# Evaluation of the DAMSUN-HF trial: the role of an artificial intelligence stethoscope in detecting reduced ejection fraction in patients living in a low-resource region

## TL;DR

This study evaluates an AI-based stethoscope for detecting heart failure in low-resource areas where access to traditional heart tests is limited.

## Contribution

The study introduces an AI-based stethoscope as a potential alternative for detecting reduced ejection fraction in low-resource settings.

## Key findings

- The AI-based stethoscope showed potential for detecting reduced ejection fraction in heart failure patients.
- The study highlights the feasibility of using portable AI technology in regions with limited access to echocardiography.
- Results suggest the AI stethoscope could improve timely diagnosis in low-resource areas.

## Abstract

Evaluation of ejection fraction (EF) is paramount for patients with symptoms of heart failure. While transthoracic echocardiography (TTE) is the most common way to evaluate EF, recent advances in artificial intelligence (AI) have opened the door for alternative methods to screen for reduced EF with smaller and more portable technology. The DAMSUN-HF study evaluated the accuracy of an AI-based stethoscope for detecting reduced EF (≤40%) in patients with symptoms of heart failure in a region with geographic and economic barriers to obtaining timely TTE. This mini-review examines the DAMSUN-HF study and highlights the potential clinical implications of the study findings.