Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Automatic computation of breast cancer biomarkers from multiple [Formula: see text] F-FDG PET image segmentation.

Scientific reports·2026

Same author

Toward a minimalist strategy for PFO closure: outcomes and patient experience from 205 ICE-guided procedures performed under local anesthesia and early discharge.

Cardiovascular intervention and therapeutics·2026

Same author

Low-Dose Rivaroxaban to Prevent Left Ventricular Thrombosis After Anterior Myocardial Infarction: The APERITIF Randomized Clinical Trial.

JAMA cardiology·2026

Same author

In-depth understanding of the multimodal deformable image registration tool of a 0.35 T MR-linac.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists·2026

Same author

Dual Diagnosis by Cardiac MRI of Takotsubo Syndrome and Adrenal Pheochromocytoma in Pregnancy.

Circulation. Cardiovascular imaging·2026

Same author

Incidence and Predictors of Hypoattenuated Thickening and Device-Related Thrombus at Three Months Postprocedural CT Assessment Following Left Atrial Appendage Occlusion With Amplatzer Devices-A Single-Center Cohort.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions·2025

Same journal

Radiation-associated bioprosthetic valve dysfunction: an initial case-control analysis.

Frontiers in cardiovascular medicine·2026

Same journal

"Pericoronary adipose tissue attenuation as a marker of global coronary inflammation rather than lesion-specific risk".

Frontiers in cardiovascular medicine·2026

Same journal

Magnetocardiography in the diagnosis of suspected ANOCA: a case series.

Frontiers in cardiovascular medicine·2026

Same journal

Bidirectional association between COPD and AF: a systematic review and meta-analysis.

Frontiers in cardiovascular medicine·2026

Same journal

Comparison of the triglyceride/high-density lipoprotein ratio, lipid profile, and glycated hemoglobin and their association with coronary artery disease in the older adult Saudi population: a case-control study.

Frontiers in cardiovascular medicine·2026

Same journal

Homocystinuria presenting with cerebral venous thrombosis: a case report highlighting progressive thrombosis.

Frontiers in cardiovascular medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 28, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Prediction of Myocardial Infarction From Patient Features With Machine Learning.

Zhihao Chen¹, Jixi Shi^1,2, Thibaut Pommier³

¹FEMTO-ST Institute, UMR 6174 CNRS, Univ. Bourgogne Franche-Comté, Belfort, France.

Frontiers in Cardiovascular Medicine

|April 4, 2022

Summary

This summary is machine-generated.

Machine learning models accurately assess myocardial infarction (MI) severity using clinical data. Troponin levels are key indicators, enabling faster treatment decisions in emergencies before MRI.

Keywords:

DE-MRI acute myocardial infarction automatic prediction clinical characteristics machine learning

More Related Videos

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Published on: September 22, 2020

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Related Experiment Videos

Last Updated: Sep 28, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Published on: September 22, 2020

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Cardiology
Medical Imaging
Machine Learning

Background:

Myocardial infarction (MI) assessment relies on complex diagnostics.
Accurate quantification of MI severity and microvascular obstruction is crucial for treatment.

Purpose of the Study:

To develop and evaluate machine learning models for automatic MI severity assessment.
To classify MI presence and persistent microvascular obstruction (PMO).
To quantify the Percentage of Infarcted Myocardium (PIM).

Main Methods:

Supervised machine learning models (classification and regression) were trained.
Ground truth labels were derived from Delayed Enhancement MRI (DE-MRI) and manual annotations.
Models were evaluated on 150 cases using cross-validation.

Main Results:

Best models achieved 88.67% classification accuracy for MI and 77.33% for PMO.
Quantification yielded mean errors of 0.056 for MI and 0.012 for PMO.
Troponin levels showed the strongest correlation with MI severity among 12 features.

Conclusions:

Machine learning models can reliably assess MI severity using conventional clinical and paraclinical data.
Early, objective MI assessment is possible before MRI, aiding emergency treatment decisions.
Troponin levels are a significant predictor of MI severity.