Obstetrics & Gynecology

Harnessing Population Pedigree Data and Machine Learning Methods to Identify Patterns of Familial Bladder Cancer Risk.

BACKGROUND: Relatives of patients with bladder cancer have been shown to be at increased risk for kidney, lung, thyroid, and cervical cancer after correcting for smoking-related behaviors that may concentrate in some families. We demonstrate a novel approach to simultaneously assess risks for multiple cancers to identify distinct multicancer configurations (multiple different cancer types that clu...

A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network.

The diagnosis of cervical dysplasia, carcinoma in situ and confirmed carcinoma cases is more easily perceived by commercially available and current research-based decision support systems when the scenario of pathologists to patient ratio is small. The treatment modalities for such diagnosis rely exclusively on precise identification of dysplasia stages as followed by The Bethesda System. The clas...

Anti-Inflammatory Activity of Sanjie Zhentong Capsule Assessed By Network Pharmacology Analysis of Adenomyosis Treatment.

BACKGROUND: Sanjie Zhentong capsule (SZC) offers excellent effect in treating adenomyosis (AM), which is a common and difficult gynecological disease ...

Two dimensional multifractal detrended fluctuation analysis of low coherence images for diagnosis of cervical pre-cancer.

We report detection of cervical pre-cancer through their low coherence images by applying two dimensional multifractal detrended fluctuation analysis....

Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram.

PURPOSE: To develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-s...

Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training.

PURPOSE: This study aimed to validate a deep learning model's diagnostic performance in using computed tomography (CT) to diagnose cervical lymph node...

Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage.

Preterm birth, defined as a delivery before 37 weeks' gestation, continues to affect 8-15% of all pregnancies and is associated with significant neona...

Patient selection for proton therapy: a radiobiological fuzzy Markov model incorporating robust plan analysis.

While proton therapy can offer increased sparing of healthy tissue compared with X-ray therapy, it can be difficult to predict whether a benefit can b...

Many-isocenter optimization for robotic radiotherapy.

Despite significant dosimetric gains, clinical implementation of the 4π non-coplanar radiotherapy on the widely available C-arm gantry system is hinde...

An artificial neural network to model response of a radiotherapy beam monitoring system.

PURPOSE: The integral quality monitor (IQM) is a real-time radiotherapy beam monitoring system, which consists of a spatially sensitive large-area ion...

Prediction of vaginal birth after cesarean deliveries using machine learning.

BACKGROUND: Efforts to reduce cesarean delivery rates to 12-15% have been undertaken worldwide. Special focus has been directed towards parturients wh...

Prediction of the individual multileaf collimator positional deviations during dynamic IMRT delivery priori with artificial neural network.

PURPOSES: Multileaf collimator (MLC) positional accuracy during dynamic intensity modulation radiotherapy (IMRT) delivery is crucial for safe and accu...

Nodular Thyroid Disease in the Era of Precision Medicine.

Management of thyroid nodules in the era of precision medicine is continuously changing. Neck ultrasound plays a pivotal role in the diagnosis and sev...

Ovarian torsion: developing a machine-learned algorithm for diagnosis.

BACKGROUND: Ovarian torsion is a common concern in girls presenting to emergency care with pelvic or abdominal pain. The diagnosis is challenging to m...

Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer.

In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) i...

Xuetonglactones A-F: Highly Oxidized Lanostane and Cycloartane Triterpenoids From Roxb. Craib.

Xuetonglactones A-F (-), six unreported highly oxidized lanostane- and cycloartane-type triterpenoids along with 22 known scaffolds (-) were isolated ...

Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network.

Machine learning and deep learning methods aims to discover patterns out of datasets such as, microarray data and medical data. In recent years, the i...

Deep Learning of Markov Model-Based Machines for Determination of Better Treatment Option Decisions for Infertile Women.

In this technical article, we are proposing ideas, that we have been developing on how machine learning and deep learning techniques can potentially a...

Technique for Robotic Transhiatal Esophagectomy.

Minimally invasive esophagectomy is increasing performed for cancers of the esophagus and gastroesophageal junction. This video demonstrates the setup...

Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the po...