Diabetes

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 method based on serum PLA2R antibodies has gradually been applied in clinical practice, only 52-86% of PLA2R-associated MN patients show positive anti-PLA2R antibodies. Therefore, renal biopsy remains the gold standard for diagnosing MN. However, the renal biopsy procedure is highly complex and invol...

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 abnormal glycogen storage and breakdown. These disorders are caused by mutations in G6PC1, which is essential for proper glucose storage and metabolism. With the advent of continuous glucose monitoring systems, development of algorithms to analyze and predict glucose levels has gained considerable atte...

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 targets and development of complications.

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 predict sarcopenia among postsurgical gastric cancer pat...

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, providing valuable insights into hemorrhagic transforma...

Predicting High Glycemia Risk Index Trajectory in Individuals With Type 1 Diabetes and Long-term Continuously Glucose Monitoring.

The glycemia risk index (GRI) is an emerging metric designed to quantify the risk of both hypo- and hyperglycemia, providing a combined assessment of ...

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

BACKGROUND: Diabetes mellitus (DM) increases the risk of vascular complications, and retinal vasculature imaging serves as a valuable indicator of bot...

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, especially with the increasing reliance on AI-driven diag...

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 programme includes annual population-based screening for d...

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 occurrence of retinopathy of prematurity (ROP) and treatme...

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 deep learning models, achieving automated segment...

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 with diabetes mellitus (DM) is crucial due to high in...

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 (RIHN) in patients with nasopharyngeal carcinoma (...

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 colorimetric paper sensor (CPS) images. We used an image da...

Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus.

BACKGROUND: Diabetic kidney disease (DKD) is a common and serious complication of diabetic mellitus (DM). More sensitive methods for early DKD predict...

Acceptability, applicability, and cost-utility of artificial-intelligence-powered low-cost portable fundus camera for diabetic retinopathy screening in primary health care settings.

AIMS: To evaluate the acceptability, applicability, and cost-utility of AI-powered portable fundus cameras for diabetic retinopathy (DR) screening in ...

Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning.

The burden of diabetic foot ulcers (DFU) is exacerbated in diabetic patients with concomitant arteriosclerotic occlusion disease (ASO) in the lower ex...

An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment.

Chronic disease (CD) like diabetes and stroke impacts global healthcare extensively, and continuous monitoring and early detection are necessary for e...

Generating evidence to support the role of AI in diabetic eye screening: considerations from the UK National Screening Committee.

Screening for diabetic retinopathy has been shown to reduce the risk of sight loss in people with diabetes, because of early detection and treatment o...

Identification of biomarkers related to iron death in diabetic kidney disease based on machine learning algorithms.

BACKGROUND: While ferroptosis has been recognised for its key role in tumour development, its involvement in DKD is not well understood. Identifying d...