Endocrinology

Integrating Bioinformatics and Machine Learning to Identify Glucose Metabolism-Related Biomarkers with Diagnostic and Prognostic Value for Patients with Kidney Renal Clear Cell Carcinoma.

BACKGROUND: Glucose metabolism plays a critical role in the development and progression of kidney renal clear cell carcinoma (KIRC). This study aimed to identify glucose metabolism-related biomarkers (GRBs) and therapeutic targets for KIRC diagnosis and prognosis using bioinformatics and machine learning.

Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models

Accurate and quick diagnosis of normal brain tissue Glioma, Meningioma, and Pituitary Tumors is crucial for optimal treatment planning and improved medical results. Magnetic Resonance Imaging (MRI) is widely used as a non-invasive diagnostic tool for detecting brain abnormalities, including tumors. However, manual interpretation of MRI scans is often time-consuming, prone to human error, and dep...

Integrating Large Language Models with Human Expertise for Disease Detection in Electronic Health Records

Objective: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performa...

Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation

We propose a novel approach that adapts hierarchical vision foundation models for real-time ultrasound image segmentation. Existing ultrasound segme...

A Scalable Framework for Evaluating Health Language Models

Large language models (LLMs) have emerged as powerful tools for analyzing complex datasets. Recent studies demonstrate their potential to generate u...

Identification of hypertension subtypes using microRNA profiles and machine learning.

OBJECTIVE: Hypertension is a major cardiovascular risk factor affecting about 1 in 3 adults. Although the majority of hypertension cases (∼90%) are cl...

What is the role of human decisions in a world of artificial intelligence: an economic evaluation of human-AI collaboration in diabetic retinopathy screening

As Artificial intelligence (AI) has been increasingly integrated into the medical field, the role of humans may become vague. While numerous studies...

Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining

The differentiation between pathological subtypes of non-small cell lung cancer (NSCLC) is an essential step in guiding treatment options and progno...

Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection

Multi-view diabetic retinopathy (DR) detection has recently emerged as a promising method to address the issue of incomplete lesions faced by single...

Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling

Type 1 Diabetes (T1D) management is a complex task due to many variability factors. Artificial Pancreas (AP) systems have alleviated patient burden ...

The Role of Artificial Intelligence in Enhancing Insulin Recommendations and Therapy Outcomes

The growing worldwide incidence of diabetes requires more effective approaches for managing blood glucose levels. Insulin delivery systems have adva...

CoRLD: Contrastive Representation Learning Of Deformable Shapes In Images

Deformable shape representations, parameterized by deformations relative to a given template, have proven effective for improved image analysis task...

Inducing Causal Structure for Interpretable Neural Networks Applied to Glucose Prediction for T1DM Patients

Causal abstraction techniques such as Interchange Intervention Training (IIT) have been proposed to infuse neural network with expert knowledge enco...

AI-Driven Diabetic Retinopathy Diagnosis Enhancement through Image Processing and Salp Swarm Algorithm-Optimized Ensemble Network

Diabetic retinopathy is a leading cause of blindness in diabetic patients and early detection plays a crucial role in preventing vision loss. Tradit...

Fibonacci-Net: A Lightweight CNN model for Automatic Brain Tumor Classification

This research proposes a very lightweight model "Fibonacci-Net" along with a novel pooling technique, for automatic brain tumor classification from ...

Data-Driven Strategies for Carbimazole Titration: Exploring Machine Learning Solutions in Hyperthyroidism Control.

BACKGROUND: University Hospitals Dorset (UHD) has more than 1000 thyroid patient contacts annually. These are primarily patients with autoimmune hyper...

Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes.

BACKGROUND: Insulin resistance, lipotoxicity, and mitochondrial dysfunction contribute to the pathogenesis of metabolic dysfunction-associated steatot...

SurgicalVLM-Agent: Towards an Interactive AI Co-Pilot for Pituitary Surgery

Image-guided surgery demands adaptive, real-time decision support, yet static AI models struggle with structured task planning and providing interac...

Precise Insulin Delivery for Artificial Pancreas: A Reinforcement Learning Optimized Adaptive Fuzzy Control Approach

This paper explores the application of reinforcement learning to optimize the parameters of a Type-1 Takagi-Sugeno fuzzy controller, designed to ope...

Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments

Uncovering causal mediation effects is of significant value to practitioners seeking to isolate the direct treatment effect from the potential media...