ReviewJACC. Asia2023
Advances in Diagnosis, Therapy, and Prognosis of Coronary Artery Disease Powered by Deep Learning Algorithms.
Review in JACC. Asia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled 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.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023.Frontiers in physiology · 2023Pooled it
- Advanced detection of coronary artery disease using a GAN-transformer model on plasma cytokine profiles.Scientific reports · 2026Article
- Region guided mask R-CNN with Haralick ResNet fusion for accurate coronary artery disease detection in computed tomography angiography images.Scientific reports · 2026Article
- An Innovative Model for Diagnosing Lesions in Coronary Angiography Imagery Using an Improved YOLOv4 Model.Bioengineering (Basel, Switzerland) · 2025Article
- Coronary artery stenosis associated with right ventricular dysfunction in acute pulmonary embolism: A case-control study.Chinese medical journal · 2025Article
- A machine learning model using echocardiographic myocardial strain to detect myocardial ischemia.Internal and emergency medicine · 2025Article
- The Long March to Identify Patients With Coronary Artery Disease by Pretest Probability Model.JACC. Asia · 2025Article
- Harnessing Artificial Intelligence in Interventional Cardiology: A Systematic Review of Current Applications.Cureus · 2025Review
- Non-invasive derivation of instantaneous free-wave ratio from invasive coronary angiography using a new deep learning artificial intelligence model and comparison with human operators' performance.The international journal of cardiovascular imaging · 2025Article
- Echocardiographic video-driven multi-task learning model for coronary artery disease diagnosis and severity grading.Frontiers in bioengineering and biotechnology · 2025Article
- Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI Innovations.Bioengineering (Basel, Switzerland) · 2024Review
- Using artificial intelligence to study atherosclerosis from computed tomography imaging: A state-of-the-art review of the current literature.Atherosclerosis · 2024Review
- Cardiovascular computed tomography in cardiovascular disease: An overview of its applications from diagnosis to prediction.Journal of geriatric cardiology : JGC · 2024Article
- Rapid genomic sequencing for genetic disease diagnosis and therapy in intensive care units: a review.NPJ genomic medicine · 2024Review
- Review
- Developing a Deep-Learning-Based Coronary Artery Disease Detection Technique Using Computer Tomography Images.Diagnostics (Basel, Switzerland) · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Percutaneous coronary intervention has been a standard treatment strategy for patients with coronary artery disease with continuous ebullient progress in technology and techniques. The application of artificial intelligence and deep learning in particular is currently boosting the development of interventional solutions, improving the efficiency and objectivity of diagnosis and treatment. The ever-growing amount of data and computing power together with cutting-edge algorithms pave the way for the integration of deep learning into clinical practice, which has revolutionized the interventional workflow in imaging processing, interpretation, and navigation. This review discusses the development of deep learning algorithms and their corresponding evaluation metrics together with their clinical applications. Advanced deep learning algorithms create new opportunities for precise diagnosis and tailored treatment with a high degree of automation, reduced radiation, and enhanced risk stratification. Generalization, interpretability, and regulatory issues are remaining challenges that need to be addressed through joint efforts from multidisciplinary community.
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.