Latest AI and machine learning research in diabetes for healthcare professionals.
Importance: Prenatal exposure to gestational diabetes mellitus (GDM) has been associated with adverse metabolic, neurodevelopmental, and psychiatric outcomes in offspring. However, whether GDM-exposed youth exhibit heterogeneous neuroanatomical patterns remains unclear. Objective: To identify distinct cortical thickness subtypes among GDM-exposed youth and examine their associations with anthropom...
Insulin therapy for type 1 diabetes requires continual dose adjustment to meals, activity, stress, illness, and changing insulin sensitivity, creating a substantial self-management burden and increasing the risk of dosing errors. We developed the Dynamic Physiology-Aware Reinforcement learning Controller (DPARC), a zero-shot automated insulin delivery algorithm that infers latent physiological dyn...
This study developed a new Cellular Omics-Structural Integration (COSI) technology platform to address the limitation of traditional technologies in s...
Objectives: Diabetes affects over 500 million people globally and glycemia is inadequately managed. Metformin is the most frequently prescribed initia...
Diabetes is a chronic metabolic disease that can lead to serious health problems if not diagnosed and managed early. Big Data Analytics (BDA) and mach...
Self-Supervised Learning (SSL) has emerged as a powerful paradigm to mitigate the reliance on large, annotated datasets, a common bottleneck in medica...
Modern deep learning offers powerful tools for automated retinal screening, but it remains unclear how different visual model families compare in real...
Biomedical image segmentation is a critical task in medical diagnosis and treatment planning, enabling precise delineation of anatomical structures an...
Diabetes is a global health burden, and early detection is critical for timely intervention. This study explores a non-invasive, data-driven framework...
Visual Question Answering (VQA) holds great promise for clinical support, particularly in ophthalmology, where retinal fundus photography is essential...
Background: Heart failure (HF) is a major contributor to inpatient hospital utilization, with persistently high 30-day readmission rates. Existing pre...
Light microscopy imaging with histological stains is central to disease diagnosis and research. It is enhanced with immunostaining to reveal cellular ...
Purpose: Genetic risk scores (GRSs) are summaries of genetic data that can improve prediction of disease risk and progression. GRSs are increasing ava...
Whether individual transcripts carry intrinsic features that predetermine their response to external perturbations is unknown. Here we used nanopore d...
Purpose: In clinical practice, accurate prediction of disease risk must be accompanied by transparent, human-understandable explanations to support di...
Haemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening hyperinflammatory syndrome characterised by uncontrolled immune activation. Redu...
Background and Objectives Patients with peripheral neuropathies (PN) commonly exhibit balance impairment. In clinical practice, balance is typically a...
Diabetic Retinopathy (DR) is an art and science of recording and classifying the retinal images of a diabetic patient. DR classification deals with cl...
Tissue homeostasis and disease emerge from cell-cell interactions operating across spatial scales: from autocrine and juxtacrine signals within microm...
Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults worldwide, yet most automated screening systems are lim...