Pregnancy

Application of artificial intelligence in screening for adverse perinatal outcomes: A protocol for systematic review.

The article presents a systematic review protocol. The aim of the study is an assessment of current studies regarding the application of artificial intelligence and neural networks in the screening for adverse perinatal outcomes. We intend to compare the reported efficacy of these methods to improve pregnancy care and outcomes. There are more and more studies that describe the role of machine lear...

Potential of Artificial Intelligence for Estimating Japanese Fetal Weights.

We developed an artificial intelligence (AI) method for estimating fetal weights of Japanese fetuses based on the gestational weeks and the bi-parietal diameter, abdominal circumference, and femur length. The AI comprised of neural network architecture was trained by deep learning with a dataset that consists of ± 2 standard devia-tion (SD), ± 1.5SD, and ± 0SD categories of the approved standard v...

Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal.

OBJECTIVES: To evaluate the feasibility of using deep-learning algorithms to classify as normal or abnormal sonographic images of the fetal brain obta...

Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology.

Artificial intelligence (AI) uses data and algorithms to aim to draw conclusions that are as good as, or even better than, those drawn by humans. AI i...

Nodule Localization in Thyroid Ultrasound Images with a Joint-Training Convolutional Neural Network.

The accurate localization of nodules in ultrasound images can convey crucial information to support a reliable diagnosis. However, this is usually cha...

Prediction of a Due Date Based on the Pregnancy History Data Using Machine Learning.

Prediction of a labor due date is important especially for the pregnancies with high risk of complications where a special treatment is needed. This i...

Deep Convolutional Neural Networks for Thyroid Tumor Grading using Ultrasound B-mode Images.

The performances of deep convolutional neural network (DCNN) modeling and transfer learning (TF) for thyroid tumor grading using ultrasound imaging we...

Using Supervised Learning Methods to Develop a List of Prescription Medications of Greatest Concern during Pregnancy.

INTRODUCTION: Women and healthcare providers lack adequate information on medication safety during pregnancy. While resources describing fetal risk ar...

Role of 5G-powered remote robotic ultrasound during the COVID-19 outbreak: insights from two cases.

The 2019 Novel Coronavirus disease (COVID-19) broke out in Wuhan, China in December 2019 and spread throughout the world. Early screening and early di...

Wavelet Spectral Deep-training of Convolutional Neural Networks for Accurate Identification of High-Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG of Preterm Sheep.

Early diagnosis and prognosis of babies with signs of hypoxic-ischemic encephalopathy (HIE) is currently limited and requires reliable prognostic biom...

Deep Convolutional Neural Network and Reverse Biorthogonal Wavelet Scalograms for Automatic Identification of High Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG.

Diagnosis of hypoxic-ischemic encephalopathy (HIE) is currently limited and prognostic biological markers are required for early identification of at ...

Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep.

Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effecti...

A Pilot Study on Scatterer Density Classification of Ultrasound Images Using Deep Neural Networks.

Quantitative ultrasound estimates different intrinsic tissue properties, which can be used for tissue characterization. Among different tissue propert...

Detecting muscle activation using ultrasound speed of sound inversion with deep learning.

Functional muscle imaging is essential for diagnostics of a multitude of musculoskeletal afflictions such as degenerative muscle diseases, muscle inju...

Deep Learning based Quantification of Ovary and Follicles using 3D Transvaginal Ultrasound in Assisted Reproduction.

Quantification of ovarian and follicular volume and follicle count are performed in clinical practice for diagnosis and management in assisted reprodu...

Automatic segmentation of ultrasound images of gastrocnemius medialis with different echogenicity levels using convolutional neural networks.

The purpose of this study was to develop an automatic method for the segmentation of muscle cross-sectional area on transverse B-mode ultrasound image...

Deep Learning Based Junctional Zone Quantification using 3D Transvaginal Ultrasound in Assisted Reproduction.

The Uterine Junctional Zone (JZ) is identified as an important anatomical region in the implantation process during assisted reproduction. The JZ chan...

Towards Sonomyography-Based Real-Time Control of Powered Prosthesis Grasp Synergies.

Sonomyography (ultrasound imaging) offers a way of classifying complex muscle activity and configuration, with higher SNR and lower hardware requireme...

Planning Framework for Robot-assisted Blood-Brain Barrier Opening with Focused Ultrasound.

This article presents a method to plan BloodBrain Barrier (BBB) disruption with Focused Ultrasound, under neuronavigated robotic assistance. Robotic a...

Automated Misspelling Detection and Correction in Persian Clinical Text.

Accurate electronic health records are important for clinical care, research, and patient safety assurance. Correction of misspelled words is required...