Automatic fetal biometry prediction using a novel deep convolutional network architecture

Oghli, Mostafa Ghelich; Shabanzadeh, Ali; Moradi, Shakiba; Sirjani, Nasim; Gerami, Reza; Ghaderi, Payam; Taheri, Morteza Sanei; Shiri, Isaac; Arabi, Hossein; Zaidi, Habib

doi:10.1016/j.ejmp.2021.06.020

Cited by 35 publications

(20 citation statements)

References 31 publications

(47 reference statements)

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“…Ten studies assessed automated measurement of the fetal abdominal circumference (AC), femur length (FL), head circumference and biparietal diameter. 8,[32][33][34][35][36][37][38][39][40] Arroyo et al focused on deep learning to assess fetal presentation and placental location, in addition to assessment of fetal biometry for estimation of gestational age in the third trimester. 33 Twenty studies focused on the automation of fetal head measurements only with automated detection of the correct scanning plane and automated measurements of various head and intracranial structures.…”

Section: Resultsmentioning

confidence: 99%

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Artificial intelligence in obstetric ultrasound: A scoping review

2023

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The objective is to summarize the current use of artificial intelligence (AI) in obstetric ultrasound. PubMed, Cochrane Library, and ClinicalTrials.gov databases were searched using the following keywords “neural networks”, OR “artificial intelligence”, OR “machine learning”, OR “deep learning”, AND “obstetrics”, OR “obstetrical”, OR “fetus”, OR “foetus”, OR “fetal”, OR “foetal”, OR “pregnancy”, or “pregnant”, AND “ultrasound” from inception through May 2022. The search was limited to the English language. Studies were eligible for inclusion if they described the use of AI in obstetric ultrasound. Obstetric ultrasound was defined as the process of obtaining ultrasound images of a fetus, amniotic fluid, or placenta. AI was defined as the use of neural networks, machine learning, or deep learning methods. The authors’ search identified a total of 127 papers that fulfilled our inclusion criteria. The current uses of AI in obstetric ultrasound include first trimester pregnancy ultrasound, assessment of placenta, fetal biometry, fetal echocardiography, fetal neurosonography, assessment of fetal anatomy, and other uses including assessment of fetal lung maturity and screening for risk of adverse pregnancy outcomes. AI holds the potential to improve the ultrasound efficiency, pregnancy outcomes in low resource settings, detection of congenital malformations and prediction of adverse pregnancy outcomes.

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Section: Resultsmentioning

confidence: 99%

“…Forty‐seven studies focused on the use of AI for the assessment of fetal biometry (Table 3). Ten studies assessed automated measurement of the fetal abdominal circumference (AC), femur length (FL), head circumference and biparietal diameter 8,32–40 . Arroyo et al.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence in obstetric ultrasound: A scoping review

2023

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“…The number of pregnant women who have participated in the study is 7875, including High Birth Weight (HBW) and Low Birth Weight (LBW) fetuses. The results show Mean Absolute Percent Error (MAPE) ranges of 0.0609 ± 0.0506 and a Mean Absolute Error (MAE) of 98.55 ± 158.63 g. Correspondingly, Oghli et al [47] present CNN architecture based on Multi-Feature Pyramid U-net (MFP-Unet) to measure fetal biometry, including Biparietal Diameter (BPD), HC, AC, and Femur Length (FL) using 1334 US images. The model achieved 0.98 on DSC, 1.14 mm on Hausdorff Distance (HD), 0.95 on conformity, and 0.2 mm on Average Perpendicular Distance (APD).…”

Section: Fetal Healthmentioning

confidence: 99%

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

Khan

Aslam

Anis

et al. 2022

Sensors

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A fetal ultrasound (US) is a technique to examine a baby’s maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health. Disorders resulting from abnormal AFV levels, commonly referred to as oligohydramnios or polyhydramnios, may pose a serious threat to a mother’s or child’s health. This paper attempts to accumulate and compare the most recent advancements in Artificial Intelligence (AI)-based techniques for the diagnosis and classification of AFV levels. Additionally, we provide a thorough and highly inclusive breakdown of other relevant factors that may cause abnormal AFV levels, including, but not limited to, abnormalities in the placenta, kidneys, or central nervous system, as well as other contributors, such as preterm birth or twin-to-twin transfusion syndrome. Furthermore, we bring forth a concise overview of all the Machine Learning (ML) and Deep Learning (DL) techniques, along with the datasets supplied by various researchers. This study also provides a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to further explore.

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“…In recent years, deep learning methods, especially convolutional neural networks (CNN), have been successfully applied to fetal biometrics. The current research mainly focuses on automatic measurement of fetal head circumference (HC), biparietal diameter (BPD), femoral length (FL), and AC, 7 and it still lacks effective automatic solutions for FASSTT measurement. The reason is two‐fold.…”

Section: Introductionmentioning

confidence: 99%

Automatic measurement of fetal abdomen subcutaneous soft tissue thickness from ultrasound image based on a U‐shaped attention network with morphological method

Yuan,

Xu,

et al. 2024

Int J Imaging Syst Tech

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Fetal abdominal subcutaneous soft tissue thickness (FASSTT) is a key indicator in evaluating fetal growth, development, and nutritional status. Currently, manual measurement in FASSTT faces the problems of difficulty in positioning, time consumption, and inaccurate measurement. Therefore, this article proposes an automatic measurement scheme for FASSTT. Firstly, a U‐shaped attention network VGG‐SeUnet is proposed to automatically segment the subcutaneous soft tissue area of the fetal abdomen. Secondly, based on the segmentation results, a morphological method is proposed to obtain FASSTT. Specifically, the segmentation network uses VGG16 as the encoder and connects the decoder through jump connections for multi‐scale fusion. We introduce channel attention to jump connections, which enables the model to select key channels for feature fusion. At the same time, the model uses the proposed DF_Loss function for training to solve the problem of sample imbalance. Based on the segmentation results, a morphological distance transformation algorithm is proposed to obtain FASSTT by drawing the maximum inscribed circle and calculating its diameter. The method is evaluated on a fetal abdominal circumference ultrasound dataset containing 135 samples. The DSC, mIOU, mPA, Precision and Recall of the segmentation experiments reache 88.9%, 81.02%, 90.44%, 86.38%, and 90.44% respectively. The difference between FASSTT's result from that provided by radiologist is 0.0615 cm in Average Error (AVE) and 0.081 cm in Root Mean Square Error (RMSE). The overall performance of this algorithm is superior to existing methods with excellent performance, and the measurement error is less than 1 millimeter. This result proves that the proposed scheme in this paper can achieve automated measurement of FASSTT and assist doctors in subsequent evaluation of fetal development and nutritional status.

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Automatic fetal biometry prediction using a novel deep convolutional network architecture

Cited by 35 publications

References 31 publications

Artificial intelligence in obstetric ultrasound: A scoping review

Artificial intelligence in obstetric ultrasound: A scoping review

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

Automatic measurement of fetal abdomen subcutaneous soft tissue thickness from ultrasound image based on a U‐shaped attention network with morphological method

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