Latest AI and machine learning research in diabetes for healthcare professionals.
BACKGROUND: Cardiometabolic multimorbidity (CMM) is a major global health burden associated with increased morbidity and mortality. The oxidative balance score (OBS), a composite measure of dietary and lifestyle factors related to oxidative balance, has not been systematically evaluated in relation to CMM and mortality. METHODS: We analyzed 11,365 NHANES participants. Logistic regression, Cox prop...
Plasma extracellular vesicles (EVs) are considered excellent sources for biomarker discovery since they carry signatures of their cellular origin and disease processes. In this paper, we evaluate the potential of plasma EV proteomics analysis for identifying predictive biomarkers of developing type 1 diabetes (T1D), which results from autoimmune destruction of insulin-producing β cells in the isle...
INTRODUCTION: Evaluating retinal fundus image for diabetic retinopathy (DR) assessment is used to reduce the risk of blindness among diabetic patients...
BACKGROUND: Type 2 diabetes (T2D) causes multisystem complications, but an integrated multi-omics framework for cross-system, multi-outcome analysis i...
BACKGROUND/AIMS: Diabetic retinopathy (DR) is a major ocular complication of diabetes mellitus. While artificial intelligence (AI)-based DR screening ...
PURPOSE: Periodontitis is a common chronic disease associated with systemic conditions such as diabetes and cardiovascular disease. Diagnosis typicall...
BACKGROUND: The clinical value of artificial intelligence (AI)-based diagnostic systems depends not only on their accuracy but also on how well their ...
BACKGROUND: The rate of treatment failure with sodium-glucose cotransporter-2 inhibitors (SGLT2i) is high among individuals with type 2 diabetes (T2D)...
OBJECTIVES: To elicit stated preferences and willingness-to-pay (WTP) for artificial intelligence (AI)-enabled blended care in type 2 diabetes mellitu...
Background diabetes mellitus is prevalent among patients with acute ischemic stroke (AIS). The prognostic significance of long-term insulin treatment ...
Improving overall health and preventing complications is crucial for timely and effective treatment of diabetes patients. In this direction, accurate ...
PURPOSE: To develop a multisource machine learning model for detecting referral-warranted retinopathy of prematurity (RW-ROP) using retinal images and...
Diabetic retinopathy (DR) is a leading cause of blindness in middle-aged and elderly populations worldwide, and its diagnosis remains challenging due ...
BACKGROUND: Early and reliable grading of diabetic retinopathy is important for preventing avoidable vision loss. Although deep learning methods have ...
With the rising prevalence of type 2 diabetes (T2D) among children and adolescents, the ability to predict the progression of prediabetes to T2D in yo...
BACKGROUND: Hemorrhagic transformation (HT) is a major complication of acute ischemic stroke (AIS), especially after mechanical thrombectomy (MT) and ...
AIMS: Achieving optimal glycaemic control remains a burden for many people with diabetes on intensive insulin treatment. The MELISSA trial aims to cli...
Diabetic retinopathy (DR) is one of the major causes of preventable blindness in the world, and accurate large-scale screening tools are needed urgent...
BACKGROUND: Evidence is limited on whether daily step counts are uniform across individuals or whether combining them with genetic risk improves predi...
Needle electromyography (nEMG) is a valuable tool for diagnosing patients with neuromuscular diseases. However, it is labor-intensive and is prone to ...