Obstetrics & Gynecology

Applying Automated Machine Learning to Predict Mode of Delivery Using Ongoing Intrapartum Data in Laboring Patients.

OBJECTIVE: This study aimed to develop and validate a machine learning (ML) model to predict the probability of a vaginal delivery (Partometer) using data iteratively obtained during labor from the electronic health record.

A Prediction Model of Human Resources Recruitment Demand Based on Convolutional Collaborative BP Neural Network.

This paper presents an in-depth study and analysis of the prediction model of force resource recruitment demand using a convolutional neural network combined with a BP neural network algorithm. BP neural network technology is introduced to be applied to enterprise management talent assessment activities. Using BP neural network has strong parallel processing characteristics, as well as unique adap...

Development of a robot-assisted thoracic surgery (RATS) program. Lessons learned after 2500 cases.

Robotic surgery provides significant advantages in terms of an optimal three-dimensional and magnified view of the surgical field, superior maneuverab...

Colposcopic multimodal fusion for the classification of cervical lesions.

: Cervical cancer is one of the two biggest killers of women and early detection of cervical precancerous lesions can effectively improve the survival...

Robotic operations in urgent general surgery: a systematic review.

Robotically assisted operations are the state of the art in laparoscopic general surgery. They are established predominantly for elective operations. ...

Bone visualization of the cervical spine with deep learning-based synthetic CT compared to conventional CT: A single-center noninferiority study on image quality.

PURPOSE: To investigate whether the image quality of a specific deep learning-based synthetic CT (sCT) of the cervical spine is noninferior to convent...

Pragmatic Prediction of Excessive Length of Stay After Cervical Spine Surgery With Machine Learning and Validation on a National Scale.

BACKGROUND: Extended postoperative hospital stays are associated with numerous clinical risks and increased economic cost. Accurate preoperative predi...

Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images.

Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70 % of advanced patients are with ...

The Impact of the Live Delivery of Goods on Consumers' Purchasing Behaviour in Complex Situations Based on Artificial Intelligence Technology.

With the development of information technology, the purchase of goods online has acquired a different dimension. This type of online purchase has made...

Spinning-enabled wireless amphibious origami millirobot.

Wireless millimeter-scale origami robots have recently been explored with great potential for biomedical applications. Existing millimeter-scale origa...

Diagnosis and Nursing Intervention of Gynecological Ovarian Endometriosis with Magnetic Resonance Imaging under Artificial Intelligence Algorithm.

This research was aimed to study the application value of the magnetic resonance imaging (MRI) diagnosis under artificial intelligence algorithms and ...

Oxygen delivery in pediatric cardiac surgery and its association with acute kidney injury using machine learning.

OBJECTIVE: Acute kidney injury (AKI) after pediatric cardiac surgery with cardiopulmonary bypass (CPB) is a frequently reported complication. In this ...

Deep learning features encode interpretable morphologies within histological images.

Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotype...

Radiomics and artificial intelligence in malignant uterine body cancers: Protocol for a systematic review.

INTRODUCTION: Uterine body cancers (UBC) are represented by endometrial carcinoma (EC) and uterine sarcoma (USa). The clinical management of both is h...

Real-time denoising of ultrasound images based on deep learning.

Ultrasound images are widespread in medical diagnosis for muscle-skeletal, cardiac, and obstetrical diseases, due to the efficiency and non-invasivene...

Deep Learning Algorithm-Based Ultrasound Image Information in Diagnosis and Treatment of Pernicious Placenta Previa.

This study was to explore the value of the deep dictionary learning algorithm in constructing a B ultrasound scoring system and exploring its applicat...

Deep Learning Algorithm-Based Magnetic Resonance Imaging Feature-Guided Serum Bile Acid Profile and Perinatal Outcomes in Intrahepatic Cholestasis of Pregnancy.

This study was aimed to explore magnetic resonance imaging (MRI) based on deep learning belief network model in evaluating serum bile acid profile and...

AL-Net: Attention Learning Network Based on Multi-Task Learning for Cervical Nucleus Segmentation.

Cervical nucleus segmentation is a crucial and challenging issue in automatic pathological diagnosis due to uneven staining, blurry boundaries, and ad...

Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model.

With the advent of big data, statistical accounting based on artificial intelligence can realistically reflect the dynamics of labor force and market ...

Prediction of preterm birth using artificial intelligence: a systematic review.

Preterm birth is the leading cause of neonatal death. It is challenging to predict preterm birth. We elucidated the state of artificial intelligence r...