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Life
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AI and Heart Failure
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AI and Heart Failure

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 1032

Special Issue Editors

Dr. Andrew Xanthopoulos

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Guest Editor
Department of Cardiology, University Hospital of Larissa, Larissa, Greece
Interests: heart failure; acute heart failure; chronic heart failure; LVAD; heart transplantation; amyloidosis; devices; pulmonary hypertension
Special Issues, Collections and Topics in MDPI journals
Dr. Polydoros Ν. Kampaktsis

E-Mail Website
Guest Editor
Department of Medicine, Irving Medical Center, Columbia University, New York, NY, USA
Interests: valvular disease; coronary artery disease; transcatheter therapies; cardiovascular imaging; artificial intelligence
Dr. Alexandros Briasoulis

E-Mail Website
Guest Editor
1. Division of Cardiovascular Medicine Section of Advanced Heart Failure and Transplant, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
2. Cardiology Department, Alexandra Hospital, Athens, Greece
Interests: cardiovascular medicine; heart failure; heart transplant
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the progress in medical technology and knowledge, large amounts of versatile medical data are now produced at high rates, constituting what is broadly called Big Data. Artificial intelligence and its algorithmic subfield termed machine learning (ML) offer patients, physicians and public health specialists advantageous methods to utilize these large amounts of medical data. Indeed, the number of indexed publications in PubMed that relate to ML and deep learning, types of machine learning algorithms that are called convolutional neural networks, has been rising exponentially since 2005. However, the application of ML algorithms is not without limitations. The collection and preprocessing of large data, overtraining and explicability of ML algorithms, risk of misinterpretation and misinformation, as well as the need for multidisciplinary teams are some of the major challenges of ML applications in the medical field.

In this context, ML algorithms are being used more and more frequently for multiple purposes within the cardiology subfield of heart failure (HF). ML algorithms have the potential to discover new knowledge, define clinical phenotypes and assist in the generation of research hypotheses. For example, they can generate research hypotheses for HF with a preserved ejection fraction, predict outcomes in different HF populations, assist physicians in the diagnosis of HF and associated clinical decision-making, and assist patients or people in avoiding HF hospitalizations by utilizing mobile devices. At the same time, great effort and expertise are warranted to address the challenges of ML applications in HF. The goal of this Special Issue is to update readers on the expanding applications and limitations of ML algorithms in HF by highlighting the key aspects and research in the field.

Dr. Andrew Xanthopoulos
Dr. Polydoros Ν. Kampaktsis
Dr. Alexandros Briasoulis
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. Life is an international peer-reviewed open access monthly 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
  • heart failure
  • heart disease

Published Papers (1 paper)

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Research

20 pages, 4718 KiB  
Article
Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality
by Wojciech Nazar, Krzysztof Nazar and Ludmiła Daniłowicz-Szymanowicz
Life 2024, 14(6), 761; https://doi.org/10.3390/life14060761 - 13 Jun 2024
Viewed by 480
Abstract
High-quality echocardiogram images are the cornerstone of accurate and reliable measurements of the heart. Therefore, this study aimed to develop, validate and compare machine learning and deep learning algorithms for accurate and automated assessment of transthoracic echocardiogram image quality. In total, 4090 single-frame [...] Read more.
High-quality echocardiogram images are the cornerstone of accurate and reliable measurements of the heart. Therefore, this study aimed to develop, validate and compare machine learning and deep learning algorithms for accurate and automated assessment of transthoracic echocardiogram image quality. In total, 4090 single-frame two-dimensional transthoracic echocardiogram images were used from apical 4-chamber, apical 2-chamber and parasternal long-axis views sampled from 3530 adult patients. The data were extracted from CAMUS and Unity Imaging open-source datasets. For every raw image, additional grayscale block histograms were developed. For block histogram datasets, six classic machine learning algorithms were tested. Moreover, convolutional neural networks based on the pre-trained EfficientNetB4 architecture were developed for raw image datasets. Classic machine learning algorithms predicted image quality with 0.74 to 0.92 accuracy (AUC 0.81 to 0.96), whereas convolutional neural networks achieved between 0.74 and 0.89 prediction accuracy (AUC 0.79 to 0.95). Both approaches are accurate methods of echocardiogram image quality assessment. Moreover, this study is a proof of concept of a novel method of training classic machine learning algorithms on block histograms calculated from raw images. Automated echocardiogram image quality assessment methods may provide additional relevant information to the echocardiographer in daily clinical practice and improve reliability in clinical decision making. Full article
(This article belongs to the Special Issue AI and Heart Failure)
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