ArticlePediatric cardiology2021
Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot.
Article in Pediatric cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 31 citations in OpenAlex.
- The role of artificial intelligence in pediatric cardiovascular imaging: clinical applications and future directions in computed tomography and magnetic resonance imaging.Pediatric radiology · 2026Review
- Echocardiography-based intelligent diagnosis and risk stratification management for tetralogy of Fallot.EBioMedicine · 2026Article
- Prognostic Modeling of Tricuspid Valve Regurgitation Outcomes Using Machine Learning-Based Survival Analysis.Journal of clinical medicine · 2026Article
- Predicting Cardiac Magnetic Resonance-Derived Ejection Fraction from Echocardiogram Via Deep Learning Approach in Tetralogy of Fallot.Pediatric cardiology · 2026Article
- Automated detection of giant cell arteritis from temporal artery biopsy specimens using deep learning approaches.Scientific reports · 2026Article
- Artificial Intelligence in Adult Congenital Heart Disease: Diagnostic and Therapeutic Applications and Future Directions.Reviews in cardiovascular medicine · 2025Review
- Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.Children (Basel, Switzerland) · 2025Review
- Role of Artificial Intelligence in Congenital Heart Disease and Interventions.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality.Life (Basel, Switzerland) · 2024Article
- MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models.Proceedings of the ... Conference on Fairness, Accountability, and Transparency · 2024Article
- Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024Article
- A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology.Radiology. Artificial intelligence · 2024Article
- The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot.CJC pediatric and congenital heart disease · 2023Review
- Artificial Intelligence in Congenital Heart Disease: Current State and Prospects.JACC. Advances · 2022Review
- Artificial Intelligence in Pediatric Cardiology: A Scoping Review.Journal of clinical medicine · 2022Article
- The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review.Cardiology in the young · 2021Article
- SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot recognition.Wireless communications & mobile computing · 2021Article
Corrections and comments
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Authors and funding
16 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ventricular contouring of cardiac magnetic resonance imaging is the gold standard for volumetric analysis for repaired tetralogy of Fallot (rTOF), but can be time-consuming and subject to variability. A convolutional neural network (CNN) ventricular contouring algorithm was developed to generate contours for mostly structural normal hearts. We aimed to improve this algorithm for use in rTOF and propose a more comprehensive method of evaluating algorithm performance. We evaluated the performance of a ventricular contouring CNN, that was trained on mostly structurally normal hearts, on rTOF patients. We then created an updated CNN by adding rTOF training cases and evaluated the new algorithm's performance generating contours for both the left and right ventricles (LV and RV) on new testing data. Algorithm performance was evaluated with spatial metrics (Dice Similarity Coefficient (DSC), Hausdorff distance, and average Hausdorff distance) and volumetric comparisons (e.g., differences in RV volumes). The original Mostly Structurally Normal (MSN) algorithm was better at contouring the LV than the RV in patients with rTOF. After retraining the algorithm, the new MSN + rTOF algorithm showed improvements for LV epicardial and RV endocardial contours on testing data to which it was naïve (N = 30; e.g., DSC 0.883 vs. 0.905 for LV epicardium at end diastole, p < 0.0001) and improvements in RV end-diastolic volumetrics (median %error 8.1 vs 11.4, p = 0.0022). Even with a small number of cases, CNN-based contouring for rTOF can be improved. This work should be extended to other forms of congenital heart disease with more extreme structural abnormalities. Aspects of this work have already been implemented in clinical practice, representing rapid clinical translation. The combined use of both spatial and volumetric comparisons yielded insights into algorithm errors.
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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.