AIMC Topic: Myocardial Infarction

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A cascade approach for the early detection and localization of myocardial infarction in 2D-echocardiography.

Medical engineering & physics
Myocardial infarction (MI) detection and localization through echocardiography are crucial for effective patient management. However, current diagnostic approaches rely heavily on visual assessment, which can be subjective. In this work we developed ...

Comparison of synthetic LGE with optimal inversion time vs. conventional LGE via representation learning: Quantification of Bias in Population Analysis.

Computers in biology and medicine
PURPOSE: Late Gadolinium Enhancement (LGE) images are crucial elements of CMR protocols for evaluating myocardial infarct (MI) severity and size. However, these images rely on signal intensity changes and manual inversion time (TI) settings, leading ...

Integrating radiomic texture analysis and deep learning for automated myocardial infarction detection in cine-MRI.

Scientific reports
Robust differentiation between infarcted and normal myocardial tissue is essential for improving diagnostic accuracy and personalizing treatment in myocardial infarction (MI). This study proposes a hybrid framework combining radiomic texture analysis...

Enhanced machine learning models for predicting three-year mortality in Non-STEMI patients aged 75 and above.

BMC geriatrics
BACKGROUND: Non-ST segment elevation myocardial infarction (Non-STEMI) is a severe cardiovascular condition mainly affecting individuals aged 75 and above, who are at higher risk of mortality due to age-related vulnerabilities and other health issues...

Identification of key genes associated with cellular aging and mitochondria in acute myocardial infarction.

Scientific reports
Acute myocardial infarction (AMI) poses a significant global mortality burden. Utilizing bio informatics, this study explored cellular aging-related genes (CARGs) and mitochondrial-related genes (MRGs). in AMI Public AMI datasets were analyzed using ...

Empowering heart attack treatment for women through machine learning and optimization techniques.

Computers in biology and medicine
Heart attack detection and treatment in women remain significantly under-optimized due to differences in symptom presentation and physiological characteristics compared to men, leading to delayed or incorrect diagnoses. Addressing this gap, this stud...

3D cardiac shape analysis with variational point cloud autoencoders for myocardial infarction prediction and virtual heart synthesis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiac anatomy and physiology vary considerably across the human population. Understanding and taking into account this variability is crucial for both accurate clinical decision-making and realistic in silico modeling of cardiac function. In this w...

Dual energy CT-based Radiomics for identification of myocardial focal scar and artificial beam-hardening.

International journal of cardiology
BACKGROUND: Computed tomography is an inadequate method for detecting myocardial focal scar (MFS) due to its moderate density resolution, which is insufficient for distinguishing MFS from artificial beam-hardening (BH). Virtual monochromatic images (...

A preliminary study on cause‑of‑death discrimination and the pathological stage identification in acute ischemia heart disease (AIHD) based on plasma lipidomic technique and machine learning algorithms.

International journal of legal medicine
The sudden death discrimination of acute ischemia heart disease (AIHD) and the determination of the AIHD pathological stage are the difficulties in forensic medicine. More potential biomarkers with high sensitivity and specificity still need to be id...

A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models.

BMC medical informatics and decision making
BACKGROUND AND OBJECTIVE: The machine learning (ML) models for acute myocardial infarction (AMI) are considered to have better predictive ability for mortality compared to conventional risk scoring models. However, previous ML prediction models have ...