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Image-Based Cardiac Diagnosis With Machine Learning: A Review.

Carlos Martin-Isla¹, Victor M Campello¹, Cristian Izquierdo¹

¹Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain.

Frontiers in Cardiovascular Medicine

|February 11, 2020

Summary

Artificial intelligence (AI) in cardiac imaging enhances cardiovascular disease (CVD) diagnosis. Machine learning methods offer automated, precise, and early detection, improving patient outcomes.

Keywords:

artificial intelligence automated diagnosis cardiac imaging cardiovascular disease deep learning machine learning radiomics

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Area of Science:

Cardiology
Medical Imaging
Artificial Intelligence

Background:

Cardiac imaging is crucial for diagnosing cardiovascular disease (CVD).
Current methods focus on visual and quantitative assessments.
Limitations exist in current diagnostic capabilities.

Purpose of the Study:

To review recent advancements in AI for cardiac imaging.
To present machine learning (ML) methods for CVD diagnosis.
To highlight opportunities for automated and precise CVD detection.

Main Methods:

Comprehensive literature review of AI and ML in cardiac imaging.
Analysis of machine learning algorithms applied to cardiovascular diagnostics.
Synthesis of current research trends and future potential.

Main Results:

Emerging AI tools assist clinicians in CVD diagnosis.
Machine learning enables more automated and precise assessments.
Potential for earlier detection of cardiovascular diseases.

Conclusions:

AI and ML are transforming cardiac imaging diagnostics.
These technologies promise more accurate and timely CVD identification.
Further exploitation of ML methods is key for improved patient care.