Artificial Intelligence-Based Automated Echocardiographic Analysis and the Workflow of Sonographers: A Randomized...

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Evaluating the impact of artificial intelligence (AI) on echocardiography workflows in clinical settings.
• Assessing AI's role in improving sonographer efficiency and patient care.

Purpose of the Study:

• To determine if AI-based automatic analysis of echocardiograms can enhance sonographer workflow.
• To measure the effects of AI assistance on examination efficiency, sonographer fatigue, and image quality.

Main Methods:

• A single-center crossover trial involving 4 sonographers comparing AI-assisted and manual echocardiography workflows.
• Primary endpoint: examination efficiency (time per patient, examinations per day).
• Secondary endpoints: sonographer fatigue, number of parameters analyzed, and image quality.

Main Results:

• AI assistance reduced examination time by 9% and increased daily examinations by 18%.
• The number of echocardiographic parameters analyzed per examination increased 3.4-fold with AI.
• Sonographer mental fatigue scores decreased, and image quality significantly improved with AI assistance.

Conclusions:

• AI-based echocardiographic analysis improves workflow efficiency, reduces sonographer fatigue, and enhances image quality.
• AI integration is a promising strategy for optimizing high-volume echocardiography practices.
• The study demonstrates AI's potential to maintain diagnostic integrity while streamlining echocardiography procedures.