Diabetes

Smartphone-based diabetic macula edema screening with an offline artificial intelligence.

BACKGROUND: Diabetic macular edema (DME) is a sight-threatening condition that needs regular examinations and remedies. Optical coherence tomography (OCT) is the most common used examination to evaluate the structure and thickness of the macula, but the software in the OCT machine does not tell the clinicians whether DME exists directly. Recently, artificial intelligence (AI) is expected to aid in...

[Determination of -nitrosodimethylamine in metformin hydrochloride and its preparations by high performance liquid chromatography-tandem mass spectrometry].

A method was established for the determination of -nitrosodimethylamine (NDMA) in metformin hydrochloride active pharmaceutical ingredient (API) and preparation samples by high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Water was used as the extraction solvent for the metformin hydrochloride API and preparation samples. The samples were analyzed by HPLC-MS/MS after vo...

[High-efficiency separation and analysis of monosaccharides in Pueraria polysaccharides by pressurized capillary electrochromatography].

Pueraria polysaccharides have been proven to possess biological activities such as bacteriostasis, anti-oxidative, anti-tumor, and immunity boosting a...

Optical coherence tomography-based diabetic macula edema screening with artificial intelligence.

BACKGROUND: Optical coherence tomography (OCT) is considered as a sensitive and noninvasive tool to evaluate the macular lesions. In patients with dia...

A novelty route for smartphone-based artificial intelligence approach to ophthalmic screening.

Artificial intelligence (AI) has been widely applied in the medical field and achieved enormous milestones in helping specialists to make diagnosis an...

Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases.

IMPORTANCE: Recent studies have demonstrated the successful application of artificial intelligence (AI) for automated retinal disease diagnostics but ...

Cost-effectiveness of Autonomous Point-of-Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes.

IMPORTANCE: Screening for diabetic retinopathy is recommended for children with type 1 diabetes (T1D) and type 2 diabetes (T2D), yet screening rates r...

Identification of Diabetes Risk Factors in Chronic Cardiovascular Patients.

Specific predictive models for diabetes polyneuropathy based on screening methods, for example Nerve conduction studies (NCS, can reach up to AUC 65.8...

Rosiglitazone-induced changes in the oxidative stress metabolism and fatty acid composition in relation with trace element status in the primary adipocytes.

BACKGROUND: Metabolic syndrome, obesity and type 2 diabetes are metabolic disorders characterized by the insulin resistance and the impairment in the ...

Gut Microbiota in T1DM-Onset Pediatric Patients: Machine-Learning Algorithms to Classify Microorganisms as Disease Linked.

AIMS: The purpose of this work is to find the gut microbial fingerprinting of pediatric patients with type 1 diabetes.

Artificial intelligence for retinopathy of prematurity.

PURPOSE OF REVIEW: In this article, we review the current state of artificial intelligence applications in retinopathy of prematurity (ROP) and provid...

Measuring Anatomical Outcomes of Anti-Vascular Endothelial Growth Factor Treatment of Diabetic Macular Edema With Artificial Intelligence-A Step Toward Individualized Medicine.

Quantification of Fluid Resolution and Visual Acuity Gain in Patients With Diabetic Macular Edema Using Deep Learning: A Post Hoc Analysis of a Randomized Clinical Trial.

IMPORTANCE: Large amounts of optical coherence tomographic (OCT) data of diabetic macular edema (DME) are acquired, but many morphologic features have...

Artificial intelligence for diabetic retinopathy screening, prediction and management.

PURPOSE OF REVIEW: Diabetic retinopathy is the most common specific complication of diabetes mellitus. Traditional care for patients with diabetes and...

Assessment and prediction of restless leg syndrome (RLS) in patients with diabetes mellitus type II through artificial intelligence (AI).

This study aimed to diagnose the incidence of restless leg syndrome (RLS) in patients with diabetes mellitus (DM) type-2, thorough artificial intellig...

Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance.

PURPOSE: Glucose intolerance (GI), defined as either prediabetes or diabetes, promotes cardiovascular events in patients with myocardial infarction (M...

Workforce Shortage for Retinopathy of Prematurity Care and Emerging Role of Telehealth and Artificial Intelligence.

Retinopathy of prematurity (ROP) is the leading cause of childhood blindness in very-low-birthweight and very preterm infants in the United States. Wi...

Predicting complications of diabetes mellitus using advanced machine learning algorithms.

OBJECTIVE: We sought to predict if patients with type 2 diabetes mellitus (DM2) would develop 10 selected complications. Accurate prediction of compli...

A Multi-Label Deep Learning Model with Interpretable Grad-CAM for Diabetic Retinopathy Classification.

The characteristics of diabetic retinopathy (DR) fundus images generally consist of multiple types of lesions which provided strong evidence for the o...

Automatic Extraction of Risk Factors for Dialysis Patients from Clinical Notes Using Natural Language Processing Techniques.

Studies have shown that mental health and comorbidities such as dementia, diabetes and cardiovascular diseases are risk factors for dialysis patients....