Congestive Heart Failure

Objective prediction of siesta based on machine learning and association with obesity.

OBJECTIVES: To predict siesta behavior using machine learning models trained on self-reported and objective data-temperature (T), activity (A), position (P), and the integrated TAP variable-and to explore its associations with obesity-related traits. METHODS: From ONTIME-MT, 889 adults wore wrist sensors for 7 days to continuously record temperature, activity, and position, and self-reported daily...

Effect of a mobile health intervention optimised with artificial intelligence on blood pressure and other cardiovascular risk factors in adults with high blood pressure: rationale and design of My Intelligent Cardiac Assistant (MICArdiac) randomised controlled trial.

INTRODUCTION: Control of blood pressure (BP) continues to be a challenge globally. Clinical trials have shown home BP monitoring and text-message interventions to lower BP. Integrating these to personalise supportive messaging in response to changing BP and activity through leveraging artificial intelligence (AI) could improve BP control. The aim of this trial is to examine the impact on BP, compa...

DIA-PINN. A physics-informed machine learning method to estimate global intrinsic diastolic chamber properties of the left ventricle from pressure-volume data.

Background: Pressure-volume (PV) loop analysis remains the gold standard for assessing the intrinsic global diastolic properties of the left ventricle...

Personalized artificial intelligence based left ventricular ejection fraction and systolic dysfunction assessment.

Left-ventricular (LV) ejection fraction (LVEF) is a fundamental measure of cardiac function, typically assessed with resource-intensive imaging techni...

Enhancing prediabetes and diabetes detection through a machine learning-enabled self-assessment approach.

OBJECTIVES: Reliable, accessible, noninvasive self-assessment screening for prediabetes/diabetes is lacking, leading to missed opportunities for early...

Determinants of LV Myocardial Dyssynchrony in CHF: Impact of Mechanical and Electrical Dyssynchrony.

Chronic heart failure (CHF) patients often present with heterogeneous patterns of cardiac dyssynchrony. Although QRS prolongation (>150 ms) and left b...

Artificial intelligence-based detection of acute postoperative airway complications following anterior cervical spine surgery: a retrospective imaging evaluation.

STUDY DESIGN: Retrospective imaging evaluation using an artificial intelligence (AI)-generated model. PURPOSE: To develop novel AI software for early ...

Artificial Intelligence-Enabled Prediction of Reversible Versus Irreversible Chemotherapy-Induced Myocardial Injury: Toward Precision Cardio-Oncology.

Cancer therapy-related cardiac dysfunction remains a major cause of morbidity among cancer survivors and may interrupt life-saving oncologic therapy o...

A novel machine learning model of smartphone-based 1-minute sit-to-stand test for prediction of six-minute walk test distance in patients with COPD.

BACKGROUND: Regular assessment of exercise tolerance is essential for managing COPD, nevertheless, the standard 6-Minute Walk Test (6MWT) is difficult...

Improving transthyretin cardiac amyloidosis detection from electrocardiograms through the Willem artificial intelligence platform.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequently underdiagnosed disease in which delay in diagnosis limits the efficacy of t...

Annular Prior Prompt Learning for Medical Images Segmentation.

Excellent performance has been achieved on medical image segmentation. Still, existing algorithms perform relatively poorly for annular objects with h...

Feasibility of Free-breathing Deep Learning-reconstructed Single-Shot Cine MRI in Participants with Arrhythmia: Comparison with Conventional Segmented Cine MRI.

Purpose To evaluate the feasibility of retrospective electrocardiographically (ECG) gated single-shot cine using deep learning-enhanced compressed sen...

Pre-heart failure in early chronic kidney disease: clinical epidemiology and treatment.

Chronic kidney disease (CKD), defined per the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines by persistent (≥3 months) abnormalities of ...

Multi-label Ensemble Model for Knee Joint Anatomy and Lesion Segmentation: Segmentation of Clinical Images With Ensembling, Morphology, and Attention (SCEMA).

PURPOSE: Quantitative MRI analysis holds significant promise for improving the early diagnosis and prognosis of osteoarthritis (OA), where accurate ti...

Predictive accuracy of a perioperative hemodynamic indices-based prediction model for moderate-to-severe acute kidney injury after orthotopic heart transplantation.

BACKGROUND: Acute kidney injury is a common complication after orthotopic heart transplantation. Previous models have failed to consider the impact of...

Assessing the Predictive Value of Kolmogorov-Arnold Networks for the No-Reflow Phenomenon in ST-Segment Elevation Myocardial Infarction: A Comparative Machine Learning Study.

OBJECTIVE: The no-reflow phenomenon in ST-segment elevation myocardial infarction (STEMI) is a significant clinical issue associated with poor cardiov...

Multi-scale adaptive fusion network for retinal layer and fluid segmentation in optical coherence tomography B-scans.

Major treats to visual health includes diabetic macular edema (DME), age-related macular degeneration (AMD) and retinal vein occlusion (RVO), which re...

Using machine learning algorithms to predict MACE in peritoneal dialysis patients.

The study aimed to predict the risks of Major adverse cardiac events (MACE) in patients undergoing peritoneal dialysis (PD) with machine learning (ML)...

Emerging perspectives in cardiovascular medicine.

Cardiovascular medicine continues to evolve rapidly through advances in molecular biology, biomarkers, digital technologies, and interventional strate...

Construction and validation of high altitude pulmonary edema prediction models based on deep learning and quantitative analysis of X-ray images.