Diabetes

Artificial intelligence to guide rituximab therapy in patients with phospholipase A2 receptor-associated membranous nephropathy.

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals.

Diabetes is a growing global health concern, affecting millions and leading to severe complications ...

Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0-3 and the development of a machine learning prediction model: a nationwide prospective cohort study.

BACKGROUND: The American Heart Association recently introduced the concept of cardiovascular-kidney-...

Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests.

Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability an...

Gut microbiome research: Revealing the pathological mechanisms and treatment strategies of type 2 diabetes.

The high prevalence and disability rate of type 2 diabetes (T2D) caused a huge social burden to the ...

A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease.

Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abn...

Rapid diagnosis of membranous nephropathy based on kidney tissue Raman spectroscopy and deep learning.

Membranous nephropathy (MN) is one of the most common glomerular diseases. Although the diagnostic m...

Detection of diabetic macular oedema patterns with fine-grained image categorisation on optical coherence tomography.

PURPOSE: To develop an artificial intelligence (AI) system for detecting pathological patterns of di...

Machine learning for high-risk hospitalization prediction in outpatient individuals with diabetes at a tertiary hospital.

OBJECTIVE: To characterize, via a predictive model using real-world data, patients with diabetes wit...

Predicting diabetes mellitus metabolic goals and chronic complications transitions-analysis based on natural language processing and machine learning models.

OBJECTIVE: To estimate Diabetes mellitus (DM) progression at one and two years in terms of glycemic ...

Risk Factors and Outcomes of Hemorrhagic Transformation in Acute Ischemic Stroke Following Thrombolysis: Analysis of a Single-Center Experience and Review of the Literature.

: This is a retrospective study conducted at the Clinical County Hospital of Craiova, Romania, provi...

Comparative study of XGBoost and logistic regression for predicting sarcopenia in postsurgical gastric cancer patients.

The use of machine learning (ML) techniques, particularly XGBoost and logistic regression, to predic...

Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.

BACKGROUND: Diabetes mellitus (DM) increases the risk of vascular complications, and retinal vascula...

Deep Learning Approach Readily Differentiates Papilledema, Non-Arteritic Anterior Ischemic Optic Neuropathy, and Healthy Eyes.

OBJECTIVE: Deep learning (DL) has been used in differentiating a range of ophthalmic conditions. We ...

Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings.

Ensuring data privacy in medical image classification is a critical challenge in healthcare, especia...

Optimizing Diabetic Retinopathy Screening at Primary Health Centres in India: A Cost-Effectiveness Analysis.

BACKGROUND: The eye care package under the Ayushman Bharat comprehensive primary healthcare programm...

Prediction of retinopathy of prematurity development and treatment need with machine learning models.

BACKGROUND: To evaluate the effectiveness of machine learning (ML) models in predicting the occurren...

Construction and validation of a deep learning-based diagnostic model for segmentation and classification of diabetic foot.

OBJECTIVE: This study aims to conduct an in-depth analysis of diabetic foot ulcer (DFU) images using...

Development and validation of Prediction models for radiation-induced hypoglossal neuropathy in patients with nasopharyngeal carcinoma.

BACKGROUND AND PURPOSE: To establish predictive models for radiation-induced hypoglossal neuropathy ...

Application of an interpretable machine learning method to predict the risk of death during hospitalization in patients with acute myocardial infarction combined with diabetes mellitus.

BACKGROUND: Predicting the prognosis of patients with acute myocardial infarction (AMI) combined wit...

Transfer learning and data augmentation for glucose concentration prediction from colorimetric biosensor images.

A deep learning algorithm is introduced to accurately predict glucose concentrations using colorimet...