Automatic Placenta Abnormality Detection Using Convolutional Neural Networks on Ultrasound Texture

Hu, Zoe; Hu, Ricky; Yan, Ryan; Mayer, Chantal; Rohling, Robert; Singla, Rohit

doi:10.1007/978-3-030-87735-4_14

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…A fully automated DL model could perform this task rapidly and reliably, and eliminate interobserver variability [104][105][106] , potentially making placental biometry a useful imaging biomarker. Moreover, these algorithms are able to assess the location (anterior or posterior) 106 and appearance (normal or abnormal) 105 of the placenta. Segmentation DL models coupled with 3D ultrasound may provide additional information on the morphology and volume of the placenta 107,108 .…”

Section: Placentamentioning

confidence: 99%

“…Placental biometry, which has been found to correlate with the occurrence of fetal smallness, pre-eclampsia and other adverse pregnancy outcomes 102,103 , is not performed routinely, because it is time-consuming and operator-dependent. A fully automated DL model could perform this task rapidly and reliably, and eliminate interobserver variability [104][105][106] , potentially making placental biometry a useful imaging biomarker. Moreover, these algorithms are able to assess the location (anterior or posterior) 106 and appearance (normal or abnormal) 105 of the placenta.…”

Section: Placentamentioning

confidence: 99%

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Use of artificial intelligence and deep learning in fetal ultrasound imaging

Zegarra

Ghi

2023

Ultrasound in Obstet & Gyne

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Deep learning is considered the leading artificial intelligence tool in image analysis in general. Deep-learning algorithms excel at image recognition, which makes them valuable in medical imaging. Obstetric ultrasound has become the gold standard imaging modality for detection and diagnosis of fetal malformations. However, ultrasound relies heavily on the operator's experience, making it unreliable in inexperienced hands. Several studies have proposed the use of deep-learning models as a tool to support sonographers, in an attempt to overcome these problems inherent to ultrasound. Deep learning has many clinical applications in the field of fetal imaging, including identification of normal and abnormal fetal anatomy and measurement of fetal biometry. In this Review, we provide a comprehensive explanation of the fundamentals of deep learning in fetal imaging, with particular focus on its clinical applicability.

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Section: Placentamentioning

confidence: 99%

Section: Placentamentioning

confidence: 99%

Use of artificial intelligence and deep learning in fetal ultrasound imaging

Zegarra

Ghi

2023

Ultrasound in Obstet & Gyne

View full text Add to dashboard Cite

show abstract

“…However, sometimes placental-mediated diseases are not recognized until later stages. Hence, Hu et al [26] proposed a CNN pipeline to detect the presence of placental diseases. The model consists of segmentation of the placenta followed by classification utilizing a dataset containing US images of 321 patients (13,384 frames).…”

Section: Placentamentioning

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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Review on Ultrasound-Image Analysis of Fetus Using Deep Learning

Gautam

Kaur

2023

2023 IEEE 8th International Conference for Convergence in Technology (I2CT)

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Automatic Placenta Abnormality Detection Using Convolutional Neural Networks on Ultrasound Texture

Cited by 3 publications

References 17 publications

Use of artificial intelligence and deep learning in fetal ultrasound imaging

Use of artificial intelligence and deep learning in fetal ultrasound imaging

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

Review on Ultrasound-Image Analysis of Fetus Using Deep Learning

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