Endocrinology

Identification of crucial genes for polycystic ovary syndrome and atherosclerosis through comprehensive bioinformatics analysis and machine learning.

OBJECTIVE: To identify potential biomarkers in patients with polycystic ovary syndrome (PCOS) and atherosclerosis, and to explore the common pathologic mechanisms between these two diseases in response to the increased risk of cardiovascular diseases in patients with PCOS.

Food-derived DPP4 inhibitors: Drug discovery based on high-throughput virtual screening and deep learning.

Dipeptidyl peptidase-4 (DPP-4) is a critical target for the treatment of type 2 diabetes. This study outlines the development of six compounds derived from food sources and modified to create promising candidates for the treatment of diabetes. These compounds were identified through a combination of virtual screening, deep learning algorithms, ADMET characterization assessment, and molecular dynam...

Disease diagnostics using machine learning of B cell and T cell receptor sequences.

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use o...

Plantar Thermogram Analysis Using Deep Learning for Diabetic Foot Risk Classification.

AIMS: Thermography is a noninvasive method to identify patients at risk of diabetic foot ulcers. In this study, we employed thermography and deep lear...

Enhancing diagnostic accuracy of thyroid nodules: integrating self-learning and artificial intelligence in clinical training.

PURPOSE: This study explores a self-learning method as an auxiliary approach in residency training for distinguishing between benign and malignant thy...

Use of a Convolutional Neural Network to Predict the Response of Diabetic Macular Edema to Intravitreal Anti-VEGF Treatment: A Pilot Study.

PURPOSE: To utilize a convolutional neural network (CNN) to predict the response of treatment-naïve diabetic macular edema (DME) to a single injection...

Diagnostic Accuracy of IDX-DR for Detecting Diabetic Retinopathy: A Systematic Review and Meta-Analysis.

PURPOSE: Diabetic retinopathy (DR) is a leading cause of vision loss worldwide, making early detection critical to prevent blindness. IDX-DR, an FDA-a...

Automated Process for Monitoring of Amiodarone Treatment: Development and Evaluation.

BACKGROUND: Amiodarone treatment requires repeated laboratory evaluations of thyroid and liver function due to potential side effects. Robotic process...

Comparative analysis for accurate multi-classification of brain tumor based on significant deep learning models.

Brain tumours are a significant health concern, often resulting in severe cognitive and physiological impairments. Accurate detection and classificati...

Predicting diabetes self-management education engagement: machine learning algorithms and models.

INTRODUCTION: Diabetes self-management education (DSME) is endorsed by the American Diabetes Association (ADA) as an essential component of diabetes m...

Enhancing diabetic retinopathy diagnosis: automatic segmentation of hyperreflective foci in OCT via deep learning.

OBJECTIVE: Hyperreflective foci (HRF) are small, punctate lesions ranging from 20 to 50 m and exhibiting high reflective intensity in optical coheren...

Leveraging OGTT derived metabolic features to detect Binge-eating disorder in individuals with high weight: a "seek out" machine learning approach.

Binge eating disorder (BED) carries a 6 times higher risk for obesity and accounts for roughly 30% of type 2 diabetes cases. Timely identification of ...

Diabetic peripheral neuropathy detection of type 2 diabetes using machine learning from TCM features: a cross-sectional study.

AIMS: Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus. Early identification of individuals at high risk of D...

Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases.

Retinal images provide a non-invasive and accessible means to directly visualize human blood vessels and nerve fibers. Growing studies have investigat...

Artificial Intelligence for Diabetic Foot Screening Based on Digital Image Analysis: A Systematic Review.

INTRODUCTION: Early detection of diabetic foot complications is essential for effective management and prevention of complications. Artificial intelli...

Retinal fundus imaging-based diabetic retinopathy classification using transfer learning and fennec fox optimization.

Diabetic retinopathy (DR) is a serious complication of diabetes that can result in vision loss if untreated, often progressing silently without warnin...

Robot-Assisted Approach to Diabetes Care Consultations: Enhancing Patient Engagement and Identifying Therapeutic Issues.

: Diabetes is a rapidly increasing global health challenge compounded by a critical shortage of diabetes care and education specialists. Robot-assiste...

Diagnosis of Thyroid Nodule Malignancy Using Peritumoral Region and Artificial Intelligence: Results of Hand-Crafted, Deep Radiomics Features and Radiologists' Assessment in Multicenter Cohorts.

OBJECTIVE: To develop, test, and externally validate a hybrid artificial intelligence (AI) model based on hand-crafted and deep radiomics features ext...

Breaking barriers: noninvasive AI model for BRAF mutation identification.

OBJECTIVE: BRAF is the most common mutation found in thyroid cancer and is particularly associated with papillary thyroid carcinoma (PTC). Currently, ...

Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.

BACKGROUND: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at significantly high risk of cardiovascular events (CVEs),...