ReviewFrontiers in artificial intelligence2021
Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis.
Review in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 5 of them syntheses 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
18 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Effectiveness of machine learning for diagnosis and prognostic prediction of congenital heart disease: a systematic review and meta-analysis.BMC pediatrics · 2026Pooled it
- Accuracy of AI-based raman spectroscopy in the diagnosis of gastric cancer: a systematic review and meta-analysis.Lasers in medical science · 2026Pooled it
- Congenital heart disease diagnosis using machine learning: a systematic literature review.Frontiers in medicine · 2026Pooled it
- The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis.Frontiers in medicine · 2022Pooled it
- Artificial Intelligence-Enabled ECG to Detect Congenitally Corrected Transposition of the Great Arteries.Pediatric cardiology · 2026Article
- Deep learning-based detection of murine congenital heart defects from µCT scans.Communications biology · 2025Article
- The digital transformation and future era: bibliometric view of artificial intelligence application in pediatric surgery.Frontiers in pediatrics · 2025Article
- Prediction of cyanotic and acyanotic congenital heart disease using machine learning models.World journal of clinical pediatrics · 2024Article
- Enhancing Clinical Validation for Early Cardiovascular Disease Prediction through Simulation, AI, and Web Technology.Diagnostics (Basel, Switzerland) · 2024Article
- Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts.Nature communications · 2024Article
- Accurately assessing congenital heart disease using artificial intelligence.PeerJ. Computer science · 2024Article
- Global Access to Comprehensive Care for Paediatric and Congenital Heart Disease.CJC pediatric and congenital heart disease · 2023Review
- Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease.Acta obstetricia et gynecologica Scandinavica · 2023Article
- Review
- Identifying the Factors Affecting the Incidence of Congenital Heart Disease Using Support Vector Machine and Particle Swarm Optimization.Advanced biomedical research · 2023Article
- Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries.Journal of multidisciplinary healthcare · 2023Review
- Using Innovative Machine Learning Methods to Screen and Identify Predictors of Congenital Heart Diseases.Frontiers in cardiovascular medicine · 2021Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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.