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Ultrasonic Assessment of Myocardial Microstructure
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Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes

Riccardo Cau1, Francesco Pisu1, Jasjit S Suri2

  • 1Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato s.s. 554 Monserrato, 09045 Cagliari, Italy.

Diagnostics (Basel, Switzerland)
|January 22, 2024
PubMed

Insights

Artificial intelligence (AI) aids in diagnosing cardiomyopathies, a major cause of heart failure. This review explores AI

Area of Science:

  • Cardiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Cardiomyopathies are a primary cause of heart failure and arrhythmias.
  • Accurate diagnosis requires integrating diverse patient data, posing clinical challenges.
  • Artificial intelligence (AI) shows promise in analyzing complex medical datasets.

Purpose of the Study:

  • To provide an overview of AI concepts in medicine.
  • To review AI models for differential diagnosis of cardiomyopathy phenotypes.
  • To examine the benefits and drawbacks of AI in cardiology.

Main Methods:

  • Literature review of AI applications in cardiovascular diseases.
  • Focus on AI models for cardiomyopathy diagnosis.
  • Analysis of AI's role in integrating multiparametric patient data.

Main Results:

  • AI can identify subtle patterns in complex datasets for improved disease stratification.
  • Existing literature demonstrates AI's potential in aiding cardiomyopathy diagnosis.
  • AI offers advanced capabilities beyond traditional diagnostic methods.

Conclusions:

  • AI is a valuable tool for the differential diagnosis of cardiomyopathies.
  • Further research is needed to fully understand AI's advantages and limitations in clinical practice.
  • AI integration can enhance cardiovascular disease management and patient outcomes.

Keywords:
artificial intelligencecardiomyopathydeep learningmachine learning

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