Pregnancy

Deep learning-based plane pose regression in obstetric ultrasound.

PURPOSE: In obstetric ultrasound (US) scanning, the learner's ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a major challenge in skill acquisition. We aim to build a US plane localisation system for 3D visualisation, training, and guidance without integrating additional sensors.

Computer Vision-Based Medical Cloud Data System for Back Muscle Image Detection.

The fast development of image recognition and information technology has influenced people's life and industry management mode not only in some common fields such as information management, but also has very much improved the working efficiency of various industries. In the healthcare field, the current highly disparate doctor-patient ratio leads to more and more doctors needing to undertake more ...

Using DeepLab v3 + -based semantic segmentation to evaluate platelet activation.

This research used DeepLab v3 + -based semantic segmentation to automatically evaluate the platelet activation process and count the number of platele...

Deep-Learning Based Adaptive Ultrasound Imaging From Sub-Nyquist Channel Data.

Traditional beamforming of medical ultrasound images relies on sampling rates significantly higher than the actual Nyquist rate of the received signal...

Deep Learning-Based Classification of Reduced Lung Ultrasound Data From COVID-19 Patients.

The application of lung ultrasound (LUS) imaging for the diagnosis of lung diseases has recently captured significant interest within the research com...

Neural Network Kalman Filtering for 3-D Object Tracking From Linear Array Ultrasound Data.

Many interventional surgical procedures rely on medical imaging to visualize and track instruments. Such imaging methods not only need to be real time...

Investigating Shift Variance of Convolutional Neural Networks in Ultrasound Image Segmentation.

While accuracy is an evident criterion for ultrasound image segmentation, output consistency across different tests is equally crucial for tracking ch...

Does proximal versus distal injury location of the medial ulnar collateral ligament of the elbow differentially impact elbow stability? An ultrasound-guided and robot-assisted biomechanical study.

BACKGROUND: The location (proximal vs. distal) of elbow medial ulnar collateral ligament (MUCL) tears impacts clinical outcomes of nonoperative treatm...

A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography.

Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibili...

Clinical target segmentation using a novel deep neural network: double attention Res-U-Net.

We introduced Double Attention Res-U-Net architecture to address medical image segmentation problem in different medical imaging system. Accurate medi...

Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation: Facilitating use of this technology in low resource settings of South Asia and East Africa.

BACKGROUND: Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches em...

Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda.

Machine Learning (ML) has been widely used in predicting the mode of childbirth and assessing the potential maternal risks during pregnancy. The prima...

Deep Learning-Based Ultrasound Combined with Gastroscope for the Diagnosis and Nursing of Upper Gastrointestinal Submucous Lesions.

The study focused on the diagnostic value of deep learning-based ultrasound combined with gastroscope examination for upper gastrointestinal submucous...

Robotic CME in obese patients: advantage of robotic ultrasound scan for vascular dissection.

Complete mesocolic excision (CME) in right-sided colon cancers appears to confer oncological benefits compared to conventional colectomy. Identificati...

Automatic measurement of fetal head circumference using a novel GCN-assisted deep convolutional network.

The growth of the fetus can be effectively monitored by measuring the fetal head circumference (HC) in ultrasound images. Moreover, it is the key to a...

Machine Learning-Aided Chronic Kidney Disease Diagnosis Based on Ultrasound Imaging Integrated with Computer-Extracted Measurable Features.

Although ultrasound plays an important role in the diagnosis of chronic kidney disease (CKD), image interpretation requires extensive training. High o...

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis.

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype o...

Development and validation of a combined nomogram model based on deep learning contrast-enhanced ultrasound and clinical factors to predict preoperative aggressiveness in pancreatic neuroendocrine neoplasms.

OBJECTIVES: This study aimed to develop and validate a combined nomogram model based on deep learning (DL) contrast-enhanced ultrasound (CEUS) and cli...

SRAS-net: Low-resolution chromosome image classification based on deep learning.

Prenatal karyotype diagnosis is important to determine if the foetus has genetic diseases and some congenital diseases. Chromosome classification is a...

Can uncertainty estimation predict segmentation performance in ultrasound bone imaging?

PURPOSE: Segmenting bone surfaces in ultrasound (US) is a fundamental step in US-based computer-assisted orthopaedic surgeries. Neural network-based s...