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Machine Learning and Artificial Intelligence in Cardiovascular Medicine

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A special issue of Journal of Cardiovascular Development and Disease (ISSN 2308-3425). This special issue belongs to the section "Cardiovascular Clinical Research".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 28306

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Dr. Chin Siang Ong

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Division of Surgical Outcomes, Department of Surgery, Yale School of Medicine, Yale University, New Haven, CT 06520, USA
Interests: cardiac surgery; cardiology; risk models; machine learning; artificial intelligence; deep learning

Special Issue Information

Dear Colleagues,

Machine learning (ML) and deep learning have revolutionized, or have the potential to revolutionize, many aspects of medicine through the addition of artificial intelligence (AI) into human clinical decision making. Although the theory of neural networks has been around for decades, recent advances in graphics processing unit (GPU) acceleration technology have allowed compute-intensive tasks to shift from central processing units (CPUs) to GPUs, hence, allowing the deep learning of medical big data with improved feasibility and cost efficiency. In cardiovascular medicine, AI has demonstrated favorable results when used in the triage and diagnosis of coronary artery disease, acute pulmonary embolism, and other cardiovascular diseases, as well as the prediction of adverse events and side effects after treatment. The deep learning of unstructured data streams, such as electrocardiograms and echocardiograms, has been used in the artificial intelligence-assisted evaluation of cardiac function, unsupervised image and video segmentation, as well as ML and the prediction of metrics related to structural heart disease and cardiomyopathy. ML algorithms and the deep learning of unstructured data streams, such as chest radiographs, have been used in the prediction of operative mortality and other adverse outcomes after cardiac surgery. This Special Issue aims to explore advances in ML techniques and AI applications in cardiovascular medicine.

Dr. Chin Siang Ong
Guest Editor

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Keywords

machine learning
artificial intelligence
cardiovascular medicine
cardiovascular disease

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Published Papers (9 papers)

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Research

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15 pages, 2114 KiB

Open AccessArticle

Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass

by Orlando Parise, Gianmarco Parise, Akshayaa Vaidyanathan, Mariaelena Occhipinti, Ali Gharaviri, Cecilia Tetta, Elham Bidar, Bart Maesen, Jos G. Maessen, Mark La Meir and Sandro Gelsomino

J. Cardiovasc. Dev. Dis. 2023, 10(2), 82; https://doi.org/10.3390/jcdd10020082 - 15 Feb 2023

Cited by 5 | Viewed by 2583

Abstract

Background: This study aims to get an effective machine learning (ML) prediction model of new-onset postoperative atrial fibrillation (POAF) following coronary artery bypass grafting (CABG) and to highlight the most relevant clinical factors. Methods: Four ML algorithms were employed to analyze 394 patients undergoing CABG, and their performances were compared: Multivariate Adaptive Regression Spline, Neural Network, Random Forest, and Support Vector Machine. Each algorithm was applied to the training data set to choose the most important features and to build a predictive model. The better performance for each model was obtained by a hyperparameters search, and the Receiver Operating Characteristic Area Under the Curve metric was selected to choose the best model. The best instances of each model were fed with the test data set, and some metrics were generated to assess the performance of the models on the unseen data set. A traditional logistic regression was also performed to be compared with the machine learning models. Results: Random Forest model showed the best performance, and the top five predictive features included age, preoperative creatinine values, time of aortic cross-clamping, body surface area, and Logistic Euro-Score. Conclusions: The use of ML for clinical predictions requires an accurate evaluation of the models and their hyperparameters. Random Forest outperformed all other models in the clinical prediction of POAF following CABG. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Cardiovascular Medicine)

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15 pages, 2205 KiB

Open AccessArticle

Prediction and Analysis of Heart Failure Decompensation Events Based on Telemonitored Data and Artificial Intelligence Methods

by Jon Kerexeta, Nekane Larburu, Vanessa Escolar, Ainara Lozano-Bahamonde, Iván Macía, Andoni Beristain Iraola and Manuel Graña

J. Cardiovasc. Dev. Dis. 2023, 10(2), 48; https://doi.org/10.3390/jcdd10020048 - 28 Jan 2023

Cited by 6 | Viewed by 2546

Abstract

Cardiovascular diseases are the leading cause of death globally, taking an estimated 17.9 million lives each year. Heart failure (HF) occurs when the heart is not able to pump enough blood to satisfy metabolic needs. People diagnosed with chronic HF may suffer from cardiac decompensation events (CDEs), which cause patients’ worsening. Being able to intervene before decompensation occurs is the major challenge addressed in this study. The aim of this study is to exploit available patient data to develop an artificial intelligence (AI) model capable of predicting the risk of CDEs timely and accurately. Materials and Methods: The vital variables of patients (n = 488) diagnosed with chronic heart failure were monitored between 2014 and 2022. Several supervised classification models were trained with these monitoring data to predict CDEs, using clinicians’ annotations as the gold standard. Feature extraction methods were applied to identify significant variables. Results: The XGBoost classifier achieved an AUC of 0.72 in the cross-validation process and 0.69 in the testing set. The most predictive physiological variables for CAE decompensations are weight gain, oxygen saturation in the final days, and heart rate. Additionally, the answers to questionnaires on wellbeing, orthopnoea, and ankles are strongly significant predictors. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Cardiovascular Medicine)

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19 pages, 1719 KiB

Open AccessArticle

Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study

by Evgeny Ovcharenko, Anton Kutikhin, Olga Gruzdeva, Anastasia Kuzmina, Tamara Slesareva, Elena Brusina, Svetlana Kudasheva, Tatiana Bondarenko, Svetlana Kuzmenko, Nikolay Osyaev, Natalia Ivannikova, Grigory Vavin, Vadim Moses, Viacheslav Danilov, Egor Komossky and Kirill Klyshnikov

J. Cardiovasc. Dev. Dis. 2023, 10(2), 39; https://doi.org/10.3390/jcdd10020039 - 23 Jan 2023

Cited by 1 | Viewed by 2344

Abstract

Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3–5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Cardiovascular Medicine)

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14 pages, 1515 KiB

Open AccessArticle

Blood Pressure Prediction Using Ensemble Rules during Isometric Sustained Weight Test

by Ramón Carrazana-Escalona, Adán Andreu-Heredia, María Moreno-Padilla, Gustavo A. Reyes del Paso, Miguel E. Sánchez-Hechavarría and Gustavo Muñoz-Bustos

J. Cardiovasc. Dev. Dis. 2022, 9(12), 440; https://doi.org/10.3390/jcdd9120440 - 7 Dec 2022

Cited by 1 | Viewed by 1930

Abstract

Background: Predicting beat-to-beat blood pressure has several clinical applications. While most machine learning models focus on accuracy, it is necessary to build models that explain the relationships of hemodynamical parameters with blood pressure without sacrificing accuracy, especially during exercise. Objective: The aim of this study is to use the RuleFit model to measure the importance, interactions, and relationships among several parameters extracted from photoplethysmography (PPG) and electrocardiography (ECG) signals during a dynamic weight-bearing test (WBT) and to assess the accuracy and interpretability of the model results. Methods: RuleFit was applied to hemodynamical ECG and PPG parameters during rest and WBT in six healthy young subjects. The WBT involves holding a 500 g weight in the left hand for 2 min. Blood pressure is taken in the opposite arm before and during exercise thereof. Results: The root mean square error of the model residuals was 4.72 and 2.68 mmHg for systolic blood pressure and diastolic blood pressure, respectively, during rest and 4.59 and 4.01 mmHg, respectively, during the WBT. Furthermore, the blood pressure measurements appeared to be nonlinear, and interaction effects were observed. Moreover, blood pressure predictions based on PPG parameters showed a strong correlation with individual characteristics and responses to exercise. Conclusion: The RuleFit model is an excellent tool to study interactions among variables for predicting blood pressure. Compared to other models, the RuleFit model showed superior performance. RuleFit can be used for predicting and interpreting relationships among predictors extracted from PPG and ECG signals. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Cardiovascular Medicine)

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30 pages, 67136 KiB

Open AccessArticle

Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm

by Pankaj K. Jain, Abhishek Dubey, Luca Saba, Narender N. Khanna, John R. Laird, Andrew Nicolaides, Mostafa M. Fouda, Jasjit S. Suri and Neeraj Sharma

J. Cardiovasc. Dev. Dis. 2022, 9(10), 326; https://doi.org/10.3390/jcdd9100326 - 27 Sep 2022

Cited by 27 | Viewed by 5435

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. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Cardiovascular Medicine)

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10 pages, 1677 KiB

Open AccessArticle

Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support

by Benjamin L. Shou, Devina Chatterjee, Joseph W. Russel, Alice L. Zhou, Isabella S. Florissi, Tabatha Lewis, Arjun Verma, Peyman Benharash and Chun Woo Choi

J. Cardiovasc. Dev. Dis. 2022, 9(9), 311; https://doi.org/10.3390/jcdd9090311 - 19 Sep 2022

Cited by 3 | Viewed by 1813

Abstract

Background: Existing prediction models for post-transplant mortality in patients bridged to heart transplantation with temporary mechanical circulatory support (tMCS) perform poorly. A more reliable model would allow clinicians to provide better pre-operative risk assessment and develop more targeted therapies for high-risk patients. Methods: We identified adult patients in the United Network for Organ Sharing database undergoing isolated heart transplantation between 01/2009 and 12/2017 who were supported with tMCS at the time of transplant. We constructed a machine learning model using extreme gradient boosting (XGBoost) with a 70:30 train:test split to predict 1-year post-operative mortality. All pre-transplant variables available in the UNOS database were included to train the model. Shapley Additive Explanations was used to identify and interpret the most important features for XGBoost predictions. Results: A total of 1584 patients were included, with a median age of 56 (interquartile range: 46–62) and 74% male. Actual 1-year mortality was 12.1%. Out of 498 available variables, 43 were selected for the final model. The area under the receiver operator characteristics curve (AUC) for the XGBoost model was 0.71 (95% CI: 0.62–0.78). The most important variables predictive of 1-year mortality included recipient functional status, age, pulmonary capillary wedge pressure (PCWP), cardiac output, ECMO usage, and serum creatinine. Conclusions: An interpretable machine learning model trained on a large clinical database demonstrated good performance in predicting 1-year mortality for patients bridged to heart transplantation with tMCS. Machine learning may be used to enhance clinician judgement in the care of markedly high-risk transplant recipients. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Cardiovascular Medicine)

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15 pages, 2614 KiB

Open AccessArticle

Toward Better Risk Stratification for Implantable Cardioverter-Defibrillator Recipients: Implications of Explainable Machine Learning Models

by Yu Deng, Sijing Cheng, Hao Huang, Xi Liu, Yu Yu, Min Gu, Chi Cai, Xuhua Chen, Hongxia Niu and Wei Hua

J. Cardiovasc. Dev. Dis. 2022, 9(9), 310; https://doi.org/10.3390/jcdd9090310 - 17 Sep 2022

Cited by 3 | Viewed by 2212

Abstract

Background: Current guideline-based implantable cardioverter-defibrillator (ICD) implants fail to meet the demands for precision medicine. Machine learning (ML) designed for survival analysis might facilitate personalized risk stratification. We aimed to develop explainable ML models predicting mortality and the first appropriate shock and compare these to standard Cox proportional hazards (CPH) regression in ICD recipients. Methods and Results: Forty-five routine clinical variables were collected. Four fine-tuned ML approaches (elastic net Cox regression, random survival forests, survival support vector machine, and XGBoost) were applied and compared with the CPH model on the test set using Harrell’s C-index. Of 887 adult patients enrolled, 199 patients died (5.0 per 100 person-years) and 265 first appropriate shocks occurred (12.4 per 100 person-years) during the follow-up. Patients were randomly split into training (75%) and test (25%) sets. Among ML models predicting death, XGBoost achieved the highest accuracy and outperformed the CPH model (C-index: 0.794 vs. 0.760, p < 0.001). For appropriate shock, survival support vector machine showed the highest accuracy, although not statistically different from the CPH model (0.621 vs. 0.611, p = 0.243). The feature contribution of ML models assessed by SHAP values at individual and overall levels was in accordance with established knowledge. Accordingly, a bi-dimensional risk matrix integrating death and shock risk was built. This risk stratification framework further classified patients with different likelihoods of benefiting from ICD implant. Conclusions: Explainable ML models offer a promising tool to identify different risk scenarios in ICD-eligible patients and aid clinical decision making. Further evaluation is needed. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Cardiovascular Medicine)

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Review

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54 pages, 9111 KiB

Open AccessReview

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

by Vandana Kumari, Naresh Kumar, Sampath Kumar K, Ashish Kumar, Sanagala S. Skandha, Sanjay Saxena, Narendra N. Khanna, John R. Laird, Narpinder Singh, Mostafa M. Fouda, Luca Saba, Rajesh Singh and Jasjit S. Suri

J. Cardiovasc. Dev. Dis. 2023, 10(12), 485; https://doi.org/10.3390/jcdd10120485 - 4 Dec 2023

Cited by 3 | Viewed by 2731

Abstract

Background and Motivation: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. Methods: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. Findings and Conclusions: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Cardiovascular Medicine)

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51 pages, 15712 KiB

Open AccessReview

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

by Narendra N. Khanna, Mahesh Maindarkar, Anudeep Puvvula, Sudip Paul, Mrinalini Bhagawati, Puneet Ahluwalia, Zoltan Ruzsa, Aditya Sharma, Smiksha Munjral, Raghu Kolluri, Padukone R. Krishnan, Inder M. Singh, John R. Laird, Mostafa Fatemi, Azra Alizad, Surinder K. Dhanjil, Luca Saba, Antonella Balestrieri, Gavino Faa, Kosmas I. Paraskevas, Durga Prasanna Misra, Vikas Agarwal, Aman Sharma, Jagjit Teji, Mustafa Al-Maini, Andrew Nicolaides, Vijay Rathore, Subbaram Naidu, Kiera Liblik, Amer M. Johri, Monika Turk, David W. Sobel, Gyan Pareek, Martin Miner, Klaudija Viskovic, George Tsoulfas, Athanasios D. Protogerou, Sophie Mavrogeni, George D. Kitas, Mostafa M. Fouda, Manudeep K. Kalra and Jasjit S. Suri Show full author list Hide full author list

J. Cardiovasc. Dev. Dis. 2022, 9(8), 268; https://doi.org/10.3390/jcdd9080268 - 15 Aug 2022

Cited by 17 | Viewed by 4694

Abstract

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Cardiovascular Medicine)

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