AIMC Topic: Diabetes Mellitus, Type 2
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Artificial intelligence based predictive tools for identifying type 2 diabetes patients at high risk of treatment Non-adherence: A systematic review.
International journal of medical informatics
Mar 1, 2025
AIMS: Several Artificial Intelligence (AI) based predictive tools have been developed to predict non-adherence among patients with type 2 diabetes (T2D). Hence, this study aimed to describe and evaluate the methodological quality of AI based predicti...
Detecting severe coronary artery stenosis in T2DM patients with NAFLD using cardiac fat radiomics-based machine learning.
Scientific reports
Feb 25, 2025
To analyze radiomics features of cardiac adipose tissue in individuals with type 2 diabetes (T2DM) and non-alcoholic fatty liver disease (NAFLD), integrating relevant clinical indicators, and employing machine learning techniques to construct a preci...
SERPINA3: A Novel Therapeutic Target for Diabetes-Related Cognitive Impairment Identified Through Integrated Machine Learning and Molecular Docking Analysis.
International journal of molecular sciences
Feb 24, 2025
Diabetes-related cognitive impairment (DCI) is a severe complication of type 2 diabetes mellitus (T2DM), with limited understanding of its molecular mechanisms hindering effective therapeutic development. This study identified SERPINA3 as a potential...
Food-derived DPP4 inhibitors: Drug discovery based on high-throughput virtual screening and deep learning.
Food chemistry
Feb 21, 2025
Dipeptidyl peptidase-4 (DPP-4) is a critical target for the treatment of type 2 diabetes. This study outlines the development of six compounds derived from food sources and modified to create promising candidates for the treatment of diabetes. These ...
Diabetic peripheral neuropathy detection of type 2 diabetes using machine learning from TCM features: a cross-sectional study.
BMC medical informatics and decision making
Feb 18, 2025
AIMS: Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus. Early identification of individuals at high risk of DPN is essential for successful early intervention. Traditional Chinese medicine (TCM) tongue diagnos...
Optimising test intervals for individuals with type 2 diabetes: A machine learning approach.
PloS one
Feb 13, 2025
BACKGROUND: Chronic disease monitoring programs often adopt a one-size-fits-all approach that does not consider variation in need, potentially leading to excessive or insufficient support for patients at different risk levels. Machine learning (ML) d...
Predicting Type 2 diabetes onset age using machine learning: A case study in KSA.
PloS one
Feb 11, 2025
The rising prevalence of Type 2 Diabetes (T2D) in Saudi Arabia presents significant healthcare challenges. Estimating the age at onset of T2D can aid early interventions, potentially reducing complications due to late diagnoses. This study, conducted...
Association Between Aortic Imaging Features and Impaired Glucose Metabolism: A Deep Learning Population Phenotyping Approach.
Academic radiology
Feb 10, 2025
RATIONALE AND OBJECTIVES: Type 2 diabetes is a known risk factor for vascular disease with an impact on the aorta. The aim of this study was to develop a deep learning framework for quantification of aortic phenotypes from magnetic resonance imaging ...
Development and Validation of a Machine Learning Algorithm for Predicting Diabetes Retinopathy in Patients With Type 2 Diabetes: Algorithm Development Study.
JMIR medical informatics
Feb 7, 2025
BACKGROUND: Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. Machine learning (ML) systems can enhance DR in community-based screening. However, predictive power models for usability and performance are still being d...
Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes.
Journal of health, population, and nutrition
Feb 7, 2025
BACKGROUND: Diabetes mellitus, an endocrine system disease, is a common disease involving many patients worldwide. Many studies are performed to evaluate the correlation between micronutrients/macronutrients on diabetes but few of them have a high st...
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