Obstetrics & Gynecology

Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions.

Muscle fat infiltration (MFI) has been widely reported across cervical spine disorders. The quantification of MFI requires time-consuming and rater-dependent manual segmentation techniques. A convolutional neural network (CNN) model was trained to segment seven cervical spine muscle groups (left and right muscles segmented separately, 14 muscles total) from Dixon MRI scans (n = 17, 17 scans < 2 we...

Vesicouterine Fistula: A Robotic Approach.

INTRODUCTION AND HYPOTHESIS: This video demonstrates surgical repair of a vesicouterine fistula via a robotic, uterine-sparing approach.

Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning.

Every year cervical cancer affects more than 300,000 people, and on average one woman is diagnosed with cervical cancer every minute. Early diagnosis ...

Robotic Radical Trachelectomy with Primary Vaginal Closure to Spare Fertility in Young Patients with Early-Stage Cervical Cancer.

OBJECTIVE: Our aim was to present the surgical technique of robotic radical trachelectomy (RRT) for early-stage squamous cell cervical cancer in women...

Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images.

Cervical cancer is the second most common cancer in women worldwide with a mortality rate of 60%. Cervical cancer begins with no overt signs and has a...

Early pregnancy diagnosis of rabbits: A non-invasive approach using Vis-NIR spatially resolved spectroscopy.

Pregnancy diagnosis is essential for rabbit's reproductive management. The early identification of non-pregnant rabbits allows for earlier re-insemina...

Semi-Supervised Topological Analysis for Elucidating Hidden Structures in High-Dimensional Transcriptome Datasets.

Topological data analysis (TDA) is a powerful method for reducing data dimensionality, mining underlying data relationships, and intuitively represent...

A stacking ensemble deep learning approach to cancer type classification based on TCGA data.

Cancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid...

Robot-assisted Exploration of Somatic Nerves in the Pelvis and Transection of the Sacrospinous Ligament for Alcock Canal Syndrome.

STUDY OBJECTIVE: Some articles have reported the surgical management of Alcock canal syndrome (ACS) using the transperineal [1], transgluteal [2], or ...

Development and assessment of a telesonography system for musculoskeletal imaging.

BACKGROUND: Telesonography systems have been developed to overcome barriers to accessing diagnostic ultrasound for patients in rural and remote commun...

A decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA-125.

BACKGROUND: To evaluate the performance of a decision support system (DSS) based on radiomics and machine learning in predicting the risk of malignanc...

Detection of segmented uterine cancer images by Hotspot Detection method using deep learning models, Pigeon-Inspired Optimization, types-based dominant activation selection approaches.

Uterine cancer consists of cells of a layer that forms the inside of the uterus. Sometimes, as a result of abnormal growth of normal cells, it can dam...

Menopausal Women's Health Care Method Based on Computer Nursing Diagnosis Intelligent System.

Taking into account the current feature extraction speed and recognition effect of intelligent diagnosis of menopausal women's health care behavior, t...

Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning.

Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the...

DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques.

Cervical cancer, one of the most common fatal cancers among women, can be prevented by regular screening to detect any precancerous lesions at early s...

A fuzzy rank-based ensemble of CNN models for classification of cervical cytology.

Cervical cancer affects more than 0.5 million women annually causing more than 0.3 million deaths. Detection of cancer in its early stages is of prime...

Development of MRI-Based Radiomics Model to Predict the Risk of Recurrence in Patients With Advanced High-Grade Serous Ovarian Carcinoma.

The purpose of our study was to develop a radiomics model based on preoperative MRI and clinical information for predicting recurrence-free survival ...

Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network.

Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and preoperative risk stratification is essential for personali...

Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning.

Over the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abun...

Conversation-Based Information Delivery Method for Facility Management.

Facility management platforms are widely used in the facility maintenance phase of the building life cycle. However, a large amount of complex buildin...