Pregnancy

Machine learning analysis of contrast-enhanced ultrasound (CEUS) for the diagnosis of acute graft dysfunction in kidney transplant recipients.

AIM: The aim of the study was to develop machine learning algorithms (MLA) for diagnosing acute graft dysfunction (AGD) in kidney transplant recipients based on contrast-enhanced ultrasound (CEUS) analysis of the graft.Materials and methods: This prospective study involved 71 patients with kidney transplant undergoing CEUS during follow-up. AGD wasdefined as an increase in serum creatinine levels ...

Deep Learning for Distinguishing Mucinous Breast Carcinoma From Fibroadenoma on Ultrasound.

PURPOSE: Mucinous breast carcinoma (MBC) tends to be misdiagnosed as fibroadenomas (FA) due to its benign imaging characteristics. We aimed to develop a deep learning (DL) model to differentiate MBC and FA based on ultrasound (US) images. The model could contribute to the diagnosis of MBC for radiologists.

Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restriction.

BACKGROUND: Fetal growth restriction (FGR) occurs in 10% of pregnancies worldwide. Placenta dysfunction, as one of the most common causes of FGR, is a...

Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.

Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and p...

The early prediction of gestational diabetes mellitus by machine learning models.

BACKGROUND: We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with so...

Can deep learning classify cerebral ultrasound images for the detection of brain injury in very preterm infants?

OBJECTIVES: Cerebral ultrasound (CUS) is the main imaging screening tool in preterm infants. The aim of this work is to develop deep learning (DL) mod...

Valorization of tomato processing by-products: Predictive modeling and optimization for ultrasound-assisted lycopene extraction.

Lycopene is a carotenoid highly valuable to the food, pharmaceutical, dye, and cosmetic industries, present in ripe tomatoes and other fruits with a d...

Identifying Elective Induction of Labor among a Diverse Pregnant Population from Electronic Health Records within a Large Integrated Health Care System.

OBJECTIVE:  Distinguishing between medically indicated induction of labor (iIOL) and elective induction of labor (eIOL) is a daunting process for rese...

Model based deep learning method for focused ultrasound pathway scanning.

The primary purpose of high-intensity focused ultrasound (HIFU), a non-invasive medical therapy, is to precisely target and ablate tumors by focusing ...

Can time-lapse culture combined with artificial intelligence improve ongoing pregnancy rates in fresh transfer cycles of single cleavage stage embryos?

PURPOSE: With the rapid advancement of time-lapse culture and artificial intelligence (AI) technologies for embryo screening, pregnancy rates in assis...

Machine learning approach to investigate pregnancy and childbirth risk factors of sleep problems in early adolescence: Evidence from two cohort studies.

BACKGROUND: This study aimed to predict early adolescent sleep problems using pregnancy and childbirth risk factors through machine learning algorithm...

A Novel Deep Learning Model for Breast Tumor Ultrasound Image Classification with Lesion Region Perception.

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception and classification to assist in breast cancer diagno...

Task sharing for increasing access to obstetric ultrasonography: a formative qualitative study of nurse-led scanning with telemedicine review in Kenya.

The informal settlements of Nairobi have higher neonatal and infant mortality rates than the average for Nairobi. Universal access to important diagno...

Artificial intelligence in nanotechnology for treatment of diseases.

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and...

Deep learning model for intravascular ultrasound image segmentation with temporal consistency.

This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Usi...

Development of fucoidan/polyethyleneimine based sorafenib-loaded self-assembled nanoparticles with machine learning and DoE-ANN implementation: Optimization, characterization, and in-vitro assessment for the anticancer drug delivery.

This study aims to develop sorafenib-loaded self-assembled nanoparticles (SFB-SANPs) using the combined approach of artificial neural network and desi...

Informatics-driven solutions for optimal care delivery in liver transplantation.

Clinical informatics, which combines health information technology and clinical expertise, aims to improve health care delivery and outcomes. For cand...

Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study.

The early detection of liver fibrosis is of significant importance. Deep learning analysis of ultrasound backscattered radiofrequency (RF) signals is ...

Leveraging artificial intelligence for better translation of fibre-based pharmaceutical systems into real-world benefits.

The increasing prominence of biologics in the pharmaceutical market requires more advanced delivery systems to deliver these delicate and complex drug...

Deep Learning With Ultrasound Images Enhance the Diagnosis of Nonalcoholic Fatty Liver.

OBJECTIVE: This research aimed to improve diagnosis of non-alcoholic fatty liver disease (NAFLD) by deep learning with ultrasound Images and reduce th...