Obstetrics & Gynecology

Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files.

In this study, we propose a deep learning-based approach to predict Intensity-modulated radiation therapy (IMRT) quality assurance (QA) gamma passing rates using delivery fluence informed by log files. A total of 112 IMRT plans for chest cancers were planned and measured by portal dosimetry equipped on TrueBeam linac. The convolutional neural network (CNN) based learning model was trained using ...

The Use of Machine Learning in MicroRNA Diagnostics: Current Perspectives.

MicroRNAs constitute small non-coding RNAs that play a pivotal role in regulating the translation and degradation of mRNA and have been associated with many diseases. Artificial Intelligence (AI) is an evolving cluster of interrelated fields, with machine learning (ML) standing out as one of the most prominent AI fields, with a plethora of applications in almost every aspect of human life. ML coul...

Robotic Surgery: Advancements and Inflection Points in the Field of Gynecology.

Robotics has become an essential part of the surgical armamentarium for a growing number of surgeons around the world. New companies seek to compete w...

Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.

Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. T...

Deep-Learning-Based Evaluation of Dual Stain Cytology for Cervical Cancer Screening: A New Paradigm.

A Partial Label-Based Machine Learning Approach For Cervical Whole-Slide Image Classification: The Winning TissueNet Solution.

Cervical cancer is the fourth most common cancer in women worldwide. To determine early treatment for patients, it is critical to accurately classify ...

Solving the problem of imbalanced dataset with synthetic image generation for cell classification using deep learning.

The low number of annotated training images and class imbalance in the field of machine learning is a common problem that is faced in many application...

Unsupervised Deep Learning based Longitudinal Follicular Growth Tracking during IVF Cycle using 3D Transvaginal Ultrasound in Assisted Reproduction.

Longitudinal follicle tracking is needed in clinical practice for diagnosis and management in assisted reproduction. Follicles are tracked over the in...

Towards Interpretable Attention Networks for Cervical Cancer Analysis.

Recent advances in deep learning have enabled the development of automated frameworks for analysing medical images and signals, including analysis of ...

Placental Super Micro-vessels Segmentation Based on ResNeXt with Convolutional Block Attention and U-Net.

Accurate placenta super micro-vessels segmentation is the key to diagnose placental diseases. However, the current automatic segmentation algorithm ha...

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers.

Ovarian cancer is one of the three most common gynecological cancers in the world, and is regarded as a priority in terms of women's cancer. In the pa...

Automatic contour segmentation of cervical cancer using artificial intelligence.

In cervical cancer treatment, radiation therapy is selected based on the degree of tumor progression, and radiation oncologists are required to deline...

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.

OBJECTIVE: Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this st...

Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data.

Most lung cancers are diagnosed at an advanced stage. Presymptomatic identification of high-risk individuals can prompt earlier intervention and impr...

DeepHPV: a deep learning model to predict human papillomavirus integration sites.

Human papillomavirus (HPV) integrating into human genome is the main cause of cervical carcinogenesis. HPV integration selection preference shows stro...

Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI.

The purpose of this study was to explore whether the Nomogram, which was constructed by combining the Deep learning and Radiomic features of T2-weight...

Robotic Applications for Benign Gynecologic Procedures.

INTRODUCTION: Robotic systems provide a platform for surgeons to expand their capabilities, allowing them to perform complex procedures safely and eff...

Gynecological Surgery and Machine Learning: Complications and Length of Stay Prediction.

In this study we are developing predictive models for a length of stay after a gynecological surgery, complications and the length of the surgery usin...

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets.

The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its ...

Using Predictive Modeling and Machine Learning to Identify Patients Appropriate for Outpatient Anterior Cervical Fusion and Discectomy.

STUDY DESIGN: Retrospective, case-control.