Pregnancy

Deep learning for real-time multi-class segmentation of artefacts in lung ultrasound.

Lung ultrasound (LUS) has emerged as a safe and cost-effective modality for assessing lung health, particularly during the COVID-19 pandemic. However, interpreting LUS images remains challenging due to its reliance on artefacts, leading to operator variability and limiting its practical uptake. To address this, we propose a deep learning pipeline for multi-class segmentation of objects (ribs, pleu...

Deep learning diagnostic performance and visual insights in differentiating benign and malignant thyroid nodules on ultrasound images.

This study aims to construct and evaluate a deep learning model, utilizing ultrasound images, to accurately differentiate benign and malignant thyroid nodules. The objective includes visualizing the model's process for interpretability and comparing its diagnostic precision with a cohort of 80 radiologists. We employed ResNet as the classification backbone for thyroid nodule prediction. The model ...

Dynamics of labor and capital in AI vs. non-AI industries: A two-industry model analysis.

There is an imbalance in the development of artificial intelligence between industries. Compared to non-AI enterprise, AI- enterprise will save labor,...

Machine learning to predict unintended pregnancy among reproductive-age women in Ethiopia: evidence from EDHS 2016.

BACKGROUND: An unintended pregnancy is a pregnancy that is either unwanted or mistimed, such as when it occurs earlier than desired. It is one of the ...

Ultrasound-based deep learning radiomics nomogram for risk stratification of testicular masses: a two-center study.

OBJECTIVE: To develop an ultrasound-driven clinical deep learning radiomics (CDLR) model for stratifying the risk of testicular masses, aiming to guid...

Deep learning-based osteochondritis dissecans detection in ultrasound images with humeral capitellum localization.

PURPOSE: Osteochondritis dissecans (OCD) of the humeral capitellum is a common cause of elbow disorders, particularly among young throwing athletes. C...

Prediction of preeclampsia from retinal fundus images via deep learning in singleton pregnancies: a prospective cohort study.

INTRODUCTION: Early prediction of preeclampsia (PE) is of universal importance in controlling the disease process. Our study aimed to assess the feasi...

Analysis of neural networks for routine classification of sixteen ultrasound upper abdominal cross sections.

PURPOSE: Abdominal ultrasound screening requires the capture of multiple standardized plane views as per clinical guidelines. Currently, the extent of...

Beyond the Echo: The Evolution and Revolution of Ultrasound in Anesthesia.

This article explores the evolving role of ultrasound technology in anesthesia. Ultrasound emerged decades ago, offering clinicians noninvasive, econo...

Artificial intelligence-based speckle featurization and localization for ultrasound speckle tracking velocimetry.

Deep learning-based super-resolution ultrasound (DL-SRU) framework has been successful in improving spatial resolution and measuring the velocity fiel...

TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity.

Despite being extremely relevant for the protection of prenatal and neonatal health, the developmental toxicity (Dev Tox) is a highly complex endpoint...

Advances in the Application of Artificial Intelligence in Fetal Echocardiography.

Congenital heart disease is a severe health risk for newborns. Early detection of abnormalities in fetal cardiac structure and function during pregnan...

Transcranial Phase Correction Using Pulse-Echo Ultrasound and Deep Learning: A 2-D Numerical Study.

Phase aberration caused by human skulls severely degrades the quality of transcranial ultrasound images, posing a major challenge in the practical app...

Deep learning to estimate gestational age from fly-to cineloop videos: A novel approach to ultrasound quality control.

OBJECTIVE: Low-cost devices have made obstetric sonography possible in settings where it was previously unfeasible, but ensuring quality and consisten...

Automated Measurement of Ovary Development in Atlantic Salmon Using Deep Learning.

OBJECTIVE: Salmon breeding companies control the egg stripping period through environmental change, which triggers the need to identify the state of m...

Multichannel high noise level ECG denoising based on adversarial deep learning.

This paper proposes a denoising method based on an adversarial deep learning approach for the post-processing of multi-channel fetal electrocardiogram...

Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms.

Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Preeclampsia is linked to mitochondrial dysfunction...

A biased coin up-and-down sequential allocation trial to determine the ED90 of intrathecal sufentanil combined with ropivacaine 2.5 mg for labor analgesia.

PURPOSE: To determine the 90 percent effective dose (ED90) of intrathecal sufentanil combined with ropivacaine 2.5 mg for labor analgesia and observe ...

Mastitis impact on high-yielding dairy farm's reproduction and net present value.

Poor udder health can have a negative impact on milk production and reproductive performance, which reduces the net present value (NPV) of dairy farms...

Is the diagnostic model based on convolutional neural network superior to pediatric radiologists in the ultrasonic diagnosis of biliary atresia?

BACKGROUND: Many screening and diagnostic methods are currently available for biliary atresia (BA), but the early and accurate diagnosis of BA remains...