Pregnancy

Interpretable Machine Learning Model Using Digitized US Features for Classifying Complex Thyroid Nodules.

Purpose To develop a digitized integrated feature-based interpretable machine learning classification model to accurately recognize complex thyroid nodules while efficiently diagnosing conventional thyroid nodules (thyroid nodules with typical benign or malignant ultrasound features). Materials and Methods Thyroid ultrasound images with pathologically confirmed nodules were retrospectively collect...

Accelerating Siloxane-Based Ionizable Lipid Design for LNPs with Data-Efficient Kolmogorov-Arnold Networks.

Ionizable lipids are fundamental to the efficacy of lipid nanoparticles (LNPs) in pivotal areas including mRNA vaccines. Their development, however, is hindered by intricate structure-property relationships and limited experimental data. To address these challenges, this study proposed a small-data-driven framework that pioneered the use of Kolmogorov-Arnold networks (KANs)─a symbolic regression-b...

HappyMums mobile application study protocol: use of a smartphone application to gather data predictive of antenatal depression.

INTRODUCTION: Mobile health (mHealth) technologies have become increasingly popular for monitoring mental health symptoms and lifestyle behaviours, an...

Automated diagnosis of plus form and early stages of ROP using deep learning models.

Retinopathy of Prematurity (ROP) represents a critical ophthalmological pathology affecting premature infants, with established associations to low bi...

Recent Trends in the Development and Clinical Translation of Polymer-based Targeted Therapeutic Nanoparticle.

Polymeric nanoparticles are a state-of-the-art innovation in nanomedicine, offering site-specific drug delivery, an improved pharmacokinetic profile, ...

Lipid nanoparticle-delivered ornithine transcarbamylase mRNA alleviates liver fibrosis in preclinical models.

BACKGROUND AND AIMS: Liver fibrosis poses a major health threat globally, with an acute shortage of effective treatments to stop or reverse its progre...

Development and validation of an artificial intelligence-based model for diagnosing benign, borderline, and malignant adnexal masses.

Classification of benign, borderline, and malignant adnexal masses is critical to effective clinical management, but remains a challenge. We developed...

Beyond the dose: a clearance-enabled in vitro platform for evaluating local therapies.

Inclusion of physiologically relevant clearance mechanisms into organ-on-a-chip models is essential to reproduce tissue exposure and predict therapeut...

Micro-/nanorobots in nanomedicine - Guidance, imaging and the integration of AI and robotics.

The integration of robotics and artificial intelligence (AI) into nanomedicine represents a significant advancement in developing targeted therapeutic...

Machine Learning-Based Beam Delivery Time Model for Mevion S250i With Hyperscan Technology.

PURPOSE: Accurate prediction of beam delivery time (BDT) is critical for operational efficiency, 4D dose calculations, and advanced proton therapy tec...

AI-Guided Design of pH- and Redox-Responsive Lignin@GO@ZIF-8 Nanocarriers for Targeted Co-Delivery of 5-Fluorouracil and Metformin in Prostate Cancer Therapy.

Prostate cancer therapy is limited by systemic toxicity and inefficient tumor-selective delivery. Here we report a multi-stimuli-responsive nanocompos...

An integrative deep learning model based on dual-mode ultrasound for diagnosing gallbladder polyps.

OBJECTIVES: The aim of this study was to develop an artificial intelligence model to automatically differentiate between non-neoplastic and neoplastic...

Exploration of the Application of Multimodal Feature Analysis Based on Random Forest Algorithm Combining Ultrasound Elastography and Contrast-Enhanced Ultrasound in the Diagnosis of Ovarian Tumors.

OBJECTIVE: This study aimed to build a multimodal ultrasound (color Doppler flow imaging/shear wave elastography/contrast-enhanced ultrasound) combine...

Predicting macrosomia and low birth weight with interpretable machine learning.

Abnormal birth weight, including macrosomia and low birth weight, constitutes a significant global health burden associated with both immediate neonat...

Hematologic markers and machine learning in predicting placenta accreta: A case-control study.

OBJECTIVE: This study aims to enhance antenatal detection of placenta accreta spectrum (PAS) and predict severe hemorrhage at delivery using machine l...

Modeling Zero-Dose Children in Ethiopia: A Machine Learning Perspective on Model Performance and Predictor Variables.

BACKGROUND: Despite progress in childhood vaccination, many children in low- and middle-income countries, including Ethiopia, remain unvaccinated, pre...

Echo Flow-Induced Temporal Correlation Learning for Ultrasound Video Object Segmentation.

OBJECTIVE: The segmentation of ultrasound video objects aims to delineate specific anatomical structures or areas of injury in sequential ultrasound i...

Ultrasound Scanner ASIC With 1-D CNN-Based Echo Pattern Recognition for Arterial Distension Monitoring.

This paper presents an A-mode ultrasound scanner application-specific integrated circuit (ASIC) for arterial distension monitoring. The ASIC operates ...

Learning Roles With Emergent Social Value Orientations.

Social dilemmas can be considered situations where individual rationality leads to collective irrationality. The multi-agent reinforcement learning co...

The Intelligent Needle: The Role of Artificial Intelligence in Ultrasound-guided Regional Anesthesia.

The integration of artificial intelligence (AI) into ultrasound-guided regional anesthesia (UGRA) marks a new stage in anesthetic practice. Since ultr...