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Artificial Intelligence in Cardiology—2nd Edition
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Artificial Intelligence in Cardiology—2nd Edition

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 (20 November 2023) | Viewed by 13247

Special Issue Editors

Dr. Laura Burattini

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
Interests: cardiovascular signal processing; cardiovascular image processing; artificial intelligence in medicine and biology; clinical decision support systems; wearable and portable sensors; assessment of noninvasive indexes of cardiovascular risk; cardiology in sport; feto-maternal cardiac monitoring; cardiac monitoring in infants
Special Issues, Collections and Topics in MDPI journals
Dr. Agnese Sbrollini

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
Interests: cardiac signal processing; biostatistics applied to cardiac signals; artificial intelligence in medicine and biology; clinical decision support systems; wearable and portable sensors; cardiorespiratory monitoring in sport; serial electrocardiography; atrial fibrillation; fetal and newborn monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Cees A. Swenne

E-Mail Website
Guest Editor
Cardiology Department, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
Interests: cardiology-related physics, physiology, modelling, and signal & image processing including AI techniques; technologies: electrophysiology, electrocardiography & vectorcardiography (diagnostic and monitoring), echocardiography, CAG/MRI/CT/PET/SPECT; subjects: acute coronary syndrome, arrhythmias, baroreflex control, exercise training, heart failure, heart rate variability, pulmonary hypertension, rehabilitation, syncope
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI), which includes machine learning, deep learning, and cognitive computing, is starting to influence all disciplines, including medicine. The exploitation of AI in cardiology will affect all cardiovascular areas, from research to clinical practice.

AI applications may find solutions to cardiovascular challenges in cardiology, allowing the integration, modeling, classification, and interpretation of heterogeneous data (e.g., demographics, laboratory tests, medical records, biosignals, bioimages, biofluid dynamics, and others), thus requiring the cooperation and contribution of several different skills and disciplines, mainly from bioengineering, cardiology, and computer science.

Like its predecessor, the second volume of this Special Issue aims to collect original papers and/or reviews on AI in cardiology. Topics include, but are not limited to:

  • AI-based clinical decision-making in cardiology;
  • Machine learning and deep learning in cardiology;
  • Knowledge engineering in cardiology;
  • Data analytics and data mining for clinical decision support in cardiology;
  • AI for diagnostics in cardiology;
  • AI for drug development and cardiovascular safety pharmacology;
  • AI-based precision medicine in cardiology;
  • Intelligent sensors, devices, and instruments in cardiology;
  • Models and systems for AI-based population cardiovascular health;
  • Ethics of AI in cardiology.

Dr. Laura Burattini
Dr. Agnese Sbrollini
Dr. Cees A. Swenne
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

  • arrhythmias
  • artificial intelligence
  • deep learning
  • cardiology
  • cardiology in sports
  • cardiovascular imaging
  • cardiovascular signals
  • clinical decision support systems
  • cognitive computing
  • computer vision
  • digital twin
  • electrocardiography
  • feto-maternal cardiac monitoring
  • infant and pediatric cardiology
  • hemodynamics
  • machine learning phonocardiography
  • safety pharmacology

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Related Special Issue

Published Papers (5 papers)

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Research

15 pages, 1483 KiB  
Article
Comparison of Electrocardiographic Parameters by Gender in Heart Failure Patients with Preserved Ejection Fraction via Artificial Intelligence
by Rustem Yilmaz and Ersoy Öz
Diagnostics 2023, 13(20), 3221; https://doi.org/10.3390/diagnostics13203221 - 16 Oct 2023
Viewed by 1795
Abstract
Background: Heart failure (HF) causes high morbidity and mortality worldwide. The prevalence of HF with preserved ejection fraction (HFpEF) is increasing compared with HF with reduced ejection fraction (HFrEF). Patients with HFpEF are a patient group with a high rate of hospitalization despite [...] Read more.
Background: Heart failure (HF) causes high morbidity and mortality worldwide. The prevalence of HF with preserved ejection fraction (HFpEF) is increasing compared with HF with reduced ejection fraction (HFrEF). Patients with HFpEF are a patient group with a high rate of hospitalization despite medical treatment. Early diagnosis is very important in this group of patients, and early treatment can improve their prognosis. Although electrocardiographic (ECG) findings have been adequately studied in patients with HFrEF, there are not enough studies on these parameters in patients with HFpEF. There are very few studies in the literature, especially on gender-specific changes. The current research aims to compare gender-specific ECG parameters in patients with HFpEF based on the implications of artificial intelligence (AI). Methods: A total of 118 patients participated in the study, of which 66 (56%) were women with HFpEF and 52 (44%) were men with HFpEF. Demographic, echocardiographic, and electrocardiographic characteristics of the patients were analyzed to compare gender-specific ECG parameters in patients with HFpEF. The AI approach combined with machine learning approaches (gradient boosting machine, k-nearest neighbors, logistic regression, random forest, and support vector machines) was applied for distinguishing male patients with HFpEF from female patients with HFpEF. Results: After determining the parameters (demographic, echocardiographic, and electrocardiographic) to distinguish male patients with HFpEF from female patients with HFpEF, machine learning methods were applied, and among these methods, the random forest model achieved an average accuracy of 84.7%. The random forest algorithm results showed that smoking, P-wave dispersion, P-wave amplitude, T-end P/(PQ*Age), Cornell product, and P-wave duration were the most influential parameters for distinguishing male patients with HFpEF from female patients with HFpEF. Conclusions: The proposed model serves as a valuable tool for physicians, facilitating the diagnosis, treatment, and follow-up for distinguishing male patients with HFpEF from female patients with HFpEF. Analyzing readily accessible electrocardiographic parameters empowers medical professionals to make informed decisions and provide enhanced care to a wide range of individuals. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiology—2nd Edition)
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13 pages, 1086 KiB  
Article
Using Machine Learning to Predict Abnormal Carotid Intima-Media Thickness in Type 2 Diabetes
by Chung-Ze Wu, Li-Ying Huang, Fang-Yu Chen, Chun-Heng Kuo and Dong-Feng Yeih
Diagnostics 2023, 13(11), 1834; https://doi.org/10.3390/diagnostics13111834 - 23 May 2023
Cited by 8 | Viewed by 2097
Abstract
Carotid intima-media thickness (c-IMT) is a reliable risk factor for cardiovascular disease risk in type 2 diabetes (T2D) patients. The present study aimed to compare the effectiveness of different machine learning methods and traditional multiple logistic regression in predicting c-IMT using baseline features [...] Read more.
Carotid intima-media thickness (c-IMT) is a reliable risk factor for cardiovascular disease risk in type 2 diabetes (T2D) patients. The present study aimed to compare the effectiveness of different machine learning methods and traditional multiple logistic regression in predicting c-IMT using baseline features and to establish the most significant risk factors in a T2D cohort. We followed up with 924 patients with T2D for four years, with 75% of the participants used for model development. Machine learning methods, including classification and regression tree, random forest, eXtreme gradient boosting, and Naïve Bayes classifier, were used to predict c-IMT. The results showed that all machine learning methods, except for classification and regression tree, were not inferior to multiple logistic regression in predicting c-IMT in terms of higher area under receiver operation curve. The most significant risk factors for c-IMT were age, sex, creatinine, body mass index, diastolic blood pressure, and duration of diabetes, sequentially. Conclusively, machine learning methods could improve the prediction of c-IMT in T2D patients compared to conventional logistic regression models. This could have crucial implications for the early identification and management of cardiovascular disease in T2D patients. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiology—2nd Edition)
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10 pages, 2943 KiB  
Article
Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning
by MHD Jafar Mortada, Selene Tomassini, Haidar Anbar, Micaela Morettini, Laura Burattini and Agnese Sbrollini
Diagnostics 2023, 13(10), 1683; https://doi.org/10.3390/diagnostics13101683 - 9 May 2023
Cited by 14 | Viewed by 3086
Abstract
Knowledge about the anatomical structures of the left heart, specifically the atrium (LA) and ventricle (i.e., endocardium—Vendo—and epicardium—LVepi) is essential for the evaluation of cardiac functionality. Manual segmentation of cardiac structures from echocardiography is the baseline reference, but results are user-dependent and time-consuming. [...] Read more.
Knowledge about the anatomical structures of the left heart, specifically the atrium (LA) and ventricle (i.e., endocardium—Vendo—and epicardium—LVepi) is essential for the evaluation of cardiac functionality. Manual segmentation of cardiac structures from echocardiography is the baseline reference, but results are user-dependent and time-consuming. With the aim of supporting clinical practice, this paper presents a new deep-learning (DL)-based tool for segmenting anatomical structures of the left heart from echocardiographic images. Specifically, it was designed as a combination of two convolutional neural networks, the YOLOv7 algorithm and a U-Net, and it aims to automatically segment an echocardiographic image into LVendo, LVepi and LA. The DL-based tool was trained and tested on the Cardiac Acquisitions for Multi-Structure Ultrasound Segmentation (CAMUS) dataset of the University Hospital of St. Etienne, which consists of echocardiographic images from 450 patients. For each patient, apical two- and four-chamber views at end-systole and end-diastole were acquired and annotated by clinicians. Globally, our DL-based tool was able to segment LVendo, LVepi and LA, providing Dice similarity coefficients equal to 92.63%, 85.59%, and 87.57%, respectively. In conclusion, the presented DL-based tool proved to be reliable in automatically segmenting the anatomical structures of the left heart and supporting the cardiological clinical practice. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiology—2nd Edition)
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10 pages, 3141 KiB  
Article
Early Changes in Acute Myocardial Infarction in Pigs: Achieving Early Detection with Wearable Devices
by Ke Li, Marco Morales-Garza, Cristiano Cardoso, Angel Moctezuma-Ramirez, Atandra Burman, Jitto Titus, Abdelmotagaly Elgalad and Emerson Perin
Diagnostics 2023, 13(6), 1006; https://doi.org/10.3390/diagnostics13061006 - 7 Mar 2023
Cited by 3 | Viewed by 2723
Abstract
We examined the changes in variables that could be recorded on wearable devices during the early stages of acute myocardial infarction (AMI) in an animal model. Early diagnosis of AMI is important for prognosis; however, delayed diagnosis is common because of patient hesitation [...] Read more.
We examined the changes in variables that could be recorded on wearable devices during the early stages of acute myocardial infarction (AMI) in an animal model. Early diagnosis of AMI is important for prognosis; however, delayed diagnosis is common because of patient hesitation and lack of timely evaluations. Wearable devices are becoming increasingly sophisticated in the ability to track indicators. In this study, we retrospectively reviewed the changes in four variables during AMI in a pig model to assess their ability to help predict AMI onset. AMI was created in 33 pigs by 90-min balloon occlusion of the left anterior descending artery. Blood pressure, EKG, and lactate and cardiac troponin I levels were recorded during the occlusion period. Blood pressure declined significantly within 15 min after balloon inflation (mean arterial pressure, from 61 ± 8 to 50 ± 8 mmHg) and remained at this low level. Within 5 min of balloon inflation, the EKG showed ST-elevation in precordial leads V1–V3. Blood lactate levels increased gradually after occlusion and peaked at 60 min (from 1.48 to 2.53 mmol/L). The continuous transdermal troponin sensor demonstrated a gradual increase in troponin levels over time. Our data suggest that significant changes in key indicators (blood pressure, EKG leads V1–V3, and lactate and troponin levels) occurred at the onset of AMI. Monitoring of these variables could be used to develop an algorithm and alert patients early at the onset of AMI with the help of a wearable device. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiology—2nd Edition)
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15 pages, 589 KiB  
Article
Barriers and Opportunities Regarding Implementation of a Machine Learning-Based Acute Heart Failure Risk Stratification Tool in the Emergency Department
by Dana R. Sax, Lillian R. Sturmer, Dustin G. Mark, Jamal S. Rana and Mary E. Reed
Diagnostics 2022, 12(10), 2463; https://doi.org/10.3390/diagnostics12102463 - 11 Oct 2022
Cited by 6 | Viewed by 2639
Abstract
Hospital admissions for patients with acute heart failure (AHF) remain high. There is an opportunity to improve alignment between patient risk and admission decision. We recently developed a machine learning (ML)-based model that stratifies emergency department (ED) patients with AHF based on predicted [...] Read more.
Hospital admissions for patients with acute heart failure (AHF) remain high. There is an opportunity to improve alignment between patient risk and admission decision. We recently developed a machine learning (ML)-based model that stratifies emergency department (ED) patients with AHF based on predicted risk of a 30-day severe adverse event. Prior to deploying the algorithm and paired clinical decision support, we sought to understand barriers and opportunities regarding successful implementation. We conducted semi-structured interviews with eight front-line ED providers and surveyed 67 ED providers. Audio-recorded interviews were transcribed and analyzed using thematic analysis, and we had a 65% response rate to the survey. Providers wanted decision support to be streamlined into workflows with minimal disruptions. Most providers wanted assistance primarily with ED disposition decisions, and secondarily with medical management and post-discharge follow-up care. Receiving feedback on patient outcomes after risk tool use was seen as an opportunity to increase acceptance, and few providers (<10%) had significant hesitations with using an ML-based tool after education on its use. Engagement with key front-line users on optimal design of the algorithm and decision support may contribute to broader uptake, acceptance, and adoption of recommendations for clinical decisions. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiology—2nd Edition)
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