Pregnancy

Deep learning for non-invasive detection of steatosis and fibrosis in MASLD: a multicenter study with a new fibroscan-labelled ultrasound dataset.

PURPOSE: This study aimed to develop and validate deep learning models for non-invasive assessment of hepatic steatosis and fibrosis using conventional B-mode ultrasound images, with Fibroscan-derived measurement as reference standard. METHODS: We utilized a multi-source approach comprising three distinct ultrasound datasets of patients with MASLD: a private clinical dataset (DS1, n = 111 patients...

GCLM as a novel biomarker for preeclampsia: Integrating bioinformatics and mechanistic validation.

INTRODUCTION: Preeclampsia (PE) is a pregnancy-specific disorder associated with hypertension and multi-organ dysfunction, posing serious risks to maternal and fetal health. Early detection remains challenging, highlighting the urgent need to identify reliable molecular biomarkers for improved diagnosis and therapeutic intervention. METHODS: We integrated Gene Expression Omnibus (GEO) datasets (GS...

Guiding design and performance of nonviral nucleic acid delivery vehicles via machine learning.

Machine Learning (ML) techniques have enabled the advancement of many technologies throughout the pharmaceutical industry, especially for drug discove...

Deep Learning-Based Skin Lesion Classification: A CNN Approach on High-Frequency Ultrasound Imaging.

High-frequency ultrasound (HFUS) is valuable for assessing skin lesions, supporting diagnosis, treatment monitoring, and surgical planning. This study...

Clinical Application of an AI-Driven Framework for Accurate Endometrial Thickness Measurement in Transvaginal Ultrasound.

OBJECTIVE: This study aims to develop an AI-based framework for automatic endometrial thickness (ET) measurement in transvaginal ultrasound (TVUS) bas...

Transformer-based ultrasound radiomics for novel multi-task automated segmentation and classification of ovarian tumors.

OBJECTIVE: To develop and evaluate a comprehensive AI-driven pipeline for automated segmentation and multi-class classification of ovarian tumors in u...

The use of machine learning to predict pharmacological therapy in gestational diabetes: A scoping review.

AIMS: Early identification of pharmacological therapy for gestational diabetes mellitus (GDM), a common pregnancy complication, through machine learni...

A Novel Instance Segmentation Method for Real-Time Detection of Thyroid Nodules in Ultrasound Videos: A Multi-Center Study.

OBJECTIVES: Thyroid nodules are one of the most common thyroid disorders and can be categorized into benign and malignant thyroid nodules. Currently, ...

Review of Techniques and Technologies for Mosaicking Ultrasound Volumes.

Ultrasound imaging, known for its affordability, portability and real-time capabilities, has become a crucial diagnostic tool worldwide. However, the ...

Feasibility of deep learning-based cancer detection in ultrasound microvascular images.

Acoustic angiography is a superharmonic contrast-enhanced ultrasound modality that maps 3-D microvasculature with fine spatial resolutions and has dem...

Deep Learning Radiomics Model Based on Ultrasound Images Predicts Myometrial Infiltration of Endometrial Cancer.

OBJECTIVE: This study aims to develop and validate a deep learning radiomics (DLR) model based on ultrasound images for non-invasively distinguishing ...

Ultrasound to guide critical decisions: What you need to know.

BACKGROUND: Hypotension in the intensive care unit (ICU) demands rapid diagnosis and intervention, as delays in identifying the etiology of shock dire...

Emerging landscape of bioinformatics and artificial intelligence applications in cell-penetrating peptide-based delivery.

INTRODUCTION: Peptides play diverse roles in biological processes, including drug discovery, antibacterial activity, and protein-protein interactions,...

AI landmark versus conventional ultrasound-guided injections for adhesive capsulitis: a prospective, propensity score-matched, superiority study.

AIM: To investigate the artificial Intelligence (AI) landmark for guiding rotator cuff interval injections for adhesive capsulitis (AC). MATERIAL AND ...

Machine learning versus traditional formulas for fetal weight estimation: An international multicenter study evaluating prediction accuracy across birth weight percentiles.

OBJECTIVE: To assess whether machine learning (ML) offers improved birth weight prediction accuracy, since despite numerous models, the Hadlock formul...

Illuminating Research Dynamics: Medical Ultrasound and Deep Learning.

BACKGROUND: Deep learning (DL) has enabled advances in ultrasound imaging, but challenges like limited datasets and device variability hinder progress...

Artificial Intelligence-Assisted Indocyanine Green Angiography for Perforators Identification in the Anterolateral Thigh Flap.

BACKGROUND: The anterolateral thigh (ALT) flap is widely used for head and neck reconstruction because of its versatility and reliable vascular supply...

Evaluation of an artificial intelligence model based on multiparametric transrectal ultrasound for localizing clinically significant prostate cancer by simulation of targeted biopsies.

INTRODUCTION AND OBJECTIVES: An AI model that performs well during training does not guarantee similar performance in clinical practice and should be ...

Predicting hydrophone phase and evaluating its uncertainty using magnitude data and minimum phase approach.

Hydrophones are commonly used to measure the acoustic output of ultrasound transducers and devices. Commonly, only the magnitude response of a hydroph...

An Ultrasound-Based Machine Learning Model for Differentiating IgG4-Related Sialadenitis and Primary Sjögren's Syndrome.

INTRODUCTION AND AIMS: To establish and validate an interpretable machine learning (ML) model based on ultrasound (US) scoring system for differentiat...