scholarly journals Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease

2017 ◽  
pp. 95-102 ◽  
Author(s):  
Jelmer M. Wolterink ◽  
Tim Leiner ◽  
Max A. Viergever ◽  
Ivana Išgum
Keyword(s):  
Neural Networks ◽  
Congenital Heart Disease ◽  
Heart Disease ◽  
Convolutional Neural Networks ◽  
Congenital Heart ◽  
Cardiovascular Mr ◽  
Mr Segmentation

scholarly journals Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease

2018 ◽  
Vol 81 (2) ◽  
pp. 1143-1156 ◽  
Author(s):  
Andreas Hauptmann ◽  
Simon Arridge ◽  
Felix Lucka ◽  
Vivek Muthurangu ◽  
Jennifer A. Steeden
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Deep Learning ◽  
Real Time ◽  
Congenital Heart ◽  
Proof Of Concept ◽  
Cardiovascular Mr ◽  
Artifact Suppression ◽  
Spatio Temporal

scholarly journals Enhanced prediction of atrial fibrillation and mortality among patients with congenital heart disease using nationwide register based medical hospital data and neural networks

2021 ◽  
Author(s):  
Kok Wai Giang ◽  
Saga Helgadottir ◽  
Mikael Dellborg ◽  
Giovanni Volpe ◽  
Zacharias Mandalenakis
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Neural Networks ◽  
Congenital Heart Disease ◽  
Logistic Regression ◽  
Heart Disease ◽  
Congenital Heart ◽  
Hospital Data ◽  
Neural Network Models ◽  
Over Time

Abstract Aims To improve short-and long-term predictions of mortality and atrial fibrillation among patients with congenital heart disease from a nationwide population using neural networks. Methods and results The Swedish National Patient Register and the Cause of Death Register were used to identify all patients with congenital heart disease born from 1970 to 2017. A total of 71,941 congenital heart disease patients were identified and followed-up from birth until the event or end of study in 2017. Based on data from a nationwide population, a neural network model was obtained to predict mortality and atrial fibrillation. Logistic regression based on the same data was used as a baseline comparison. Of 71,941 congenital heart disease patients, a total of 5768 died (8.02%) and 995 (1.38%) developed atrial fibrillation over time with a mean follow-up time of 16.47 years (standard deviation 12.73 years). The performance of neural network models in predicting the mortality and atrial fibrillation was higher than the performance of logistic regression regardless of the complexity of the disease, with an average Area Under the Receiver Operating Characteristic of > 0.80 and >0.70, respectively. The largest differences were observed in mortality and complexity of congenital heart disease over time. Conclusion We found that neural networks can be used to predict mortality and atrial fibrillation on a nationwide scale using data that are easily obtainable by clinicians. In addition, neural networks showed a high performance overall and, in most cases, with better performance for prediction as compared with more traditional regression methods.

scholarly journals Cardiovascular MR and CT in congenital heart disease

Heart ◽  
2006 ◽  
Vol 93 (12) ◽  
pp. 1637-1647 ◽  
Author(s):  
A. Crean
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Congenital Heart ◽  
Cardiovascular Mr

Congenital Heart Disease: Cardiovascular MR Imaging by Using an Intravascular Blood Pool Contrast Agent

Radiology ◽  
2011 ◽  
Vol 260 (3) ◽  
pp. 680-688 ◽  
Author(s):  
Marcus R. Makowski ◽  
Andrea J. Wiethoff ◽  
Sergio Uribe ◽  
Victoria Parish ◽  
René M. Botnar ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Mr Imaging ◽  
Contrast Agent ◽  
Congenital Heart ◽  
Blood Pool ◽  
Blood Pool Contrast Agent ◽  
Cardiovascular Mr

Evaluation with Cardiovascular MR Imaging of Baffles and Conduits Used in Palliation or Repair of Congenital Heart Disease

Radiographics ◽  
2012 ◽  
Vol 32 (3) ◽  
pp. E107-E127 ◽  
Author(s):  
Jimmy C. Lu ◽  
Adam L. Dorfman ◽  
Anil K. Attili ◽  
Maryam Ghadimi Mahani ◽  
Jonathan R. Dillman ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Mr Imaging ◽  
Congenital Heart ◽  
Cardiovascular Mr

scholarly journals Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis

2021 ◽  
Vol 4 ◽  
Author(s):  
Zahra Hoodbhoy ◽  
Uswa Jiwani ◽  
Saima Sattar ◽  
Rehana Salam ◽  
Babar Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Congenital Heart Disease ◽  
Heart Disease ◽  
Diagnostic Accuracy ◽  
Congenital Heart ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Cochrane Library ◽  
Reference Standard

Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD.Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed. Studies that reported the diagnostic ability of ML for the detection of CHD compared to the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 tool. The sensitivity and specificity results from the studies were used to generate the hierarchical Summary ROC (HSROC) curve.Results: We included 16 studies (1217 participants) that used ML algorithm to diagnose CHD. Neural networks were used in seven studies with overall sensitivity of 90.9% (95% CI 85.2–94.5%) and specificity was 92.7% (95% CI 86.4–96.2%). Other ML models included ensemble methods, deep learning and clustering techniques but did not have sufficient number of studies for a meta-analysis. Majority (n=11, 69%) of studies had a high risk of patient selection bias, unclear bias on index test (n=9, 56%) and flow and timing (n=12, 75%) while low risk of bias was reported for the reference standard (n=10, 62%).Conclusion: ML models such as neural networks have the potential to diagnose CHD accurately without the need for trained personnel. The heterogeneity of the diagnostic modalities used to train these models and the heterogeneity of the CHD diagnoses included between the studies is a major limitation.

scholarly journals Cardiovascular MR Imaging in Neonates and Infants with Congenital Heart Disease

Radiographics ◽  
2007 ◽  
Vol 27 (1) ◽  
pp. 5-18 ◽  
Author(s):  
Christian J. Kellenberger ◽  
Shi-Joon Yoo ◽  
Emanuela R. Valsangiacomo Büchel
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Mr Imaging ◽  
Congenital Heart ◽  
Cardiovascular Mr ◽  
Neonates And Infants
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