Latest AI and machine learning research in diabetes for healthcare professionals.
Anti-vascular endothelial growth factor (anti-VEGF) therapy is the mainstay of management for diabet...
OBJECTIVES: Automated segmentation of retinal blood vessels in optical coherence tomography angiogra...
The clinical presentation of acromegaly reflects systemic effects of chronic GH and IGF-I excess. Di...
BACKGROUND: Hypoglossal neuropathy is the most common lower cranial neuropathy detected as a delayed...
AIMS: Digital interventions have emerged as promising tools to support mental well-being in diabetes...
Diabetes mellitus represents one of the most prevalent chronic diseases worldwide, posing serious ch...
PURPOSE: To develop a machine learning (ML)-driven polygenic risk score (PRS) for diabetic retinopat...
This study aims to assess the predictive value of dietary antioxidants in diabetes-cancer comorbidit...
The rapid fabrication of artificial intelligence-based hardware worldwide is intensifying the electr...
BACKGROUND: Glycated hemoglobin (HbA1c) is a convenient tool to evaluate glycemic status but its abi...
Emerging evidence implicates cerebellar degeneration-related protein 1 antisense transcript (CDR1as)...
BACKGROUND: Progress in type 1 diabetes (T1D) algorithm development is limited by the fragmentation ...
BACKGROUND: Continuous glucose monitoring (CGM) provides real-time glucose data, aiding diabetes man...
AIMS: To evaluate large language models (LLMs) accuracy in carbohydrate (CHO) counting (CC) and comp...
OBJECTIVE: To evaluate the predictive utility of the initial lactate-to-albumin ratio (LAR) measured...
PURPOSE: To test inter-observer variation of plus disease diagnoses among Norwegian ophthalmologists...
Hyperglycemia is a major risk factor for chronic kidney disease (CKD). This multicenter prospective ...
Type 2 diabetes (T2D), inflammatory bowel disease (IBD), and colorectal cancer (CRC) share overlappi...