Obstetrics & Gynecology

The feasibility study on the generalization of deep learning dose prediction model for volumetric modulated arc therapy of cervical cancer.

PURPOSE: To develop a 3D-Unet dose prediction model to predict the three-dimensional dose distribution of volumetric modulated arc therapy (VMAT) for cervical cancer and test the dose prediction performance of the model in endometrial cancer to explore the feasibility of model generalization.

Design and Evaluation of a Postpartum Depression Ontology.

OBJECTIVE: Postpartum depression (PPD) remains an understudied research area despite its high prevalence. The goal of this study is to develop an ontology to aid in the identification of patients with PPD and to enable future analyses with electronic health record (EHR) data.

Ultrasound Image under Artificial Intelligence Algorithm to Evaluate the Intervention Effect of Accelerated Rehabilitation Surgery Nursing on Laparoscopic Hysterectomy.

To explore the application value of accelerated rehabilitation surgery (ERAS) nursing in laparoscopic total hysterectomy, 120 patients who underwent l...

Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning.

BACKGROUND: Ovarian cancer (OC) is a malignancy exhibiting high mortality in female tumors. Glycosylation is a posttranslational modification of prote...

Comparison of robot-assisted and conventional laparoscopy for colorectal surgery for endometriosis: A prospective cohort study.

BACKGROUND: Our objective was to evaluate surgical outcomes of robotic compared to conventional laparoscopy for colorectal surgery for endometriosis.

Effect of Injection Speed of Heavy Bupivacaine in Spinal Anesthesia on Quality of Block and Hemodynamic Changes.

BACKGROUND AND AIMS: Spinal anesthesia is a technique widely used for gynecological, lower abdominal, pelvic and lower limb procedures. Even though it...

Robot-assisted techniques in vascular and endovascular surgery.

For thousands of years, robots have inspired the imagination of humans, but it was only about 35 years ago that a robot was used for the first time in...

A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning.

Vaginitis is a gynecological disease affecting the health of millions of women all over the world. The traditional diagnosis of vaginitis is based on ...

A Semi-supervised Deep Learning Method for Cervical Cell Classification.

Currently, the Thinprep cytologic test (TCT) is the most popular cervical cancer cytology test technique. It can detect precancerous conditions and mi...

Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy.

PURPOSE: This study aims to develop a deep learning method that skips the time-consuming inverse optimization process for automatic generation of mach...

Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers.

Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer...

Segmentation of metastatic cervical lymph nodes from CT images of oral cancers using deep-learning technology.

OBJECTIVE: The purpose of this study was to establish a deep-learning model for segmenting the cervical lymph nodes of oral cancer patients and diagno...

Machine learning methods to predict presence of residual cancer following hysterectomy.

Surgical management for gynecologic malignancies often involves hysterectomy, often constituting the most common gynecologic surgery worldwide. Despit...

Correlation Between Serum AMH Levels and Cardiometabolic Indices in PCOS Women.

INTRODUCTION: Polycystic ovary syndrome (PCOS) has a predilection for several cardio-metabolic disorders in future. Levels of anti-Mullerian hormone (...

A technical guide to robotic catheter angiography with the Corindus CorPath GRX system.

Robotic catheter angiography is an emerging technology with the opportunity to increase procedural precision and decrease occupational hazards.1-5 In ...

Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients.

BACKGROUND: Despite the growing number of patients with both coronary artery disease and gynecological cancer, there are no nationally representative ...

Automatic segmentation of magnetic resonance images for high-dose-rate cervical cancer brachytherapy using deep learning.

PURPOSE: Magnetic resonance (MR) imaging is the gold standard in image-guided brachytherapy (IGBT) due to its superior soft-tissue contrast for target...

A bibliometric review of peripartum cardiomyopathy compared to other cardiomyopathies using artificial intelligence and machine learning.

As developments in artificial intelligence and machine learning become more widespread in healthcare, their potential to transform clinical outcomes a...

A deep learning algorithm to identify cervical ossification of posterior longitudinal ligaments on radiography.

The cervical ossification of the posterior longitudinal ligament (cOPLL) is sometimes misdiagnosed or overlooked on radiography. Thus, this study aime...

Framework for Integrating Equity Into Machine Learning Models: A Case Study.

Predictive analytic models leveraging machine learning methods increasingly have become vital to health care organizations hoping to improve clinical ...