Congestive Heart Failure

Integrated multi-omics analysis and machine learning developed diagnostic markers and prognostic model based on Efferocytosis-associated signatures for septic cardiomyopathy.

Septic cardiomyopathy (SCM) is characterized by an abnormal inflammatory response and increased mortality. The role of efferocytosis in SCM is not well understood. We used integrated multi-omics analysis to explore the clinical and genetic roles of efferocytosis in SCM. We identified six module genes (ATP11C, CD36, CEBPB, MAPK3, MAPKAPK2, PECAM1) strongly associated with SCM, leading to an accurat...

Applications of artificial intelligence in diagnosis of uncommon cystoid macular edema using optical coherence tomography imaging: A systematic review.

Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application...

Machine learning-based model development for predicting risk factors of prolonged intra-aortic balloon pump therapy in patients with coronary artery bypass grafting.

Machine learning algorithms are frequently used to clinical risk prediction. Our study was designed to predict risk factors of prolonged intra-aortic ...

Machine learning-based detection of sleep-disordered breathing in hypertrophic cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is often concomitant with sleep-disordered breathing (SDB), which can cause adverse cardiovascular event...

Integrating machine learning approach to identify sleep-disordered breathing in hypertrophic cardiomyopathy.

Neural network model for prediction of possible sarcopenic obesity using Korean national fitness award data (2010-2023).

Sarcopenic obesity (SO) is characterized by concomitant sarcopenia and obesity and presents a high risk of disability, morbidity, and mortality among ...

Inter-Rater and Intra-Rater Agreement in Scoring Severity of Rodent Cardiomyopathy and Relation to Artificial Intelligence-Based Scoring.

We previously developed a computer-assisted image analysis algorithm to detect and quantify the microscopic features of rodent progressive cardiomyopa...

Prediction of developmental toxic effects of fine particulate matter (PM) water-soluble components via machine learning through observation of PM from diverse urban areas.

The global health implications of fine particulate matter (PM) underscore the imperative need for research into its toxicity and chemical composition....

A new methodology for determining the central pressure waveform from peripheral measurement using Fourier-based machine learning.

Radial applanation tonometry is a well-established technique for hemodynamic monitoring and is becoming popular in affordable non-invasive wearable he...

Diabetic retinopathy screening through artificial intelligence algorithms: A systematic review.

Diabetic retinopathy (DR) poses a significant challenge in diabetes management, with its progression often asymptomatic until advanced stages. This un...

Machine learning predictions of the adverse events of different treatments in patients with ischemic left ventricular systolic dysfunction.

This study aimed to develop several new machine learning models based on hibernating myocardium to predict the major adverse cardiac events(MACE) of i...

Precise risk-prediction model including arterial stiffness for new-onset atrial fibrillation using machine learning techniques.

Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia and is an important risk factor for ischemic cerebrovascular eve...

Deep learning automatic semantic segmentation of glioblastoma multiforme regions on multimodal magnetic resonance images.

OBJECTIVES: In patients having naïve glioblastoma multiforme (GBM), this study aims to assess the efficacy of Deep Learning algorithms in automating t...

Automated assessment of cardiac dynamics in aging and dilated cardiomyopathy Drosophila models using machine learning.

The Drosophila model is pivotal in deciphering the pathophysiological underpinnings of various human ailments, notably aging and cardiovascular diseas...

Tricuspid valve flow measurement using a deep learning framework for automated valve-tracking 2D phase contrast.

PURPOSE: Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow...

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical va...

Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI).

The accurate prediction of in-hospital mortality in Asian women after ST-Elevation Myocardial Infarction (STEMI) remains a crucial issue in medical re...

Unveiling MiRNA-124 as a biomarker in hypertrophic cardiomyopathy: An innovative approach using machine learning and intelligent data analysis.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a widespread hereditary cardiac pathology characterized by thickened heart walls and rearrangement of...

Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013-2023).

BACKGROUND AND OBJECTIVES: Optical coherence tomography (OCT) has ushered in a transformative era in the domain of ophthalmology, offering non-invasiv...

A 36-nW Electrocardiogram Anomaly Detector Based on a 1.5-bit Non-Feedback Delta Quantizer for Always-on Cardiac Monitoring.

An always-on electrocardiogram (ECG) anomaly detector (EAD) with ultra-low power (ULP) consumption is proposed for continuous cardiac monitoring appli...