Pregnancy

Using machine learning to investigate the influence of the prenatal chemical exposome on neurodevelopment of young children.

Research investigating the prenatal chemical exposome and child neurodevelopment has typically focus...

Predicting the efficacy of microwave ablation of benign thyroid nodules from ultrasound images using deep convolutional neural networks.

BACKGROUND: Thyroid nodules are frequent in clinical settings, and their diagnosis in adults is grow...

An integrated microfluidic and fluorescence platform for probing in vivo neuropharmacology.

Neurotechnologies and genetic tools for dissecting neural circuit functions have advanced rapidly ov...

FetDTIAlign: A deep learning framework for affine and deformable registration of fetal brain dMRI.

Diffusion MRI (dMRI) offers unique insights into the microstructure of fetal brain tissue in utero. ...

Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study.

BACKGROUND: Effective management of postoperative pain remains a significant challenge in obstetric ...

Smart MXene-based microrobots for targeted drug delivery and synergistic therapies.

MXenes and their composites exhibit remarkable electrical conductivity, mechanical flexibility, and ...

Portal dose image prediction using Monte Carlo generated transmission energy fluence maps of dynamic radiotherapy treatment plans: a deep learning approach.

This work aims to develop and investigate the feasibility of a hybrid model combining Monte Carlo (M...

Point-of-care ultrasound in obstetric anesthesia clinical practice.

PURPOSE OF THE REVIEW: Point-of-care ultrasound (POCUS) is increasingly recognized as a valuable too...

Mother: a maternal online technology for health care dataset.

OBJECTIVES: These data enable the development of both textual and speech based conversational machin...

Use of Artificial Intelligence and Machine Learning in Critical Care Ultrasound.

This article explores the transformative potential of artificial intelligence (AI) in critical care ...

Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.

Early diagnosis and access to resources, support and therapy are critical for improving long-term ou...

Prediction of Seronegative Hashimoto's thyroiditis using machine learning models based on ultrasound radiomics: a multicenter study.

BACKGROUND: Seronegative Hashimoto's thyroiditis is often underdiagnosed due to the lack of antibody...

Development and Validation of an Algorithm for Segmentation of the Prostate and its Zones from Three-dimensional Transrectal Multiparametric Ultrasound Images.

BACKGROUND AND OBJECTIVE: Multiparametric ultrasound (mpUS) is being investigated as an alternative ...

Using artificial intelligence system for assisting the classification of breast ultrasound glandular tissue components in dense breast tissue.

To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound ana...

Correction to "Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis".

A text dataset of fire door defects for pre-delivery inspections of apartments during the construction stage.

Defect classification from text descriptions written by inspectors during the construction stage can...

FreqYOLO: A uterine disease detection network based on local and global frequency feature learning.

Leiomyomas (LM) and adenomyosis (AM) are common gynecological diseases with high incidence rates and...

Robot-assisted ultrasound probe calibration for image-guided interventions.

BACKGROUND: Trackable ultrasound probes facilitate ultrasound-guided procedures, allowing real-time ...

Predicting high-risk factors for postoperative inadequate analgesia and adverse reactions in cesarean delivery surgery: a prospective study.

BACKGROUND: Early identification of high-risk factors for inadequate analgesia and adverse reactions...

Prediction of IUGR condition at birth by means of CTG recordings and a ResNet model.

OBJECTIVE: Sub-optimal uterine-placental perfusion and fetal nutrition can lead to intrauterine grow...

FET-UNet: Merging CNN and transformer architectures for superior breast ultrasound image segmentation.

PURPOSE: Breast cancer remains a significant cause of mortality among women globally, highlighting t...