Obstetrics & Gynecology

DeepDose: a robust deep learning-based dose engine for abdominal tumours in a 1.5 T MRI radiotherapy system.

We present a robust deep learning-based framework for dose calculations of abdominal tumours in a 1.5 T MRI radiotherapy system. For a set of patient plans, a convolutional neural network is trained on the dose of individual multi-leaf-collimator segments following the DeepDose framework. It can then be used to predict the dose distribution per segment for a set of patient anatomies. The network w...

CT based automatic clinical target volume delineation using a dense-fully connected convolution network for cervical Cancer radiation therapy.

BACKGROUND: It is very important to accurately delineate the CTV on the patient's three-dimensional CT image in the radiotherapy process. Limited to the scarcity of clinical samples and the difficulty of automatic delineation, the research of automatic delineation of cervical cancer CTV based on CT images for new patients is slow. This study aimed to assess the value of Dense-Fully Connected Convo...

GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.

The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta caus...

Is Anti-Müllerian Hormone a Marker of Ovarian Reserve in Young Breast Cancer Patients Receiving a GnRH Analog during Chemotherapy?

INTRODUCTION: Anti-Müllerian hormone (AMH) is the most reliable biomarker of ovarian reserve; however, its role in predicting ovarian recovery after c...

Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting.

IMPORTANCE: Cervical cancer is highly preventable but remains a common and deadly cancer in areas without screening programs. The creation of a diagno...

Peri-operative blood management of Jehovah's Witnesses undergoing cytoreductive surgery for advanced ovarian cancer.

BACKGROUND: The aim of this study was to evaluate the efficacy and feasibility of a peri-operative bloodless medicine and surgery (BMS) protocol in re...

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses.

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic...

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains ...

Integrated Learning: Screening Optimal Biomarkers for Identifying Preeclampsia in Placental mRNA Samples.

Preeclampsia (PE) is a maternal disease that causes maternal and child death. Treatment and preventive measures are not sound enough. The problem of P...

The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design.

Remarkable advancements in the computational techniques and nanoelectronics have attracted considerable interests for development of highly-sophistica...

An interdisciplinary team-training protocol for robotic gynecologic surgery improves operating time and costs: analysis of a 4-year experience in a university hospital setting.

Main aim of this study is to assess the effect of a structured, interdisciplinary, surgical, team-training protocol in robotic gynecologic surgery, wi...

Intrahepatic cholestasis of pregnancy: machine-learning algorithm to predict elevated bile acid based on clinical and laboratory data.

PURPOSE: Applying machine-learning models to clinical and laboratory features of women with intrahepatic cholestasis of pregnancy (ICP) and creating a...

Design and Comparison of Magnetically-Actuated Dexterous Forceps Instruments for Neuroendoscopy.

Robot-assisted minimally invasive surgical (MIS) techniques offer improved instrument precision and dexterity, reduced patient trauma and risk, and pr...

TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.

Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-gr...

Full robotic multivisceral resections: the Modena experience and literature review.

The robotic platform is becoming a multidisciplinary tool, versatile, and suitable for multiple procedures. Combined multivisceral resections may repr...

Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury.

Traumatic spinal cord injury (SCI) produces a complex syndrome that is expressed across multiple endpoints ranging from molecular and cellular changes...

Biohybrid robotics: From the nanoscale to the macroscale.

Biohybrid robotics is a field in which biological entities are combined with artificial materials in order to obtain improved performance or features ...

CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation.

Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved ...

Quantitative analysis of abnormalities in gynecologic cytopathology with deep learning.

Cervical cancer is one of the most frequent cancers in women worldwide, yet the early detection and treatment of lesions via regular cervical screenin...

Risk of Upper-body Adverse Events in Robot-assisted Total Laparoscopic Hysterectomy for Benign Gynecologic Disease.

STUDY OBJECTIVE: Recent studies suggest that prolonged Trendelenburg positioning during robot-assisted total laparoscopic hysterectomy (RA-TLH) may le...