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Cardiomyopathy V: Interprofessional Care01:29

Cardiomyopathy V: Interprofessional Care

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Managing cardiomyopathy involves addressing underlying or precipitating causes, treating heart failure with medications, and implementing dietary changes and a balanced exercise and rest regimen.Lifestyle ModificationsCardiomyopathy patients should adopt a low-sodium diet to reduce fluid retention and manage heart failure. A personalized exercise and rest plan helps maintain physical fitness without overstraining the heart. Avoiding alcohol and tobacco is essential to prevent further damage to...
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Implantation of the Syncardia Total Artificial Heart
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Artificial intelligence in cardiac surgery: A systematic review.

Ralf Martz Sulague1,2, Francis Joshua Beloy3, Jillian Reeze Medina4

  • 1Graduate School of Arts and Sciences, Georgetown University, Washington, District of Columbia, USA.

World Journal of Surgery
|July 17, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) is increasingly used in cardiac surgery for risk assessment and complication prediction. Further research is needed to ensure AI

Keywords:
artificial intelligencecardiac surgerydeep learningmachine learningtechnology

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Area of Science:

  • Cardiovascular Surgery
  • Medical Artificial Intelligence
  • Machine Learning in Medicine

Background:

  • Artificial intelligence (AI) offers potential for enhancing cardiovascular care efficiency, efficacy, and patient outcomes.
  • Cardiac surgery is a key area where AI applications are being explored to improve clinical practice.

Purpose of the Study:

  • To provide a comprehensive overview of current Artificial Intelligence applications within cardiac surgery.
  • To identify trends, popular machine learning techniques, and common application areas in AI-driven cardiac surgery research.

Main Methods:

  • A systematic literature search was performed for AI applications in cardiac surgery up to February 2024.
  • Studies were filtered using inclusion/exclusion criteria, and risk of bias was assessed.
  • Key findings from the selected studies were summarized.

Main Results:

  • 81 studies reported AI applications in cardiac surgery, with a notable increase since 2020.
  • Random forest was the most common machine learning technique, followed by support vector machine, logistic regression, and eXtreme Gradient Boosting.
  • Studies focused on valvular surgery, heart transplant, coronary revascularization, and congenital heart disease surgery, primarily in adult patients.

Conclusions:

  • AI primarily aids in predicting post-operative complications and enhancing clinical decision-making through risk assessment and prognostication.
  • Significant progress in AI for cardiac surgery has been observed, but clinical validation of accuracy and safety is crucial before widespread adoption.