Latest AI and machine learning research in endocrinology for healthcare professionals.
Purpose: In clinical practice, accurate prediction of disease risk must be accompanied by transparent, human-understandable explanations to support diagnostic confidence, guide therapeutic decisions, and meet ethical and regulatory standards. While deep neural networks achieve high predictive performance in tasks such as cancer detection and diabetes risk stratification, their black-box nature pre...
Haemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening hyperinflammatory syndrome characterised by uncontrolled immune activation. Reduced high- and low-density lipoprotein cholesterol and hypertriglyceridaemia are reported in HLH, suggesting lipid metabolism disturbances although in-depth serum metabolomic analysis is lacking in HLH. Here a lipid-focused NMR spectroscopy platform w...
In routine care, individuals identified a priori as high-risk are usually tested for conditions more frequently. Protected attributes, such as sex or ...
Medication adherence among patients with diabetes remains suboptimal in low and middle income countries, including Nigeria. Emerging digital health in...
Large language models are increasingly deployed in clinical decision-support contexts, yet systematic evaluation of their factual reliability in gener...
We present a digital twin framework for real time glucose monitoring and forecasting in septic patients in intensive care units (ICUs). The framework ...
Clinical time-series forecasting is increasingly studied for decision support, yet standard aggregate metrics can obscure whether a model is actually ...
The systemic, metabolic, lifestyle factors have established associations with Alzheimer's Disease (AD) through epidemiologic and AD-specific biomarker...
The pituitary gland operates as an organized signaling network in which endocrine cell populations coordinate hormone secretion, through homotypic and...
Background: Diabetic retinopathy (DR) is the leading cause of preventable blindness among working-age adults worldwide, yet screening coverage remains...
Objective To develop and evaluate a novel machine learning (ML) framework tailored to a clinical diabetes dataset and to assess whether demographic st...
Thyroid cancer is a heterogeneous malignancy with variable outcomes, highlighting the need for reliable biomarkers and effective risk stratification. ...
Artificial Intelligence and Machine Learning (AI/ML) models used in clinical settings are increasingly deployed to support clinical decision-making. H...
Disease progression varies with age and is influenced by underlying genetic, biochemical, and hormonal etiologies, suggesting the need for tailored mo...
Metabolic dysfunction is increasingly recognized as a risk factor for poor outcomes in breast cancer, but whether incretin-based therapies confer surv...
Multimodal learning has the potential to improve clinical prediction by integrating complementary data sources, but the incremental value of imaging b...
Introduction: Accurate stratification of hard atherosclerotic cardiovascular disease (ASCVD) risk remains challenging despite advances in prevention. ...
Brain tumors represent one of the most critical neurological conditions, where early and accurate diagnosis is directly correlated with patient surviv...
Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, and automated grading systems play a crucial role in large-scale screenin...
Abstract Background Diabetic Retinopathy (DR) is one of the leading cause of vision loss and blindness. AI models have been instrumental in providing ...