Pregnancy

Deep Augmented Metric Learning Network for Prostate Cancer Classification in Ultrasound Images.

Prostate cancer screening often relies on cost-intensive MRIs and invasive needle biopsies. Transrectal ultrasound imaging, as a more affordable and non-invasive alternative, faces the challenge of high inter-class similarity and intra-class variability between benign and malignant prostate cancers. This complexity requires more stringent differentiation of subtle features for accurate auxiliary d...

Syn-Net: A Synchronous Frequency-Perception Fusion Network for Breast Tumor Segmentation in Ultrasound Images.

Accurate breast tumor segmentation in ultrasound images is a crucial step in medical diagnosis and locating the tumor region. However, segmentation faces numerous challenges due to the complexity of ultrasound images, similar intensity distributions, variable tumor morphology, and speckle noise. To address these challenges and achieve precise segmentation of breast tumors in complex ultrasound ima...

Optimization of enzyme-ultrasound assisted extraction from mulberries anthocyanins based on response surface methodology and deep neural networks and analysis of in vitro antioxidant activities.

This study used Xinjiang native "medicinal and food dual-use" resource mulberries as raw material, and optimized the extraction process of mulberries ...

Multi-task interaction learning for accurate segmentation and classification of breast tumors in ultrasound images.

In breast diagnostic imaging, the morphological variability of breast tumors and the inherent ambiguity of ultrasound images pose significant challeng...

Prediction of clinical risk factors in pregnancy using optimized neural network scheme.

Women should be aware of prenancy related health issues. A user-friendly model is developed in which the patients can use as well as clinicians to det...

Sustainable gelatin extraction from poultry skin-head-feet blend: An ultrasound-assisted approach.

The study investigated gelatin extraction from chicken skin-head-feet (SHF) blend using conventional and ultrasound-assisted methods with food-grade a...

Detection of obstetric anal sphincter injuries using machine learning-assisted impedance spectroscopy: a prospective, comparative, multicentre clinical study.

To evaluate the clinical performance and safety of the ONIRY system for obstetric anal sphincter injuries (OASI) detection versus three-dimensional en...

Machine learning-assisted design of immunomodulatory lipid nanoparticles for delivery of mRNA to repolarize hyperactivated microglia.

Regulating inflammatory microglia presents a promising strategy for treating neurodegenerative and autoimmune disorders, yet effective therapeutic age...

Prediction of Lymph Node Metastasis in Lung Cancer Using Deep Learning of Endobronchial Ultrasound Images With Size on CT and PET-CT Findings.

BACKGROUND AND OBJECTIVE: Echo features of lymph nodes (LNs) influence target selection during endobronchial ultrasound-guided transbronchial needle a...

Performance of a point-of-care ultrasound platform for artificial intelligence-enabled assessment of pulmonary B-lines.

BACKGROUND: The incorporation of artificial intelligence (AI) into point-of-care ultrasound (POCUS) platforms has rapidly increased. The number of B-l...

Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs.

PURPOSES: To develop a predictive model for recurrent intussusception based on abdominal ultrasound (US) images and abdominal radiographs.

Reliability and validity of a novel single-lead portable electrocardiogram device for pregnant women: a comparative study.

BACKGROUND: WenXinWuYang, a novel portable Artificial Intelligence Electrocardiogram (AI-ECG) device, can detect many kinds of abnormal heart disease ...

Complex-Valued Convolutional Gated Recurrent Neural Network for Ultrasound Beamforming.

Ultrasound detection is a potent tool for the clinical diagnosis of various diseases due to its real-time, convenient, and noninvasive qualities. Yet,...

GBCHV an advanced deep learning anatomy aware model for accurate classification of gallbladder cancer utilizing ultrasound images.

This study introduces a novel deep learning approach aimed at accurately classifying Gallbladder Cancer (GBC) into benign, malignant, and normal categ...

Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models.

OBJECTIVE: To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test ...

Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.

OBJECTIVE: The aim of this study was to develop and validate a deep learning radiomics (DLR) model based on longitudinal ultrasound data and clinical ...

Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer.

OBJECTIVE: This study explores the value of combining intratumoral and peritumoral radiomics features from ultrasound imaging with clinical characteri...

Evaluation of AI-based nerve segmentation on ultrasound: relevance of standard metrics in the clinical setting.

BACKGROUND: In artificial intelligence for ultrasound-guided regional anaesthesia, accurate nerve identification is essential. The technology communit...

Utilization of tree-based machine learning models for predicting low birth weight cases.

BACKGROUND: Low birth weight (LBW) is a health condition that affects over 20 million gestational outcomes worldwide. The current literature indicates...

Impact of an artificial intelligence decision support system among radiologists with different levels of experience in breast ultrasound: A prospective study in a tertiary center.

PURPOSE: To assess the impact of an artificial intelligence decision support system (Koios DS) on the diagnostic performance of radiologists with diff...