Related Experiment Video
Updated: Jan 8, 2026
Ultrasonic Assessment of Myocardial Microstructure
Published on: January 14, 2014
Artificial Intelligence-Based Automated Echocardiographic Analysis and the Workflow of Sonographers: A Randomized
Akira Sakamoto1, Nobuyuki Kagiyama1, Eiichiro Sato1
1Department of Cardiovascular Biology and Medicine Juntendo University Graduate School of Medicine Tokyo Japan.
Background:
This trial evaluated whether an artificial intelligence (AI)-based automatic analysis for echocardiography could improve sonographer workflow in real-world clinical practice.
Methods:
In a single-center crossover trial, 4 sonographers were randomly assigned to use AI assistance (AI days) or manual workflow (non-AI days) on a daily basis. The AI tool automatically measured echocardiographic parameters, allowing sonographers to focus on verifying AI-generated values. Expert echocardiologists finalized all reports. The primary end point was examination efficiency, measured by examination time per patient and number of examinations per day. Secondary end points included sonographer fatigue, the number of analyzed echocardiographic parameters, and image quality.
Results:
A total of 585 patients were scanned over 38 study days (AI days, 317; non-AI days, 268) between January 30 and March 26, 2024. Baseline characteristics were comparable between groups. AI assistance significantly reduced examination time (13.0±3.5 minutes versus 14.3±4.2 minutes, P<0.001) and increased the number of daily examinations (16.7±2.5 versus 14.1±2.5, P=0.003). The number of echocardiographic parameters per examination increased 3.4-fold on AI days (85±12 versus 25±1, P<0.001). Despite the higher workload, sonographers reported lower mental fatigue scores on AI days (4.1±1.1 versus 4.7±0.6, P=0.039). AI-generated measurements agreed with final expert-endorsed values within acceptable clinical limits for 90% of parameters. Notably, image quality significantly improved on AI days (P<0.001).
Conclusions:
This real-world randomized trial demonstrated that AI-based echocardiographic analysis enhances workflow efficiency, reduces sonographer fatigue, and improves image quality without compromising diagnostic integrity. AI integration holds promise for optimizing high-volume echocardiography workflows.
Registration:
URL: https://center6.umin.ac.jp. Unique identifier: UMIN000053259.
Related Concept Videos
01:17Imaging Studies for Cardiovascular System I:Echocardiography
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...
01:20Imaging Studies for Cardiovascular System II:Types of Echocardiography
Types of Echocardiography
Transthoracic Echocardiography (TTE)
TTE is the most common type of echocardiogram which involves placing a transducer on the patient's chest, emitting sound waves to create heart images. TTE is invaluable for evaluating the heart's size, structure, and motion, making it particularly useful for...