en POLSKI
eISSN: 2449-6731
ISSN: 2449-6723
Prenatal Cardiology
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1/2022
 
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abstract:
Research paper

Evaluation of an artificial intelligence algorithm (HeartAssistᵀᴹ) in the assessment of fetal cardiothoracic ratio: a prospective study

Maria Elena Pietrolucci
1
,
Pavjola Maqina
1
,
Sara Greco
1
,
Giorgia Berti
1
,
Virginia Viviano
1
,
Giuseppe Rizzo
1

1.
Department of Obstetrics and Gynecology, Fondazione Policlinico tor Vergata, Università Roma Tor Vergata, Rome, Italy
Prenat Cardio 2022
Online publish date: 2023/01/11
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Introduction
To study intra-observer and inter-observer repeatability and inter-method agreement between manual and automatic methods in assessing fetal thoracic circumference (TC), cardiac circumference (CC), and cardiac/thoracic circumference ratio (C/T).

Material and methods
In a prospective study on low-risk pregnant women undergoing second-trimester ultrasonographic examination, a frame of the thoracic circumference was obtained at the level of the 4-chamber view. For each frame the TC, CC, and C/T were measured offline by 2 examiners manually and by HeartAssistTM artificial intelligence software. Intra- and inter-observer repeatability and inter-method agreement between manual and automatic methods were analysed.

Results
Fifty consecutive pregnant women were considered at a median gestational age of 20.9 weeks. All intraclass correlation coefficients (ICC) comparing manual with heart assist were > 0.929. Intra- and inter-observers ICC were respectively > 0.971 and > 0.931 for all the 3 variables, representing good agreement. The time necessary to obtain the measurements was significantly lower using heart assist than the manual method (82 s vs. 22.5 s; p < 0.0001).

Conclusions
Heart assist allows automatic measurement of the C/T ratio. This technique was reproducible and reached the same accuracy as that of manual measurements. Heart assist is faster than manual and has the potential to become the preferred technique to obtain cardiac biometry.

keywords:

cardiothoracic ratio, fetal echocardiography, artificial intelligence

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