Endocrinology

Diagnosis of thyroid cartilage invasion by laryngeal and hypopharyngeal cancers based on CT with deep learning.

OBJECTIVES: To develop a convolutional neural network (CNN) model to diagnose thyroid cartilage invasion by laryngeal and hypopharyngeal cancers observed on computed tomography (CT) images and evaluate the model's diagnostic performance.

The impact of clinical history on the predictive performance of machine learning and deep learning models for renal complications of diabetes.

BACKGROUND AND OBJECTIVE: Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy. The early identification of individuals at heightened risk of such complications or their exacerbation can be crucial to set a correct course of treatment. However, there are currently no widely accepted predictive tools for this task and, additionally, most of these models rely...

Deep learning for early detection of chronic kidney disease stages in diabetes patients: A TabNet approach.

Chronic kidney disease (CKD) poses a significant risk for diabetes patients, often leading to severe complications. Early and accurate CKD stage detec...

Nutritionally Responsive PMv DAT Neurons Are Dynamically Regulated During Pubertal Transition.

Pubertal development is tightly regulated by energy balance. The crosstalk between metabolism and reproduction is orchestrated by complex neural netwo...

A Machine Learning Approach to Molecular Initiating Event Prediction Using High-Throughput Transcriptomic Chemical Screening Data.

Improved scalability of high-throughput RNA-sequencing technologies has contributed to their proposed use in regulatory contexts for chemical hazard i...

Preserving Privacy, Increasing Accessibility, and Reducing Cost: An On-Device Artificial Intelligence Model for Medical Transcription and Note Generation

Background: Clinical documentation represents a significant burden for healthcare providers, with physicians spending up to 2 hours daily on adminis...

Topological Signatures vs. Gradient Histograms: A Comparative Study for Medical Image Classification

We present the first comparative study of two fundamentally distinct feature extraction techniques: Histogram of Oriented Gradients (HOG) and Topolo...

Evaluating Large Language Models for Multimodal Simulated Ophthalmic Decision-Making in Diabetic Retinopathy and Glaucoma Screening

Large language models (LLMs) can simulate clinical reasoning based on natural language prompts, but their utility in ophthalmology is largely unexpl...

Research on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n

Early detection and diagnosis of diabetic retinopathy is one of the current research focuses in ophthalmology. However, due to the subtle features o...

Sparse group LASSO and nonlinear machine learning for frequency-feature optimization in noninvasive blood glucose monitoring via bioimpedance spectroscopy.

Diabetic patients need to test their blood glucose levels (BGL) frequently; however, traditional methods of blood collection and testing cause great p...

Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.

Since the discovery of the life-saving hormone insulin in 1921 by Dr. Frederick Banting in 1921, there have been many critical discoveries and technic...

Equitable Deep Learning for Diabetic Retinopathy Detection Using Multidimensional Retinal Imaging With Fair Adaptive Scaling.

PURPOSE: To investigate the fairness of existing deep models for diabetic retinopathy (DR) detection and introduce an equitable model to reduce group ...

Deep learning-enhanced image analysis for liquid crystal optical sensing.

In liquid crystal (LC) sensors, each microliter of LC contains billions of molecules with numerous orientation combinations, generating thousands of o...

A supervised machine learning approach for predicting the need for postsurgical intervention in acromegaly.

OBJECTIVE: Patients with growth hormone (GH)-secreting pituitary adenomas (PAs) experience various symptoms and comorbidities, which can ultimately le...

Global Thyroid Cancer Patterns and Predictive Analytics: Integrating Machine Learning for Advanced Diagnostic Modelling.

BACKGROUND: The global increase in thyroid cancer prevalence, particularly among female populations, underscores critical gaps in our understanding of...

Neural Networks for On-Chip Model Predictive Control: A Method to Build Optimized Training Datasets and its Application to Type-1 Diabetes.

Training neural networks (NNs) to behave as model predictive control (MPC) algorithms is an effective way to implement them in constrained embedded de...

Recommendations for the Management of Diabetes During Ramadan Applying the Principles of the ADA/ EASD Consensus: Update 2025.

Ramadan fasting is a sacred ritual observed by approximately 1.8 billion Muslims each year, most of whom adhere to fasting due to its significance as ...

Integrative analyses of 16S rDNA sequencing and serum metabolomics demonstrate significant roles for the oral microbiota and serum metabolites in post-kidney transplant diabetes mellitus.

Oral microbiota and serum metabolites play crucial roles in diabetes, but their relationship with post-transplant diabetes mellitus (PTDM), a common c...

The Interplay of Cross-Organ Immune Regulation in Inflammation and Cancer.

Organs dynamically interact with each other through immunomodulation to create a systemic immune response and influence disease progression. While tra...

The Use of Continuous Glucose Monitoring to Diagnose Stage 2 Type 1 Diabetes.

This consensus report evaluates the potential role of continuous glucose monitoring (CGM) in screening for stage 2 type 1 diabetes (T1D). CGM offers a...