AI-Supported Echocardiography for the Detection of Heart Diseases - A Scoping Review.

INTRODUCTION

Cardiovascular diseases are a leading cause of mortality worldwide, highlighting the urgent need for accurate and efficient diagnostic tools. Echocardiography, a non-invasive imaging technique, plays a central role in the diagnosis of heart diseases, yet the potential impact of artificial intelligence (AI) on its accuracy and speed has not yet been reviewed and summarized. This scoping review aims to address this research gap by synthesizing existing evidence on AI-assisted echocardiography's.

METHODS

The study followed Arksey and O'Malley's six-stage model for scoping reviews and searched the databases PubMed, Web of Science and Livivo. Inclusion criteria encompassed studies from cardiology utilizing AI for heart diseases diagnosis in adults, published from 2018 to 2023. Data extraction focused on study characteristics, AI models employed, accuracy metrics, and diagnostic speed.

RESULTS

From 1059 identified studies, nine records met the inclusion criteria, categorized into view classification, left ventricular ejection fraction (LVEF) quantification, and diseases classification. Convolutional Neural Networks (CNN) were commonly used. While 44% of studies compared AI with cardiologists, those studies indicated AI's high diagnostic accuracy, with mean accuracy ranging from 87% to 92%. Three studies assessed AI's speed, demonstrating significant time savings.

DISCUSSION

The review highlights AI's potential in enhancing diagnostic accuracy and efficiency in echocardiography, particularly in regions with limited access to specialized cardiologists. However, further research is needed to assess AI's specific added value compared to cardiologists, optimize training data quality, and enable real-time image processing.