Evidence map›Paper›PMID 39272624›Full record

ReviewDiagnostics (Basel, Switzerland)2024

AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions.

Luiza Camelia Nechita, Aurel Nechita, Andreea Elena Voipan, Daniel Voipan, Mihaela Debita, Ana Fulga, Iuliu Fulga, Carmina Liana Musat

Registry-linked trialAbstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07163767 (Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images), which is not on this map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07163767 active not recruitingnot on this mapstarted 2025, after this paper: background citation

Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images

TypeobservationalSponsorGuangdong Provincial People's HospitalRan2025 to 2028Enrolled150,000ConditionsAcute Myocardial Infarction (AMI), Electrocardiography, Artifical IntelligenceArmsDeep learning approach of ECG for AMI detection
3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Artificial Intelligence in Cardiovascular Health: Insights into Post-COVID Public Health Challenges.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Luiza Camelia NechitaFaculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania.
Aurel NechitaFaculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania.
Andreea Elena VoipanFaculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania.
Daniel VoipanFaculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania.
Mihaela DebitaFaculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania.
Ana FulgaFaculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania.
Iuliu FulgaFaculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania.
Carmina Liana MusatFaculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 800008 Galati, Romania.

Funding

"Dunarea de Jos" University of Galati 27232141
6 · The paper itself

Abstract

The application of artificial intelligence (AI) in electrocardiography is revolutionizing cardiology and providing essential insights into the consequences of the COVID-19 pandemic. This comprehensive review explores AI-enhanced ECG (AI-ECG) applications in risk prediction and diagnosis of heart diseases, with a dedicated chapter on COVID-19-related complications. Introductory concepts on AI and machine learning (ML) are explained to provide a foundational understanding for those seeking knowledge, supported by examples from the literature and current practices. We analyze AI and ML methods for arrhythmias, heart failure, pulmonary hypertension, mortality prediction, cardiomyopathy, mitral regurgitation, hypertension, pulmonary embolism, and myocardial infarction, comparing their effectiveness from both medical and AI perspectives. Special emphasis is placed on AI applications in COVID-19 and cardiology, including detailed comparisons of different methods, identifying the most suitable AI approaches for specific medical applications and analyzing their strengths, weaknesses, accuracy, clinical relevance, and key findings. Additionally, we explore AI's role in the emerging field of cardio-oncology, particularly in managing chemotherapy-induced cardiotoxicity and detecting cardiac masses. This comprehensive review serves as both an insightful guide and a call to action for further research and collaboration in the integration of AI in cardiology, aiming to enhance precision medicine and optimize clinical decision-making.

Indexed as

artificial intelligencecardiologycardio-oncologyconvolutional neural networks (CNNs)COVID-19deep learning (DL)diagnosiselectrocardiographymachine learning (ML)risk prediction

Identifiers

PMID39272624
PMCPMC11394310

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.