AIMC Topic: Heart Defects, Congenital
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Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms.
IEEE journal of biomedical and health informatics
Apr 4, 2024
One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the heart structure. The current gold-standard assessment technique, echocardiography, causes delays in the diagnosis owing to the need for experts who vary...
Machine learning prediction model of major adverse outcomes after pediatric congenital heart surgery: a retrospective cohort study.
International journal of surgery (London, England)
Apr 1, 2024
BACKGROUND: Major adverse postoperative outcomes (APOs) can greatly affect mortality, hospital stay, care management and planning, and quality of life. This study aimed to evaluate the performance of five machine learning (ML) algorithms for predicti...
A motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Mar 22, 2024
BACKGROUND: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for three-dimensional (3D) whole-heart acqu...
Gated cardiac CT in infants: What can we expect from deep learning image reconstruction algorithm?
Journal of cardiovascular computed tomography
Mar 12, 2024
BACKGROUND: ECG-gated cardiac CT is now widely used in infants with congenital heart disease (CHD). Deep Learning Image Reconstruction (DLIR) could improve image quality while minimizing the radiation dose.
Electrocardiography
Age Factors
Predictive Value of Tests
Reproducibility of Results
Infant
Deep Learning
Heart Defects, Congenital
Phantoms, Imaging
Computed Tomography Angiography
Radiation Exposure
Coronary Angiography
Radiographic Image Interpretation, Computer-Assisted
Radiation Dosage
Humans
Cardiac-Gated Imaging Techniques
hART: Deep learning-informed lifespan heart failure risk trajectories.
International journal of medical informatics
Feb 19, 2024
BACKGROUND: Heart failure (HF) results in persistent risk and long-term comorbidities. This is particularly true for patients with lifelong HF sequelae of cardiovascular disease such as patients with congenital heart disease (CHD).
Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts.
Nature communications
Feb 1, 2024
Early detection is critical to achieving improved treatment outcomes for child patients with congenital heart diseases (CHDs). Therefore, developing effective CHD detection techniques using low-cost and non-invasive pediatric electrocardiogram are hi...
Advances in the Application of Artificial Intelligence in Fetal Echocardiography.
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
Jan 9, 2024
Congenital heart disease is a severe health risk for newborns. Early detection of abnormalities in fetal cardiac structure and function during pregnancy can help patients seek timely diagnostic and therapeutic advice, and early intervention planning ...
Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images.
PloS one
Dec 14, 2023
COVID-19 has potential consequences on the pulmonary and cardiovascular health of millions of infected people worldwide. Chest computed tomographic (CT) imaging has remained the first line of diagnosis for individuals infected with SARS-CoV-2. Howeve...
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...
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