Congestive Heart Failure

Deep learning method with integrated invertible wavelet scattering for improving the quality ofcardiac DTI.

Respiratory motion, cardiac motion and inherently low signal-to-noise ratio (SNR) are major limitati...

The promise of machine learning models in cardiovascular risk prediction : Machine learning predictions of the adverse events of different treatments in patients with ischemic left ventricular systolic dysfunction.

Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.

Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, ...

Automated echocardiographic diastolic function grading: A hybrid multi-task deep learning and machine learning approach.

BACKGROUND: Assessing left ventricular diastolic function (LVDF) with echocardiography as per ASE gu...

EFNet: A multitask deep learning network for simultaneous quantification of left ventricle structure and function.

PURPOSE: The purpose of this study is to develop an automated method using deep learning for the rel...

Prediction of treatment outcome for branch retinal vein occlusion using convolutional neural network-based retinal fluorescein angiography.

Deep learning techniques were used in ophthalmology to develop artificial intelligence (AI) models f...

Optimized deep CNN for detection and classification of diabetic retinopathy and diabetic macular edema.

Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are vision related complications prominen...

Accurate low and high grade glioma classification using free water eliminated diffusion tensor metrics and ensemble machine learning.

Glioma, a predominant type of brain tumor, can be fatal. This necessitates an early diagnosis and ef...

Improved diagnosis of arrhythmogenic right ventricular cardiomyopathy using electrocardiographic deep learning.

BACKGROUND: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a rare genetic heart disease a...

A novel approach for automatic classification of macular degeneration OCT images.

Age-related macular degeneration (AMD) and diabetic macular edema (DME) are significant causes of bl...

Rethinking masked image modelling for medical image representation.

Masked Image Modelling (MIM), a form of self-supervised learning, has garnered significant success i...

Artificial intelligence and myocarditis-a systematic review of current applications.

Myocarditis, marked by heart muscle inflammation, poses significant clinical challenges. This study,...

Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer.

To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in pr...

Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.

The generalization of deep neural network algorithms to a broader population is an important challen...

A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defin...

Artificial intelligence-driven electrocardiography: Innovations in hypertrophic cardiomyopathy management.

Hypertrophic Cardiomyopathy (HCM) presents a complex diagnostic and prognostic challenge due to its ...

Assessment of left ventricular wall thickness and dimension: accuracy of a deep learning model with prediction uncertainty.

Left ventricular (LV) geometric patterns aid clinicians in the diagnosis and prognostication of vari...

Mavacamten in hypertrophic obstructive cardiomyopathy: Prospects for AI integration and mitigating healthcare disparities.

Hypertrophic obstructive cardiomyopathy (HOCM) is an autosomal dominant condition that still remains...

Non-Contact Blood Pressure Estimation From Radar Signals by a Stacked Deformable Convolution Network.

This study introduces a contactless blood pressure monitoring approach that combines conventional ra...

Hybrid deep learning models for the screening of Diabetic Macular Edema in optical coherence tomography volumes.

Several studies published so far used highly selective image datasets from unclear sources to train ...

A personalized mRNA signature for predicting hypertrophic cardiomyopathy applying machine learning methods.

Hypertrophic cardiomyopathy (HCM) may lead to cardiac dysfunction and sudden death. This study was d...