Endocrinology

Enhancing the accuracy of blood-glucose tests by upgrading FTIR with multiple-reflections, quantum cascade laser, two-dimensional correlation spectroscopy and machine learning.

The accuracy of screening diabetes from non-diabetes is drastically enhanced by strategically upgrading the bench-marking infrared spectroscopy technique for non-invasive tests of blood-glucose, both with state-of-the-art instrumentation-retrofits and with intelligent spectral-datamining tools. First, the signal-to-noise performance of FTIR in measuring the spectral features of a glucose solution ...

Multi-lesion segmentation guided deep attention network for automated detection of diabetic retinopathy.

Accurate multi-lesion segmentation together with automated grading on fundus images played a vital role in diagnosing and treating diabetic retinopathy (DR). Nevertheless, the intrinsic patterns of fundus lesions aggravated challenges in DR detection process. Therefore, we proposed a novel multi-lesion segmentation guided deep attention network (MSGDA-Net) for accurate and automated DR detection, ...

Screening of CAD-related secretory genes associated with type II diabetes based on comprehensive bioinformatics analysis and machine learning.

BACKGROUND: Type II diabetes mellitus (T2DM) is strongly linked with a heightened risk of coronary artery disease (CAD). Exploring biological targets ...

Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning.

BACKGROUND: This study aimed to characterize the oral and gut microbiota in prediabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) patients...

Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications.

Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDI...

Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis.

The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Pres...

Efficient brain tumor grade classification using ensemble deep learning models.

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental t...

A novel meta learning based stacked approach for diagnosis of thyroid syndrome.

Thyroid syndrome, a complex endocrine disorder, involves the dysregulation of the thyroid gland, impacting vital physiological functions. Common cause...

Physiological model-based machine learning for classifying patients with binge-eating disorder (BED) from the Oral Glucose Tolerance Test (OGTT) curve.

BACKGROUND AND OBJECTIVE: Binge eating disorder (BED) is the most frequent eating disorder, often confused with obesity, with which it shares several ...

Metabolomics-Based Machine Learning for Predicting Mortality: Unveiling Multisystem Impacts on Health.

Reliable predictors of long-term all-cause mortality are needed for middle-aged and older populations. Previous metabolomics mortality studies have li...

Prediction of Incident Diabetic Retinopathy in Adults With Type 1 Diabetes Using Machine Learning Approach: An Exploratory Study.

BACKGROUND: Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T...

Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-κB receptors based on machine learning and molecular docking.

Breast cancer, the second most prevalent cancer among women worldwide, necessitates the exploration of novel therapeutic approaches. To target the fou...

Editorial Commentary: Studies Evaluating Artificial Intelligence Large Language Models' Ability to Respond to Questions Are Repetitive and Out-of-Date: Artificial Intelligence Must Now Be Applied to Improve Clinical Practice and Patient Care.

While artificial intelligence (AI) technologies such as ChatGPT have shown very real and powerful capabilities to date, this does not mean that resear...

Robust brain MRI image classification with SIBOW-SVM.

Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting humans. Early detection and classification of brai...

mHPpred: Accurate identification of peptide hormones using multi-view feature learning.

Peptide hormones were first used in medicine in the early 20th century, with the pivotal event being the isolation and purification of insulin in 1921...

Improving Clinical Preparedness: Community Health Nurses and Early Hypoglycemia Prediction in Type 2 Diabetes Using Hybrid Machine Learning Techniques.

OBJECTIVES: The aim of the study was to analyze the data of diabetic patients regarding warning signs of hypoglycemia to predict it at an early stage ...

A machine learning tool for identifying newly diagnosed heart failure in individuals with known diabetes in primary care.

AIMS: We aimed to create a predictive model utilizing machine learning (ML) to identify new cases of congestive heart failure (CHF) in individuals wit...

Grading of diabetic retinopathy using a pre-segmenting deep learning classification model: Validation of an automated algorithm.

PURPOSE: To validate the performance of autonomous diabetic retinopathy (DR) grading by comparing a human grader and a self-developed deep-learning (D...

Development of a machine learning model for precision prognosis of rapid kidney function decline in people with diabetes and chronic kidney disease.

AIMS: To develop a machine learning model for predicting rapid kidney function decline in people with type 2 diabetes (T2D) and chronic kidney disease...

Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy.

The early diagnosis of diabetic retinopathy (DR) is challenging, highlighting the urgent need to identify new biomarkers. Immune responses play a cruc...