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Artificial Intelligence in Diagnosis, Treatment and Prognosis of Cardiovascular Diseases
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Artificial Intelligence in Diagnosis, Treatment and Prognosis of Cardiovascular Diseases

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 340

Special Issue Editor

Dr. Diaa A. Hakim

E-Mail Website
Guest Editor
Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
Interests: general cardiology; interventional cardiology; cardiovascular imaging (CT and MRI); intravascular imaging (IVUS and OCT); artificial intelligence applications in cardiovascular medicine

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) refers to the development of computer systems capable of performing tasks that typically require human intelligence, such as recognizing speech, making decisions, and solving problems.

The history of artificial intelligence (AI) began in the mid-20th century, with significant milestones such as the coining of the term “artificial intelligence” by John McCarthy in 1956 and the development of early AI programs like the Logic Theorist. Artificial intelligence (AI) plays a crucial role in medicine by enhancing diagnostic accuracy, personalizing treatment plans, and supporting clinical decision-making through advanced data analysis.

Cutting-edge research in artificial intelligence (AI) is driving innovations in areas such as natural language processing, computer vision, and autonomous systems, significantly enhancing capabilities across various industries and clinical applications that help in disease diagnosis, prognosis and decision-making in management plans.

This Special Issue will focus on groundbreaking advancements in artificial intelligence, featuring cutting-edge research and innovative applications across the field of cardiovascular care. We welcome submissions of reviews and articles using AI-enabled programs.

Dr. Diaa A. Hakim
Guest Editor

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. Journal of Personalized Medicine 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
  • machine learning
  • deep learning
  • generative artificial intelligence
  • large language models

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Published Papers (1 paper)

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24 pages, 1616 KiB  
Systematic Review
Artificial Intelligence in Risk Stratification and Outcome Prediction for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis
by Shayan Shojaei, Asma Mousavi, Sina Kazemian, Shiva Armani, Saba Maleki, Parisa Fallahtafti, Farzin Tahmasbi Arashlow, Yasaman Daryabari, Mohammadreza Naderian, Mohamad Alkhouli, Jamal S. Rana, Mehdi Mehrani, Yaser Jenab and Kaveh Hosseini
J. Pers. Med. 2025, 15(7), 302; https://doi.org/10.3390/jpm15070302 - 11 Jul 2025
Viewed by 74
Abstract
Background/Objectives: Transcatheter aortic valve replacement (TAVR) has been introduced as an optimal treatment for patients with severe aortic stenosis, offering a minimally invasive alternative to surgical aortic valve replacement. Predicting these outcomes following TAVR is crucial. Artificial intelligence (AI) has emerged as a [...] Read more.
Background/Objectives: Transcatheter aortic valve replacement (TAVR) has been introduced as an optimal treatment for patients with severe aortic stenosis, offering a minimally invasive alternative to surgical aortic valve replacement. Predicting these outcomes following TAVR is crucial. Artificial intelligence (AI) has emerged as a promising tool for improving post-TAVR outcome prediction. In this systematic review and meta-analysis, we aim to summarize the current evidence on utilizing AI in predicting post-TAVR outcomes. Methods: A comprehensive search was conducted to evaluate the studies focused on TAVR that applied AI methods for risk stratification. We assessed various ML algorithms, including random forests, neural networks, extreme gradient boosting, and support vector machines. Model performance metrics—recall, area under the curve (AUC), and accuracy—were collected with 95% confidence intervals (CIs). A random-effects meta-analysis was conducted to pool effect estimates. Results: We included 43 studies evaluating 366,269 patients (mean age 80 ± 8.25; 52.9% men) following TAVR. Meta-analyses for AI model performances demonstrated the following results: all-cause mortality (AUC = 0.78 (0.74–0.82), accuracy = 0.81 (0.69–0.89), and recall = 0.90 (0.70–0.97); permanent pacemaker implantation or new left bundle branch block (AUC = 0.75 (0.68–0.82), accuracy = 0.73 (0.59–0.84), and recall = 0.87 (0.50–0.98)); valve-related dysfunction (AUC = 0.73 (0.62–0.84), accuracy = 0.79 (0.57–0.91), and recall = 0.54 (0.26–0.80)); and major adverse cardiovascular events (AUC = 0.79 (0.67–0.92)). Subgroup analyses based on the model development approaches indicated that models incorporating baseline clinical data, imaging, and biomarker information enhanced predictive performance. Conclusions: AI-based risk prediction for TAVR complications has demonstrated promising performance. However, it is necessary to evaluate the efficiency of the aforementioned models in external validation datasets. Full article
(This article belongs to the Special Issue Artificial Intelligence in Diagnosis, Treatment and Prognosis of Cardiovascular Diseases)
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