Endocrinology

Latest AI and machine learning research in endocrinology for healthcare professionals.

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Development and validation of predictive models for diabetic retinopathy using machine learning.

OBJECTIVE: This study aimed to develop and compare machine learning models for predicting diabetic r...

Neural Network-Enhanced Electrochemical/SERS Dual-Mode Microfluidic Platform for Accurate Detection of Interleukin-6 in Diabetic Wound Exudates.

Interleukin-6 (IL-6) plays a pivotal role in the inflammatory response of diabetic wounds, providing...

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 at...

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...

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

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory ...

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 f...

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 detecti...

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 ...

Building an intelligent diabetes Q&A system with knowledge graphs and large language models.

INTRODUCTION: This paper introduces an intelligent question-answering system designed to deliver per...

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 physiologica...

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. Ear...

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% o...

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 exhib...

Capsule network-based deep learning for early and accurate diabetic retinopathy detection.

Glaucoma, an optic nerve disease resulting in blindness if left untreated, is a difficult condition ...

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

INTRODUCTION: Diabetes self-management education (DSME) is endorsed by the American Diabetes Associa...

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...

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 un...

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 diab...

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