Pregnancy

Computational and artificial neural network based study of functional SNPs of human LEPR protein associated with reproductive function.

Genetic polymorphisms are mostly associated with inherited diseases, detecting and analyzing the biological significance of functional single-nucleotide polymorphisms (SNPs) using wet laboratory experiments is an arduous task hence the computational analysis of putative SNPs is essential before conducting a study on a large population. SNP in the leptin receptor (LEPR) could result in the retentio...

DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in .

Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted...

Automatic brain tissue segmentation in fetal MRI using convolutional neural networks.

MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic an...

A Mechatronic Platform for Computer Aided Detection of Nodules in Anatomopathological Analyses via Stiffness and Ultrasound Measurements.

This study presents a platform for ex-vivo detection of cancer nodules, addressing automation of medical diagnoses in surgery and associated histologi...

Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net.

PURPOSE: Transrectal ultrasound (TRUS) is a versatile and real-time imaging modality that is commonly used in image-guided prostate cancer interventio...

Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa.

PURPOSE: To evaluate whether a machine learning (ML) analysis employing MRI-derived texture analysis (TA) features could be useful in assessing the pr...

The significance of artificial intelligence in drug delivery system design.

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and inte...

Prediction of coronary thin-cap fibroatheroma by intravascular ultrasound-based machine learning.

BACKGROUND AND AIMS: Although grayscale intravascular ultrasound (IVUS) is commonly used for assessing coronary lesion morphology and optimizing stent...

Use of artificial intelligence (AI) in the interpretation of intrapartum fetal heart rate (FHR) tracings: a systematic review and meta-analysis.

OBJECTIVES: To determine the degree of inter-rater reliability (IRR) between human and artificial intelligence (AI) interpretation of fetal heart rate...

Artificial Neural Network Analysis of Spontaneous Preterm Labor and Birth and Its Major Determinants.

BACKGROUND: Little research based on the artificial neural network (ANN) is done on preterm birth (spontaneous preterm labor and birth) and its major ...

Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound.

Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatme...

Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging.

Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can...

Image Quality Improvement of Hand-Held Ultrasound Devices With a Two-Stage Generative Adversarial Network.

As a widely used imaging modality in the medical field, ultrasound has been applied in community medicine, rural medicine, and even telemedicine in re...

Deep Learning for Real-time, Automatic, and Scanner-adapted Prostate (Zone) Segmentation of Transrectal Ultrasound, for Example, Magnetic Resonance Imaging-transrectal Ultrasound Fusion Prostate Biopsy.

BACKGROUND: Although recent advances in multiparametric magnetic resonance imaging (MRI) led to an increase in MRI-transrectal ultrasound (TRUS) fusio...

Attention-aware fully convolutional neural network with convolutional long short-term memory network for ultrasound-based motion tracking.

PURPOSE: One of the promising options for motion management in radiation therapy (RT) is the use of LINAC-compatible robotic-arm-mounted ultrasound im...

Machine learning methods for automated technical skills assessment with instructional feedback in ultrasound-guided interventions.

OBJECTIVE: Currently, there is a worldwide shift toward competency-based medical education. This necessitates the use of automated skills assessment m...

Exon level machine learning analyses elucidate novel candidate miRNA targets in an avian model of fetal alcohol spectrum disorder.

Gestational alcohol exposure causes fetal alcohol spectrum disorder (FASD) and is a prominent cause of neurodevelopmental disability. Whole transcript...

Automatic segmentation of levator hiatus from ultrasound images using U-net with dense connections.

In this paper, we propose a fully automatic method based on a densely connected convolutional network for the segmentation of the levator hiatus from ...

Automatic Tracking of Muscle Cross-Sectional Area Using Convolutional Neural Networks with Ultrasound.

OBJECTIVES: The purpose of this study was to develop an automatic tracking method for the muscle cross-sectional area (CSA) on ultrasound (US) images ...

Neural networks for automatic scoring of arthritis disease activity on ultrasound images.

BACKGROUND: The development of standardised methods for ultrasound (US) scanning and evaluation of synovitis activity by the OMERACT-EULAR Synovitis S...