Diabetes

Advances in the application of artificial intelligence-driven multi-modal imaging technologies in the comprehensive diagnosis and treatment of diabetic foot ulcers.

Diabetic foot ulcers are serious skin wounds that affect many people with diabetes, often leading to...

Integrative bioinformatic analysis prioritizes TIMP1 and FN1 as angiogenesis-related candidate genes in diabetic foot ulcers.

BACKGROUND: Diabetic foot ulcers (DFUs) represent a severe chronic complication of diabetes and are ...

Machine learning models for predicting readmission after stroke: A systematic review and meta-analysis.

BACKGROUND: Hospital readmission following stroke poses a significant challenge for healthcare syste...

Glycolytic reprogramming in host response to Borrelia burgdorferi: A gene signature revealed by integrative bioinformatics analysis and machine learning.

Lyme disease (LD), a multifaceted condition caused by Borrelia burgdorferi (Bb), remains poorly unde...

Thrombospondin-2 is a performant biomarker of at-risk MASH and advanced MASH fibrosis in a large multicentre European cohort.

BACKGROUND: Non-invasive biomarkers with biological rationale are needed for identifying patients wi...

Identifying gut microbiome signatures of type 1 diabetes using machine learning and evolutionary feature selection.

BACKGROUND: Type 1 Diabetes Mellitus (T1D) has been increasingly associated with alterations in the ...

Experiences and Perceived Influence of the Artificial Intelligence-Based Health Education Accurately Linking System (AI-HEALS) on Health Behaviors Among Patients With Type 2 Diabetes: Qualitative Study.

BACKGROUND: The management of type 2 diabetes requires sustained self-management across diet, physic...

Clinically interpretable nomogram incorporating radiomics and deep learning feature fusion from abdominal CT for preclinical type 2 diabetes.

OBJECTIVE: To develop and externally validate a multimodal artificial intelligence framework for opp...

Public Expectations for Food and Drug Administration Approval of AI-Based Clinical Decision Support Tools: Quantitative Study.

BACKGROUND: Regulation of artificial intelligence (AI) has been slow relative to the pace of its int...

Methodological Approaches to and Reported Performance of Applications of Automated Machine Learning in Diabetes Risk Prediction: Rapid Review.

BACKGROUND: Type 2 diabetes (T2D) is a complex, chronic condition that imposes a substantial burden ...

Peripheral retinal haemorrhage density on ultra-widefield imaging as a novel biomarker for predicting diabetic retinopathy progression: a 2-year longitudinal study in Asians.

AIMS: To evaluate whether predominantly peripheral lesions (PPLs) and other imaging biomarkers on ul...

Machine learning-based prediction of diabetic retinopathy using clinlabomics: a multi-center study.

BACKGROUND: Diabetic retinopathy (DR) is a leading cause of vision loss, yet conventional retinal sc...

Cardiovascular Risk Stratification in Youth-Onset Type 2 Diabetes Using Machine Learning.

BACKGROUND: The rising incidence of youth-onset type 2 diabetes mellitus (T2DM), along with the risk...

Tubulointerstitial Diseases: An Updated Framework for Diverse and Emerging Entities.

Tubulointerstitial diseases represent a heterogeneous group of kidney diseases with diverse causes a...

Zwitterionic Lipid Nanotherapeutics from Mulberry for Oral Treatment of Diabetic Colitis.

Diabetic colitis is a severe gastrointestinal complication of type 2 diabetes, which presents the ke...

Transforming hypoglycemia prediction in adult type 1 diabetes: a systematic review and meta-analysis for precision care.

Type 1 diabetes mellitus (T1DM) patients require lifelong insulin therapy; however, iatrogenic hypog...

Machine learning and regional homogeneity reveal early and subtle brain changes in type 1 diabetes.

BACKGROUND AND PURPOSE: Type 1 diabetes mellitus (T1DM) usually begins early in life, and its develo...

Comment on "A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus".

SPISE index and ensemble machine learning refine cardiovascular risk stratification in stage 0-3 CKM syndrome.

BACKGROUND: While the single-point insulin sensitivity estimator (SPISE) shows promise as an insulin...

Global approval and certification of ophthalmic AI devices: A comparative regulatory perspective.

Artificial intelligence (AI) tools are rapidly reshaping ophthalmology by improving screening and di...

Beyond the Mean: A Machine Learning-Based Trend Analysis of CGM Metrics for Improved HbA1C Estimation in Type 2 Diabetes.

BACKGROUND: Hemoglobin A1C (HbA1C) is the gold standard for assessing long-term glycemic control in ...