RETIRED: Determination of Gestational Age by Ultrasound

doi:10.1016/S1701-2163(15)30664-2

Journal of Obstetrics and Gynaecology Canada

Volume 36, Issue 2, February 2014, Pages 171-181

https://doi.org/10.1016/S1701-2163(15)30664-2 Get rights and content

This document has been archived because it contains outdated information. It should not be consulted for clinical use, but for historical research only. Please visit the journal website for the most recent guidelines.

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DeepGA for automatically estimating fetal gestational age through ultrasound imaging
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Citation Excerpt :
Gestational age (GA) determines the estimated date of delivery (EDD), and its prognosis forms an integral part of the prenatal screening examination [1]. Accurate GA estimation is essential for screening fetal abnormalities, allowing rational planning of the screening examination and prenatal monitoring [2]. The examination of GA is routinely offered early in the second trimester, enabling the expectant parents to make informed fertility decisions about their unborn child (e.g., termination of pregnancy, intrauterine therapy, or intervention) [3].
Accurate estimation of gestational age (GA) is vital for identifying fetal abnormalities. Conventionally, GA is estimated by measuring the morphology of the cranium, abdomen, and femur manually and inputting them into the classic Hadlock formula to assess fetal growth. However, this procedure incurs considerable overhead and suffers from bias caused by the operators, yielding suboptimal estimations. To address this challenge, we develop an automatic DeepGA model to achieve fully automatic GA prediction in an end-to-end manner. Our model uses a deep segmentation model (DeepSeg) to accurately identify and segment three critical tissues, including the cranium, abdomen, and femur, in which their morphology is automatically extracted. After that, we are able to directly estimate the GA via a deep regression model (DeepReg). We evaluate DeepGA on a large dataset, including 10,413 ultrasound images from 7113 subjects. It achieves superior performance over the traditional measurement approach, with a mean absolute estimation error (MAE) of 5 days. Our DeepGA model is a novel automatic solution on the basis of artificial intelligence learning that can help radiologists improve the performance of GA estimation in various clinical scenarios, thereby enhancing the efficiency of prenatal examinations.
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This clinical practice guideline has been prepared by the Diagnostic Imaging Committee, reviewed by the Family Physician Advisory Committee, and approved by the Executive and Council of the Society of Obstetricians and Gynaecologists of Canada.

Disclosure statements have been received from all contributors.

: This document reflects emerging clinical and scientific advances on the date issued and is subject to change. The information should not be construed as dictating an exclusive course of treatment or procedure to be followed. Local institutions can dictate amendments to these opinions. They should be well documented if modified at the local level. None of these contents may be reproduced in any form without prior written permission of the SOGC.

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SOGC CLINICAL PRACTICE GUIDELINESRETIRED: Determination of Gestational Age by Ultrasound

SOGC CLINICAL PRACTICE GUIDELINES
RETIRED: Determination of Gestational Age by Ultrasound