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Diagnostics
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Artificial Intelligence in Cardiology
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Artificial Intelligence in Cardiology

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 December 2021) | Viewed by 19144

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. 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
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

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, modelling, classification, and interpretation of heterogeneous data (e.g., demographics, laboratory tests, medical records, biosignals, bioimages, biofluidodynamics, and others), thus requiring the cooperation and contribution of several different skills, mainly from bioengineering, cardiology, and computer science.

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 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. Cees A. Swenne
Dr. Agnese Sbrollini
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

  • Artificial intelligence;
  • Machine and deep learning;
  • Clinical decision support systems;
  • Cognitive computing;
  • Computer vision;
  • Digital twin;
  • Cardiology;
  • Electrocardiography;
  • Phonocardiography;
  • Hemodynamics;
  • Safety pharmacology;
  • Cardiovascular signals;
  • Cardiovascular imaging;
  • Arrhythmias;
  • Cardiology in sport;
  • Feto-maternal cardiac monitoring;
  • Infant and pediatric cardiology

Related Special Issue

Published Papers (7 papers)

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Research

11 pages, 1256 KiB  
Article
Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients
by Pablo Juan-Salvadores, Cesar Veiga, Víctor Alfonso Jiménez Díaz, Alba Guitián González, Cristina Iglesia Carreño, Cristina Martínez Reglero, José Antonio Baz Alonso, Francisco Caamaño Isorna and Andrés Iñiguez Romo
Diagnostics 2022, 12(2), 422; https://doi.org/10.3390/diagnostics12020422 - 6 Feb 2022
Cited by 4 | Viewed by 2264
Abstract
Coronary artery disease is a chronic disease with an increased expression in the elderly. However, different studies have shown an increased incidence in young subjects over the last decades. The prediction of major adverse cardiac events (MACE) in very young patients has a [...] Read more.
Coronary artery disease is a chronic disease with an increased expression in the elderly. However, different studies have shown an increased incidence in young subjects over the last decades. The prediction of major adverse cardiac events (MACE) in very young patients has a significant impact on medical decision-making following coronary angiography and the selection of treatment. Different approaches have been developed to identify patients at a higher risk of adverse outcomes after their coronary anatomy is known. This is a prognostic study of combined data from patients ≤40 years old undergoing coronary angiography (n = 492). We evaluated whether different machine learning (ML) approaches could predict MACE more effectively than traditional statistical methods using logistic regression (LR). Our most effective model for long-term follow-up (60 ± 27 months) was random forest (RF), obtaining an area under the curve (AUC) = 0.79 (95%CI 0.69–0.88), in contrast with LR, obtaining AUC = 0.66 (95%CI 0.53–0.78, p = 0.021). At 1-year follow-up, the RF test found AUC 0.80 (95%CI 0.71–0.89) vs. LR 0.50 (95%CI 0.33–0.66, p < 0.001). The results of our study support the hypothesis that ML methods can improve both the identification of MACE risk patients and the prediction vs. traditional statistical techniques even in a small sample size. The application of ML techniques to focus the efforts on the detection of MACE in very young patients after coronary angiography could help tailor upfront follow-up strategies in such young patients according to their risk of MACE and to be used for proper assignment of health resources. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiology)
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15 pages, 3659 KiB  
Article
Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network
by Zakarya Farea Shaaf, Muhammad Mahadi Abdul Jamil, Radzi Ambar, Ahmed Abdu Alattab, Anwar Ali Yahya and Yousef Asiri
Diagnostics 2022, 12(2), 414; https://doi.org/10.3390/diagnostics12020414 - 5 Feb 2022
Cited by 11 | Viewed by 3568
Abstract
Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based [...] Read more.
Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image’s labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. Results: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network’s performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. Conclusion: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiology)
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15 pages, 2627 KiB  
Article
Automatic Identification of Bioprostheses on X-ray Angiographic Sequences of Transcatheter Aortic Valve Implantation Procedures Using Deep Learning
by Laura Busto, César Veiga, José A. González-Nóvoa, Marcos Loureiro-Ga, Víctor Jiménez, José Antonio Baz and Andrés Íñiguez
Diagnostics 2022, 12(2), 334; https://doi.org/10.3390/diagnostics12020334 - 27 Jan 2022
Cited by 3 | Viewed by 1757
Abstract
Transcatheter aortic valve implantation (TAVI) has become the treatment of choice for patients with severe aortic stenosis and high surgical risk. Angiography has been established as an essential tool in TAVI, as this modality provides real-time images required to support the intervention. The [...] Read more.
Transcatheter aortic valve implantation (TAVI) has become the treatment of choice for patients with severe aortic stenosis and high surgical risk. Angiography has been established as an essential tool in TAVI, as this modality provides real-time images required to support the intervention. The automatic interpretation and parameter extraction on such images can lead to significative improvements and new applications in the procedure that, in most cases, rely on a prior identification of the transcatheter heart valve (THV). In this paper, U-Net architecture is proposed for the automatic segmentation of THV on angiographies, studying the role of its hyperparameters in the quality of the segmentations. Several experiments have been conducted, testing the methodology using multiple configurations and evaluating the results on different types of frames captured during the procedure. The evaluation has been performed in terms of conventional classification metrics, complemented with two new metrics, specifically defined for this problem. Those new metrics provide a more appropriate assessment of the quality of the results, given the class imbalance in the dataset. From an analysis of the evaluation results, it can be concluded that the method provides appropriate segmentation results for this dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiology)
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15 pages, 1343 KiB  
Article
A Machine Learning Approach for Chronic Heart Failure Diagnosis
by Dafni K. Plati, Evanthia E. Tripoliti, Aris Bechlioulis, Aidonis Rammos, Iliada Dimou, Lampros Lakkas, Chris Watson, Ken McDonald, Mark Ledwidge, Rebabonye Pharithi, Joe Gallagher, Lampros K. Michalis, Yorgos Goletsis, Katerina K. Naka and Dimitrios I. Fotiadis
Diagnostics 2021, 11(10), 1863; https://doi.org/10.3390/diagnostics11101863 - 10 Oct 2021
Cited by 26 | Viewed by 3465
Abstract
The aim of this study was to address chronic heart failure (HF) diagnosis with the application of machine learning (ML) approaches. In the present study, we simulated the procedure that is followed in clinical practice, as the models we built are based on [...] Read more.
The aim of this study was to address chronic heart failure (HF) diagnosis with the application of machine learning (ML) approaches. In the present study, we simulated the procedure that is followed in clinical practice, as the models we built are based on various combinations of feature categories, e.g., clinical features, echocardiogram, and laboratory findings. We also investigated the incremental value of each feature type. The total number of subjects utilized was 422. An ML approach is proposed, comprising of feature selection, handling class imbalance, and classification steps. The results for HF diagnosis were quite satisfactory with a high accuracy (91.23%), sensitivity (93.83%), and specificity (89.62%) when features from all categories were utilized. The results remained quite high, even in cases where single feature types were employed. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiology)
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11 pages, 892 KiB  
Article
Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques
by Rosanna I. Comoretto, Danila Azzolina, Angela Amigoni, Giorgia Stoppa, Federica Todino, Andrea Wolfler, Dario Gregori and on behalf of the TIPNet Study Group
Diagnostics 2021, 11(7), 1299; https://doi.org/10.3390/diagnostics11071299 - 20 Jul 2021
Cited by 4 | Viewed by 2054
Abstract
The present work aims to identify the predictors of hemodynamic failure (HF) developed during pediatric intensive care unit (PICU) stay testing a set of machine learning techniques (MLTs), comparing their ability to predict the outcome of interest. The study involved patients admitted to [...] Read more.
The present work aims to identify the predictors of hemodynamic failure (HF) developed during pediatric intensive care unit (PICU) stay testing a set of machine learning techniques (MLTs), comparing their ability to predict the outcome of interest. The study involved patients admitted to PICUs between 2010 and 2020. Data were extracted from the Italian Network of Pediatric Intensive Care Units (TIPNet) registry. The algorithms considered were generalized linear model (GLM), recursive partition tree (RPART), random forest (RF), neural networks models, and extreme gradient boosting (XGB). Since the outcome is rare, upsampling and downsampling algorithms have been applied for imbalance control. For each approach, the main performance measures were reported. Among an overall sample of 29,494 subjects, only 399 developed HF during the PICU stay. The median age was about two years, and the male gender was the most prevalent. The XGB algorithm outperformed other MLTs in predicting HF development, with a median ROC measure of 0.780 (IQR 0.770–0.793). PIM 3, age, and base excess were found to be the strongest predictors of outcome. The present work provides insights for the prediction of HF development during PICU stay using machine-learning algorithms. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiology)
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12 pages, 1801 KiB  
Article
Automated Recognition of Ultrasound Cardiac Views Based on Deep Learning with Graph Constraint
by Yanhua Gao, Yuan Zhu, Bo Liu, Yue Hu, Gang Yu and Youmin Guo
Diagnostics 2021, 11(7), 1177; https://doi.org/10.3390/diagnostics11071177 - 29 Jun 2021
Cited by 4 | Viewed by 1705
Abstract
In transthoracic echocardiographic (TTE) examination, it is essential to identify the cardiac views accurately. Computer-aided recognition is expected to improve the accuracy of cardiac views of the TTE examination, particularly when obtained by non-trained providers. A new method for automatic recognition of cardiac [...] Read more.
In transthoracic echocardiographic (TTE) examination, it is essential to identify the cardiac views accurately. Computer-aided recognition is expected to improve the accuracy of cardiac views of the TTE examination, particularly when obtained by non-trained providers. A new method for automatic recognition of cardiac views is proposed consisting of three processes. First, a spatial transform network is performed to learn cardiac shape changes during a cardiac cycle, which reduces intra-class variability. Second, a channel attention mechanism is introduced to adaptively recalibrate channel-wise feature responses. Finally, the structured signals by the similarities among cardiac views are transformed into the graph-based image embedding, which acts as unsupervised regularization constraints to improve the generalization accuracy. The proposed method is trained and tested in 171792 cardiac images from 584 subjects. The overall accuracy of the proposed method on cardiac image classification is 99.10%, and the mean AUC is 99.36%, better than known methods. Moreover, the overall accuracy is 97.73%, and the mean AUC is 98.59% on an independent test set with 37,883 images from 100 subjects. The proposed automated recognition model achieved comparable accuracy with true cardiac views, and thus can be applied clinically to help find standard cardiac views. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiology)
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24 pages, 6588 KiB  
Article
Prediction of Hypertension Outcomes Based on Gain Sequence Forward Tabu Search Feature Selection and XGBoost
by Wenbing Chang, Xinpeng Ji, Yiyong Xiao, Yue Zhang, Bang Chen, Houxiang Liu and Shenghan Zhou
Diagnostics 2021, 11(5), 792; https://doi.org/10.3390/diagnostics11050792 - 27 Apr 2021
Cited by 5 | Viewed by 2213
Abstract
For patients with hypertension, serious complications, such as myocardial infarction, a common cause of heart failure, occurs in the late stage of hypertension. Hypertension outcomes can lead to complications, including death. Hypertension outcomes threaten patients’ lives and need to be predicted. In our [...] Read more.
For patients with hypertension, serious complications, such as myocardial infarction, a common cause of heart failure, occurs in the late stage of hypertension. Hypertension outcomes can lead to complications, including death. Hypertension outcomes threaten patients’ lives and need to be predicted. In our research, we reviewed the hypertension medical data from a tertiary-grade A class hospital in Beijing, and established a hypertension outcome prediction model with the machine learning theory. We first proposed a gain sequence forward tabu search feature selection (GSFTS-FS) method, which can search the optimal combination of medical variables that affect hypertension outcomes. Based on this, the XGBoost algorithm established a prediction model because of its good stability. We verified the proposed method by comparing other commonly used models in similar works. The proposed GSFTS-FS improved the performance by about 10%. The proposed prediction method has the best performance and its AUC value, accuracy, F1 value, and recall of 10-fold cross-validation were 0.96. 0.95, 0.88, and 0.82, respectively. It also performed well on test datasets with 0.92, 0.94, 0.87, and 0.80 for AUC, accuracy, F1, and recall, respectively. Therefore, the XGBoost with GSFTS-FS can accurately and effectively predict the occurrence of outcomes for patients with hypertension, and can provide guidance for doctors in clinical diagnoses and medical decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cardiology)
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