AIMC Topic: Heart Defects, Congenital
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The Evolving Role of Machine Learning in the Analysis of Outcomes After Pediatric and Congenital Cardiac Surgery.
The Annals of thoracic surgery
Dec 9, 2023
Congenital Heart Surgery Machine Learning-Derived In-Depth Benchmarking Tool.
The Annals of thoracic surgery
Dec 6, 2023
BACKGROUND: We previously showed that machine learning-based methodologies of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery and assess case-mix-adjusted performance after benchmark procedures. We exten...
Deep Learning Models for Predicting Left Heart Abnormalities From Single-Lead Electrocardiogram for the Development of Wearable Devices.
Circulation journal : official journal of the Japanese Circulation Society
Nov 14, 2023
BACKGROUND: Left heart abnormalities are risk factors for heart failure. However, echocardiography is not always available. Electrocardiograms (ECGs), which are now available from wearable devices, have the potential to detect these abnormalities. Ne...
A cluster-based ensemble approach for congenital heart disease prediction.
Computer methods and programs in biomedicine
Nov 7, 2023
BACKGROUND: One of the most prevalent birth disorders is congenital heart diseases (CHD). Although CHD risk factors have been the subject of numerous studies, their propensity to cause CHD has not been tested. Particularly few research has attempted ...
Commentary: Is machine learning needed to understand that heart failure is a risk in adult congenital cardiac surgery?
The Journal of thoracic and cardiovascular surgery
Oct 14, 2023
Impact of heart failure on reoperation in adult congenital heart disease: An innovative machine learning model.
The Journal of thoracic and cardiovascular surgery
Sep 28, 2023
OBJECTIVES: The study objectives were to evaluate the association between preoperative heart failure and reoperative cardiac surgical outcomes in adult congenital heart disease and to develop a risk model for postoperative morbidity/mortality.
Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease.
Acta obstetricia et gynecologica Scandinavica
Aug 10, 2023
INTRODUCTION: This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence.
Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning.
Journal of perinatal medicine
May 11, 2023
OBJECTIVES: Congenital heart defects (CHDs) are the most common birth defects. Recently, artificial intelligence (AI) was used to assist in CHD diagnosis. No comparison has been made among the various types of algorithms that can assist in the prenat...
3D ECG display with deep learning approach for identification of cardiac abnormalities from a variable number of leads.
Physiological measurement
Feb 16, 2023
The objective of this study is to explore new imaging techniques with the use of the deep learning method for the identification of cardiac abnormalities present in electrocardiogram (ECG) signals with 2, 3, 4, 6 and 12-lead in the framework of the P...
Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart.
Computational and mathematical methods in medicine
Jan 24, 2023
Congenital heart defect (CHD) refers to the overall structural abnormality of the heart or large blood vessels in the chest cavity. It is the most common type of fetal congenital defects. Prenatal diagnosis of congenital heart disease can improve the...
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