ReviewFrontiers in cardiovascular medicine2020
Image-Based Cardiac Diagnosis With Machine Learning: A Review.
Review in Frontiers in cardiovascular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
70 citing papers in PubMed, 243 citations in OpenAlex.
- Echo Chambers: Bias and Representation in Cardiac Imaging Datasets for Artificial Intelligence.Current cardiology reports · 2026Review
- Large Language Models for Patient Education in Cardiovascular Imaging: Prospective Observational Comparative Study.JMIR formative research · 2026Observational
- Cross-domain dynamic routing decoders for multi-domain generalization in ultrasound imaging.Quantitative imaging in medicine and surgery · 2026Article
- Multi-phase deep learning model for automated disease classification from cardiac cine MRI.Journal of the Royal Society, Interface · 2025Article
- Advances in artificial intelligence and precision nutrition approaches to improve maternal and child health in low resource settings.Nature communications · 2025Review
- CELM: An Ensemble Deep Learning Model for Early Cardiomegaly Diagnosis in Chest Radiography.Diagnostics (Basel, Switzerland) · 2025Article
- Volatilome and machine learning in ischemic heart disease: Current challenges and future perspectives.World journal of cardiology · 2025Article
- Machine Learning-Based Model for Predicting Coronary Heart Disease Using Preβ HDL and Cytokines as Plasma Biomarkers.Proceedings. International Conference on Computational Science and Computational Intelligence · 2025Article
- Interoception, cardiac health, and heart failure: The potential for artificial intelligence (AI)-driven diagnosis and treatment.Physiological reports · 2025Review
- Beyond genomic studies of congenital heart defects through systematic modelling and phenotyping.Disease models & mechanisms · 2024Article
- Semi-supervised segmentation of cardiac chambers from LGE-CMR using feature consistency awareness.BMC cardiovascular disorders · 2024Article
- Applying a Deep Learning Model for Total Kidney Volume Measurement in Autosomal Dominant Polycystic Kidney Disease.Bioengineering (Basel, Switzerland) · 2024Article
- Review
- A Novel Approach to Identifying Hibernating Myocardium Using Radiomics-Based Machine Learning.Cureus · 2024Article
- The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis.Cureus · 2024Review
- Decoding pulsatile patterns of cerebrospinal fluid dynamics through enhancing interpretability in machine learning.Scientific reports · 2024Article
- Shallow and deep learning classifiers in medical image analysis.European radiology experimental · 2024Review
- Generative Artificial Intelligence: Enhancing Patient Education in Cardiovascular Imaging.BJR open · 2024Review
- Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data.Frontiers in cardiovascular medicine · 2024Article
- Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
Abstract
Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.
Indexed as
Identifiers
What OpenQuestion holds
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.