Obstetrics & Gynecology

The Effectiveness and Safety of Ultrasonic Bone Scalpel Versus Conventional Method in Cervical Laminectomy: A Retrospective Study of 311 Patients.

STUDY DESIGN: Retrospective cohort study.

Automatic segmentation of the uterus on MRI using a convolutional neural network.

BACKGROUND: This study was performed to evaluate the clinical feasibility of a U-net for fully automatic uterine segmentation on MRI by using images of major uterine disorders.

Robotic Excision of Full-thickness Diaphragmatic Endometriosis.

STUDY OBJECTIVE: To describe a robotic approach to excision of full-thickness diaphragmatic endometriosis.

NOVEL MUTATIONS IN AN INFANT WITH MICROCEPHALIC PRIMORDIAL DWARFISM, DILATED CARDIOMYOPATHY, SUBCLINICAL HYPOTHYROIDISM, AND EARLY DEATH: EXPANDING THE PHENOTYPE OF MUTATIONS.

OBJECTIVE: Microcephalic primordial dwarfism (MPD) is a group of clinically and genetically heterogeneous disorders which result in severe prenatal an...

Predicting gamma passing rates for portal dosimetry-based IMRT QA using machine learning.

PURPOSE: Intensity-modulated radiation therapy (IMRT) quality assurance (QA) measurements are routinely performed prior to treatment delivery to verif...

A Human Microrobot Interface Based on Acoustic Manipulation.

Micro/nanorobotic systems capable of targeted transporting and releasing hold considerable promise for drug delivery, cellular surgery, biosensing, na...

Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy.

External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-...

How and why should the radiologist look at the placenta?

This editorial comment refers to the article "Identification of suspicious invasive placentation based on clinical MRI data using textural features an...

Social Media Surveillance of Multiple Sclerosis Medications Used During Pregnancy and Breastfeeding: Content Analysis.

BACKGROUND: Multiple sclerosis (MS) is a chronic neurological disease occurring mostly in women of childbearing age. Pregnant women with MS are usuall...

Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images.

INTRODUCTION: Ovarian tumors are the most common diagnostic challenge for gynecologists and ultrasound examination has become the main technique for a...

Deep-learning-assisted automatic digitization of applicators in 3D CT image-based high-dose-rate brachytherapy of gynecological cancer.

PURPOSE: Applicator digitization is one of the most critical steps in 3D high-dose-rate brachytherapy (HDRBT) treatment planning. Motivated by recent ...

Transoral robotic surgery for hilo-parenchymal submandibular stones: step-by-step description and reasoned approach.

The conservative transoral approach to hilo-parenchymal submandibular stones has been proposed as an alternative to traditional sialadenectomy. The ma...

Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier.

Cervical cancer is the fourth most communal malignant disease amongst women worldwide. In maximum circumstances, cervical cancer indications are not p...

Two-Dimensional Light Scattering Anisotropy Cytometry for Label-Free Classification of Ovarian Cancer Cells via Machine Learning.

We develop a single-mode fiber-based cytometer for the obtaining of two-dimensional (2D) light scattering patterns from static single cells. Anisotrop...

A new method for automatic counting of ovarian follicles on whole slide histological images based on convolutional neural network.

The ovary is a complex endocrine organ that shows significant structural and functional changes in the female reproductive system over recurrent cycle...

Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning.

Metastasis of solid tumors is a key determinant of cancer patient survival. Targeting micrometastases using nanoparticles could offer a way to stop me...

Improved Deep Learning Network Based in combination with Cost-sensitive Learning for Early Detection of Ovarian Cancer in Color Ultrasound Detecting System.

With the development of theories and technologies in medical imaging, most of the tumors can be detected in the early stage. However, the nature of ov...

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-nucleoti...

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 f...

Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models.

BACKGROUND: Comprehensive breast cancer risk prediction models enable identifying and targeting women at high-risk, while reducing interventions in th...