Diabetes

A single session of strength training changed plasma levels of resistin, but not leptin in overweight and obese men.

Obesity has a complex multifactorial etiology and is characterized by excessive accumulation of adipose tissue. Visceral adipose tissue has deleterious effects on health because it secretes large amounts of inflammatory cytokines. Nutritional calorie restriction associated with strength training may be useful in managing chronic systemic inflammation. This study aimed to evaluate the acute effect ...

Deep learning innovations in diagnosing diabetic retinopathy: The potential of transfer learning and the DiaCNN model.

Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical need for early detection and timely intervention to avert visual deterioration. Diagnosing DR is inherently complex, as it necessitates the meticulous examination of intricate retinal images by experienced specialists. This makes the early diagnosis of DR essential for effective treatment and prevention...

Machine learning application in autoimmune diseases: State of art and future prospectives.

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and th...

Exhaled breath signal analysis for diabetes detection: an optimized deep learning approach.

In this study, a flexible deep learning system for breath analysis is created using an optimal hybrid deep learning model. To improve the quality of t...

Asprosin is a Reliable Predictor of Osteoporosis in Type 2 Diabetic Postmenopausal Women: A Case-Control Study.

The study evaluates Asprosin's value in diabetic postmenopausal women, examining its reliability as a predictor for osteoporosis (OP) in the second ty...

Retracted: Application Research of Artificial Intelligence Screening System for Diabetic Retinopathy.

[This retracts the article DOI: 10.1155/2022/2185547.].

Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review.

PURPOSE: Diabetes is a major public health challenge with widespread prevalence, often leading to complications such as Diabetic Nephropathy (DN)-a ch...

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis.

BACKGROUND: The use of machine learning and deep learning techniques in the research on diabetes has garnered attention in recent times. Nonetheless, ...

A novel approach for diabetic foot diagnosis: Deep learning-based detection of lower extremity arterial stenosis.

PURPOSE OF THE STUDY: Assessing the lower extremity arterial stenosis scores (LEASS) in patients with diabetic foot ulcer (DFU) is a challenging task ...

A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients.

An Artificial Intelligence (AI)-enabled human-centered smart healthcare monitoring system can be useful in life saving, specifically for diabetes pati...

Retinopathy of Prematurity in Late Preterm Twins with a Birth Weight Discordance: Can it be Predicted by Artificial Intelligence?

OBJECTIVES: The objective is to predict the development of retinopathy of prematurity (ROP) in discordant twins using a machine learning approach.

Machine learning approach reveals microbiome, metabolome, and lipidome profiles in type 1 diabetes.

INTRODUCTION: Type 1 diabetes (T1D) is a complex disorder influenced by genetic and environmental factors. The gut microbiome, the serum metabolome, a...

Soft robotics-inspired sensing system for detecting downward movement and pistoning in prosthetic sockets: A proof-of-concept study.

BACKGROUND: Vertical displacement of the residual limb within transtibial prosthetic socket, often known as "pistoning" or downward movement, may lead...

A cohort study on the predictive capability of body composition for diabetes mellitus using machine learning.

PURPOSE: We applied machine learning to study associations between regional body fat distribution and diabetes mellitus in a population of community a...

A stacked ensemble machine learning approach for the prediction of diabetes.

OBJECTIVES: Diabetes has become a leading cause of mortality in both developed and developing countries, impacting a growing number of individuals wor...

Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning.

BACKGROUND/AIMS: Fundus fluorescein angiography (FFA) is an important technique to evaluate diabetic retinopathy (DR) and other retinal diseases. The ...

Automated segmentation of ultra-widefield fluorescein angiography of diabetic retinopathy using deep learning.

BACKGROUND/AIMS: Retinal capillary non-perfusion (NP) and neovascularisation (NV) are two of the most important angiographic changes in diabetic retin...

The application of artificial intelligence in diabetic retinopathy screening: a Saudi Arabian perspective.

INTRODUCTION: Diabetic retinopathy (DR) is the leading cause of preventable blindness in Saudi Arabia. With a prevalence of up to 40% of patients with...

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges.

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of peo...

Serum myokines as potential biomarkers of myostatin inhibition in sport doping: a preliminary study on their baseline levels in elite athletes.

We considered in this study the possibility of developing an indirect procedure for detecting myostatin inhibition/suppression, a practice that is pro...