retention and edema are the signs of heart abnormalities.

Patients with heart diseases have a high death rate mostly due to late diagnosis or misdiagnosis, which results in late treatment. Normally, blood tests, electrocardiogram tests, coronary angiograms, chest X-ray tests, etc. are carried out following diagnosis. Although these tests are used, there is still a gap in early detection, especially when the cardiac condition is in its infant stages. Thus, more effective systems are needed to support physicians in diagnosis. Recently, artificial intelligence (AI) models have been incorporated into clinical cases to offer solutions to various health issues.

Various models have been built for the diagnosis, risk assessment, prognosis, and treatment planning of patients with heart diseases through subsets of AI. The main objectives of AI in cardiac medicine are to enhance patient care, increase effectiveness, and enhance clinical outcomes. Cardiology is well-positioned to benefit from AI, due to the expansion of possible sources of new patient data and improvements in research and treatments. Lately, advances in AI in cardiology have given rise to a variety of uses, including accurate disease classification, the integration of many imaging modalities, ongoing telemonitoring and diagnosis, treatment management, and AI-aided diagnosis.

The goal of this Special Issue is to provide an insight into the state-of-the-art in this field, evaluate various aspects, and identify potential future possibilities for artificial intelligence applications in cardiac disease diagnosis.

Dr. Dilber Uzun Ozsahin
Prof. Dr. Tahir Yagdi
Dr. Ilker Ozsahin
Guest Editors

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

  • technological trends in the diagnosis of congenital heart abnormalities in children
  • application of machine learning in the risk assessment of electrocardiograms
  • the development of new smart healthcare
  • benefits of AI in early cardiac disease detection
  • integration of smart healthcare systems for cardiac diseases
  • enhanced palliative healthcare via AI
  • artificial neural networks in the diagnosis of heart failure
  • prognosis of ischemic heart disease (IHD) using machine learning models
  • novel hybridized models in the early detection of stroke
  • application of AI in congestive heart failure diagnostic techniques analysis

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Published Papers (5 papers)

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Research

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18 pages, 6087 KiB  
Article
Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia
by Shams Ul Haq, Sibghat Ullah Bazai, Ali Fatima, Shah Marjan, Jing Yang, Lip Yee Por, Mohd Anjum, Sana Shahab and Chin Soon Ku
Diagnostics 2023, 13(18), 2867; https://doi.org/10.3390/diagnostics13182867 - 6 Sep 2023
Cited by 2 | Viewed by 1510
Abstract
Arrhythmia is a cardiac condition characterized by an irregular heart rhythm that hinders the proper circulation of blood, posing a severe risk to individuals’ lives. Globally, arrhythmias are recognized as a significant health concern, accounting for nearly 12 percent of all deaths. As [...] Read more.
Arrhythmia is a cardiac condition characterized by an irregular heart rhythm that hinders the proper circulation of blood, posing a severe risk to individuals’ lives. Globally, arrhythmias are recognized as a significant health concern, accounting for nearly 12 percent of all deaths. As a result, there has been a growing focus on utilizing artificial intelligence for the detection and classification of abnormal heartbeats. In recent years, self-operated heartbeat detection research has gained popularity due to its cost-effectiveness and potential for expediting therapy for individuals at risk of arrhythmias. However, building an efficient automatic heartbeat monitoring approach for arrhythmia identification and classification comes with several significant challenges. These challenges include addressing issues related to data quality, determining the range for heart rate segmentation, managing data imbalance difficulties, handling intra- and inter-patient variations, distinguishing supraventricular irregular heartbeats from regular heartbeats, and ensuring model interpretability. In this study, we propose the Reseek-Arrhythmia model, which leverages deep learning techniques to automatically detect and classify heart arrhythmia diseases. The model combines different convolutional blocks and identity blocks, along with essential components such as convolution layers, batch normalization layers, and activation layers. To train and evaluate the model, we utilized the MIT-BIH and PTB datasets. Remarkably, the proposed model achieves outstanding performance with an accuracy of 99.35% and 93.50% and an acceptable loss of 0.688 and 0.2564, respectively. Full article
(This article belongs to the Special Issue Diagnostic AI and Cardiac Diseases)
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18 pages, 1636 KiB  
Article
Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel
by Istiak Mahmud, Md Mohsin Kabir, M. F. Mridha, Sultan Alfarhood, Mejdl Safran and Dunren Che
Diagnostics 2023, 13(15), 2540; https://doi.org/10.3390/diagnostics13152540 - 31 Jul 2023
Cited by 7 | Viewed by 2123
Abstract
Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and [...] Read more.
Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient’s heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%. Full article
(This article belongs to the Special Issue Diagnostic AI and Cardiac Diseases)
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24 pages, 3344 KiB  
Article
The Application of Deep Learning for the Segmentation and Classification of Coronary Arteries
by Şerife Kaba, Huseyin Haci, Ali Isin, Ahmet Ilhan and Cenk Conkbayir
Diagnostics 2023, 13(13), 2274; https://doi.org/10.3390/diagnostics13132274 - 5 Jul 2023
Cited by 10 | Viewed by 4236
Abstract
In recent years, the prevalence of coronary artery disease (CAD) has become one of the leading causes of death around the world. Accurate stenosis detection of coronary arteries is crucial for timely treatment. Cardiologists use visual estimations when reading coronary angiography images to [...] Read more.
In recent years, the prevalence of coronary artery disease (CAD) has become one of the leading causes of death around the world. Accurate stenosis detection of coronary arteries is crucial for timely treatment. Cardiologists use visual estimations when reading coronary angiography images to diagnose stenosis. As a result, they face various challenges which include high workloads, long processing times and human error. Computer-aided segmentation and classification of coronary arteries, as to whether stenosis is present or not, significantly reduces the workload of cardiologists and human errors caused by manual processes. Moreover, deep learning techniques have been shown to aid medical experts in diagnosing diseases using biomedical imaging. Thus, this study proposes the use of automatic segmentation of coronary arteries using U-Net, ResUNet-a, UNet++, models and classification using DenseNet201, EfficientNet-B0, Mobilenet-v2, ResNet101 and Xception models. In the case of segmentation, the comparative analysis of the three models has shown that U-Net achieved the highest score with a 0.8467 Dice score and 0.7454 Jaccard Index in comparison with UNet++ and ResUnet-a. Evaluation of the classification model’s performances has shown that DenseNet201 performed better than other pretrained models with 0.9000 accuracy, 0.9833 specificity, 0.9556 PPV, 0.7746 Cohen’s Kappa and 0.9694 Area Under the Curve (AUC). Full article
(This article belongs to the Special Issue Diagnostic AI and Cardiac Diseases)
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Review

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16 pages, 2294 KiB  
Review
Can Artificial Intelligence Revolutionize the Diagnosis and Management of the Atrial Septal Defect in Children?
by Eliza Cinteza, Corina Maria Vasile, Stefan Busnatu, Ionel Armat, Arsenie Dan Spinu, Radu Vatasescu, Gabriela Duica and Alin Nicolescu
Diagnostics 2024, 14(2), 132; https://doi.org/10.3390/diagnostics14020132 - 6 Jan 2024
Cited by 1 | Viewed by 1782
Abstract
Atrial septal defects (ASDs) present a significant healthcare challenge, demanding accurate and timely diagnosis and precise management to ensure optimal patient outcomes. Artificial intelligence (AI) applications in healthcare are rapidly evolving, offering promise for enhanced medical decision-making and patient care. In the context [...] Read more.
Atrial septal defects (ASDs) present a significant healthcare challenge, demanding accurate and timely diagnosis and precise management to ensure optimal patient outcomes. Artificial intelligence (AI) applications in healthcare are rapidly evolving, offering promise for enhanced medical decision-making and patient care. In the context of cardiology, the integration of AI promises to provide more efficient and accurate diagnosis and personalized treatment strategies for ASD patients. In interventional cardiology, sometimes the lack of precise measurement of the cardiac rims evaluated by transthoracic echocardiography combined with the floppy aspect of the rims can mislead and result in complications. AI software can be created to generate responses for difficult tasks, like which device is the most suitable for different shapes and dimensions to prevent embolization or erosion. This paper reviews the current state of AI in healthcare and its applications in cardiology, emphasizing the specific opportunities and challenges in applying AI to ASD diagnosis and management. By exploring the capabilities and limitations of AI in ASD diagnosis and management. This paper highlights the evolution of medical practice towards a more AI-augmented future, demonstrating the capacity of AI to unlock new possibilities for healthcare professionals and patients alike. Full article
(This article belongs to the Special Issue Diagnostic AI and Cardiac Diseases)
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19 pages, 3281 KiB  
Review
Electrocardiographic Characteristics, Identification, and Management of Frequent Premature Ventricular Contractions
by Dimitris Tsiachris, Michail Botis, Ioannis Doundoulakis, Lamprini Iro Bartsioka, Panagiotis Tsioufis, Athanasios Kordalis, Christos-Konstantinos Antoniou, Konstantinos Tsioufis and Konstantinos A. Gatzoulis
Diagnostics 2023, 13(19), 3094; https://doi.org/10.3390/diagnostics13193094 - 29 Sep 2023
Viewed by 2559
Abstract
Premature ventricular complexes (PVCs) are frequently encountered in clinical practice. The association of PVCs with adverse cardiovascular outcomes is well established in the context of structural heart disease, yet not so much in the absence of structural heart disease. However, cardiac magnetic resonance [...] Read more.
Premature ventricular complexes (PVCs) are frequently encountered in clinical practice. The association of PVCs with adverse cardiovascular outcomes is well established in the context of structural heart disease, yet not so much in the absence of structural heart disease. However, cardiac magnetic resonance (CMR) seems to contribute prognostically in the latter subgroup. PVC-induced myocardial dysfunction refers to the impairment of ventricular function due to PVCs and is mostly associated with a PVC burden > 10%. Surface 12-lead ECG has long been used to localize the anatomic site of origin and multiple algorithms have been developed to differentiate between right ventricular and left ventricular outflow tract (RVOT and LVOT, respectively) origin. Novel algorithms include alternative ECG lead configurations and, lately, sophisticated artificial intelligence methods have been utilized to determine the origins of outflow tract arrhythmias. The decision to therapeutically address PVCs should be made upon the presence of symptoms or the development of PVC-induced myocardial dysfunction. Therapeutic modalities include pharmacological therapy (I-C antiarrhythmic drugs and beta blockers), as well as catheter ablation, which has demonstrated superior efficacy and safety. Full article
(This article belongs to the Special Issue Diagnostic AI and Cardiac Diseases)
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