ArticleScientific reports2023
Graph matching and deep neural networks based whole heart and great vessel segmentation in congenital heart disease.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 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
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Effectiveness of machine learning for diagnosis and prognostic prediction of congenital heart disease: a systematic review and meta-analysis.BMC pediatrics · 2026Pooled it
- Deep learning for cardiac CT segmentation for congenital heart disease: A systematic review.PloS one · 2026Pooled it
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- The impact of PMScientific reports · 2026Article
- Artificial Intelligence in Pediatric Cardiology: Present Applications and Future Directions.Pediatric reports · 2026Review
- A fully automated explainable predictive model for diagnosing pre-capillary and post-capillary pulmonary hypertension on routine unenhanced CT: results from the ASPIRE registry.European heart journal. Digital health · 2026Article
- A Review on Registration Techniques for Cardiac Computed Tomography and Ultrasound Images.Bioengineering (Basel, Switzerland) · 2025Review
- A novel hybrid layer-based encoder-decoder framework for 3D segmentation in congenital heart disease.Scientific reports · 2025Article
- Ambient Air Pollution and Congenital Heart Disease: Updated Evidence and Future Challenges.Antioxidants (Basel, Switzerland) · 2025Review
- Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging.Frontiers in cardiovascular medicine · 2024Review
- Three-dimensional printed models as an effective tool for the management of complex congenital heart disease.Frontiers in bioengineering and biotechnology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Congenital heart disease (CHD) is one of the leading causes of mortality among birth defects, and due to significant variations in the whole heart and great vessel, automatic CHD segmentation using CT images has been always under-researched. Even though some segmentation algorithms have been developed in the literature, none perform very well under the complex structure of CHD. To deal with the challenges, we take advantage of deep learning in processing regular structures and graph algorithms in dealing with large variations and propose a framework combining both the whole heart and great vessel segmentation in complex CHD. We benefit from deep learning in segmenting the four chambers and myocardium based on the blood pool, and then we extract the connection information and apply graph matching to determine the categories of all the vessels. Experimental results on 68 3D CT images covering 14 types of CHD illustrate our framework can increase the Dice score by 12% on average compared with the state-of-the-art whole heart and great vessel segmentation method in normal anatomy. We further introduce two cardiovascular imaging specialists to evaluate our results in the standard of the Van Praagh classification system, and achieves well performance in clinical evaluation. All these results may pave the way for the clinical use of our method in the incoming future.
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.