AIMC Topic: Echocardiography

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A comprehensive scoping review on machine learning-based fetal echocardiography analysis.

Computers in biology and medicine Jan 15, 2025
Fetal echocardiography (ultrasound of the fetal heart) plays a vital role in identifying heart defects, allowing clinicians to establish prenatal and postnatal management plans. Machine learning-based methods are emerging to support the automation of...
Female Neural Networks, Computer Echocardiography Heart Defects, Congenital Machine Learning Ultrasonography, Prenatal Fetal Heart Pregnancy Humans
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Artificial intelligence for left ventricular hypertrophy detection and differentiation on echocardiography, cardiac magnetic resonance and cardiac computed tomography: A systematic review.

International journal of cardiology Jan 10, 2025
AIMS: Left ventricular hypertrophy (LVH) is a common clinical finding associated with adverse cardiovascular outcomes. Once LVH is diagnosed, defining its cause has crucial clinical implications. Artificial intelligence (AI) may allow significant pro...
Diagnosis, Differential Hypertrophy, Left Ventricular Magnetic Resonance Imaging, Cine Artificial Intelligence Tomography, X-Ray Computed Echocardiography Humans
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Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data.

Scientific reports Jan 8, 2025
In recent years, the healthcare data system has expanded rapidly, allowing for the identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause of death in developed countries, often lea...
Echocardiography Cluster Analysis Heart Diseases Humans Neural Networks, Computer Algorithms
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Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer.

Journal of applied clinical medical physics Dec 20, 2024
PURPOSE: Cardiotoxicity is one of the major concerns in breast cancer treatment, significantly affecting patient outcomes. To improve the likelihood of favorable outcomes for breast cancer survivors, it is essential to carefully balance the potential...
Adult Breast Neoplasms Radiomics Echocardiography Middle Aged Radiation Injuries Organs at Risk Cardiotoxicity Machine Learning Unilateral Breast Neoplasms Female Humans Radiotherapy Planning, Computer-Assisted Aged Radiotherapy Dosage Radiotherapy, Intensity-Modulated
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Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements.

Open heart Dec 18, 2024
BACKGROUND: Cardiac amyloidosis (CA) is an underdiagnosed, progressive and lethal disease. Machine learning applied to common measurements derived from routine echocardiogram studies can inform suspicion of CA.
Cardiomyopathies Middle Aged Predictive Value of Tests Amyloidosis Male Echocardiography Humans Female Machine Learning Reproducibility of Results Retrospective Studies Aged
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Utility of an Echocardiographic Machine Learning Model to Predict Outcomes in Intensive Cardiac Care Unit Patients.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography Dec 14, 2024
INTRODUCTION: The risk stratification at admission to the intensive cardiac care unit (ICCU) is crucial and remains challenging.
Echocardiography Middle Aged Risk Assessment Female Aged Male Hospital Mortality Coronary Care Units Humans Machine Learning Prospective Studies Prognosis Intensive Care Units
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Deep learning for cardiac imaging: focus on myocardial diseases, a narrative review.

Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese Dec 9, 2024
The integration of computational technologies into cardiology has significantly advanced the diagnosis and management of cardiovascular diseases. Computational cardiology, particularly, through cardiovascular imaging and informatics, enables a precis...
Echocardiography Deep Learning Cardiomyopathies Humans Cardiac Imaging Techniques
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BEAS-Net: A Shape-Prior-Based Deep Convolutional Neural Network for Robust Left Ventricular Segmentation in 2-D Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Nov 27, 2024
Left ventricle (LV) segmentation of 2-D echocardiography images is an essential step in the analysis of cardiac morphology and function and-more generally-diagnosis of cardiovascular diseases (CVD). Several deep learning (DL) algorithms have recently...
Convolutional Neural Networks Algorithms Image Processing, Computer-Assisted Humans Echocardiography Heart Ventricles Neural Networks, Computer Deep Learning
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Hardware-Independent Deep Signal Processing: A Feasibility Study in Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Nov 27, 2024
Deep learning (DL) models have emerged as alternative methods to conventional ultrasound (US) signal processing, offering the potential to mimic signal processing chains, reduce inference time, and enable the portability of processing chains across h...
Heart Feasibility Studies Deep Learning Image Processing, Computer-Assisted Signal Processing, Computer-Assisted Humans Algorithms Echocardiography
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Spatiotemporal Deep Learning-Based Cine Loop Quality Filter for Handheld Point-of-Care Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Nov 27, 2024
The reliability of automated image interpretation of point-of-care (POC) echocardiography scans depends on the quality of the acquired ultrasound data. This work reports on the development and validation of spatiotemporal deep learning models to asse...
Image Interpretation, Computer-Assisted Humans Echocardiography Deep Learning Heart Ventricles Algorithms Reproducibility of Results Point-of-Care Systems
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