Latest AI and machine learning research in diabetes for healthcare professionals.
OBJECTIVE: Diabetes Insipidus (DI) is a common complication that occurs following transsphenoidal surgery for sellar lesions. DI is usually transient but can be permanent in select patients. Prior studies have described preoperative risk factors for developing postoperative DI. However, no predictive risk score has been created to risk stratify these patients.
Diabetes mellitus (DM), a prevalent metabolic disorder, has significant global health implications. The advent of machine learning (ML) has revolutionized the ability to predict and manage diabetes early, offering new avenues to mitigate its impact. This systematic review examined 53 articles on ML applications for diabetes prediction, focusing on datasets, algorithms, training methods, and evalua...
Diabetes mellitus, a chronic metabolic disorder, is characterized by high blood glucose levels. External insulin administration, along with diet and e...
AIMS: To investigate the prognostic value of the glucose-to-albumin ratio (GAR) in predicting 30-day and 90-day mortality in septic ICU patients.
Diabetes mellitus (DM) is a highly prevalent chronic condition with significant health and economic impacts; therefore, an accurate diagnosis is essen...
OBJECTIVES: To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensio...
Diabetic foot ulcers (DFUs), a class of chronic wounds, affect ~750,000 individuals every year in the US alone and identifying non-healing DFUs that...
Retinal diseases such as Diabetic Retinopathy (DR) and Macular Hole (MH) significantly impact vision and affect millions worldwide. Early detection ...
Hepatitis B Virus-associated membranous nephropathy (HBV-MN) significantly impacts renal health, particularly in areas with high HBV prevalence. Under...
Accurate diabetes risk prediction relies on identifying key features from complex health datasets, but conventional methods like mutual information ...
The global prevalence of diabetes, particularly type 2 diabetes mellitus (T2DM), is rapidly increasing, posing significant health and economic chall...
Retinal imaging has emerged as a powerful, non-invasive modality for detecting and quantifying biomarkers of systemic diseases-ranging from diabetes...
Effective and accurate diagnosis of diseases such as cancer, diabetes, and heart failure is crucial for timely medical intervention and improving pa...
Using massive datasets, foundation models are large-scale, pre-trained models that perform a wide range of tasks. These models have shown consistent...
Hypoglycemia is a serious complication in individuals with type 2 diabetes mellitus. Identifying who is most at risk remains challenging due to the no...
BACKGROUND: Diabetic oral ulceration (DOU) is a prevalent and debilitating complication among diabetic patients, significantly impairing their quality...
This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (SDNet), for simultaneous denoising and super-resolu...
The scarcity of high-quality, labelled retinal imaging data, which presents a significant challenge in the development of machine learning models fo...
BACKGROUND: Chronic obstructive pulmonary disease (COPD) and type 2 diabetes mellitus (T2DM) are on the rise. While there is evidence of a link betwee...
Pancreatic cancer is a devasting disease which is an increasing cause of cancer mortality. The aim of this study was to characterise, using descriptiv...