ReviewChildren (Basel, Switzerland)2025
Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.
Review in Children (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Artificial Intelligence in Pediatric Imaging: A Primer for Pediatric Clinicians.Indian journal of pediatrics · 2026Review
- Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions.Pediatric radiology · 2026Review
- The role of artificial intelligence in pediatric cardiovascular imaging: clinical applications and future directions in computed tomography and magnetic resonance imaging.Pediatric radiology · 2026Review
- Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence.Circulation · 2026Article
- Artificial Intelligence in Pediatric Cardiology: Present Applications and Future Directions.Pediatric reports · 2026Review
- Deep Learning-Based Automated Echocardiographic Measurements in Pediatric and Congenital Heart Disease.medRxiv : the preprint server for health sciences · 2026Article
- Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence.medRxiv : the preprint server for health sciences · 2026Article
- Prediction of Postoperative Mortality After Fontan Procedure: A Clinical Prediction Model Study Using Deep Learning Artificial Intelligence Techniques.Journal of cardiovascular development and disease · 2025Article
- Essential Concepts in Artificial Intelligence: A Guide for Pediatric Providers.Children (Basel, Switzerland) · 2025Review
- Reshaping Anesthesia with Artificial Intelligence: From Concept to Reality.Anesthesia and critical care (Houston, Tex.) · 2025Article
- 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
4 authors.
Funding
Abstract
Cardiovascular magnetic resonance (CMR) imaging is essential for the management of congenital heart disease (CHD), due to the ability to perform anatomic and physiologic assessments of patients. However, CMR scans can be time-consuming to perform and analyze, creating roadblocks to broader use of CMR in CHD. Recent publications have shown artificial intelligence (AI) has the potential to increase efficiency, improve image quality, and reduce errors. This review examines the use of AI techniques to improve CMR in CHD, by focusing on deep learning techniques applied to image acquisition and reconstruction, image processing and reporting, clinical use cases, and future directions.
Indexed as
Identifiers
What Socratic 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 Socratic graph.