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Imaging Studies for Cardiovascular System I:Echocardiography

Imaging Studies for Cardiovascular System I:Echocardiography

Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion, evaluates...

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Transthoracic Echocardiography in Mice

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Automated echocardiographic left ventricular dimension assessment in dogs using artificial intelligence: Development

Catherine C Stowell¹, Valeria Kallassy², Beth Lane¹

¹National Heart and Lung Institute (NHLI), Imperial College, London, UK.

Journal of Veterinary Internal Medicine

|February 16, 2024

Summary

Artificial intelligence (AI) can accurately measure canine echocardiographic left ventricular (LV) dimensions. The AI’s performance was comparable to veterinary experts, showing potential for improved accuracy and reproducibility in canine cardiology.

Keywords:

artificial intelligence echocardiography vendor-neutral

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Published on: December 11, 2017

Area of Science:

Veterinary cardiology
Artificial intelligence in medicine
Medical imaging analysis

Background:

Echocardiographic measurements in dogs can lack accuracy and reproducibility.
Artificial intelligence (AI) offers potential to enhance these measurements.

Purpose of the Study:

To train a neural network for measuring left ventricular (LV) linear dimensions in canine echocardiograms.
To evaluate the AI's performance against expert measurements.

Main Methods:

A neural network was trained on 1398 echocardiogram frames from 461 dogs.
AI measurements were compared to consensus expert annotations on 50 validation studies.
Accuracy was quantified by deviation from expert consensus.

Main Results:

AI measurements showed no significant difference from expert consensus for septal dimensions in end-diastole.
AI performance was comparable or superior to experts for LV dimensions (LVD) and free wall measurements.
No performance differences were observed between AI and experts in end-systole.

Conclusions:

An AI network can be effectively trained to measure linear LV dimensions in dogs.
The AI's measurement performance is indistinguishable from that of experienced veterinary cardiologists.
AI holds promise for improving accuracy and reproducibility in canine echocardiography.