Diabetes

Food Liking-Based Diet Quality Indexes (DQI) Generated by Conceptual and Machine Learning Explained Variability in Cardiometabolic Risk Factors in Young Adults.

The overall pattern of a diet (diet quality) is recognized as more important to health and chronic disease risk than single foods or food groups. Indexes of diet quality can be derived theoretically from evidence-based recommendations, empirically from existing datasets, or a combination of the two. We used these methods to derive diet quality indexes (DQI), generated from a novel dietary assessme...

Deep Learning Classification for Diabetic Foot Thermograms.

According to the World Health Organization (WHO), Diabetes Mellitus (DM) is one of the most prevalent diseases in the world. It is also associated with a high mortality index. Diabetic foot is one of its main complications, and it comprises the development of plantar ulcers that could result in an amputation. Several works report that thermography is useful to detect changes in the plantar tempera...

Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAP).

The rising prevalence and global burden of diabetes fortify the need for more comprehensive and effective management to prevent, monitor, and treat di...

Diabetic retinopathy and ultrawide field imaging.

The introduction of ultrawide field imaging has allowed the visualization of approximately 82% of the total retinal area compared to only 30% using 7-...

Simultaneous ultra-trace quantitative colorimetric determination of antidiabetic drugs based on gold nanoparticles aggregation using multivariate calibration and neural network methods.

In this study, a simple and rapid method was investigated for the simultaneous ultra-trace colorimetric determination of Metformin (MET) and Sitaglipt...

Lessons Learned About Autonomous AI: Finding a Safe, Efficacious, and Ethical Path Through the Development Process.

Artificial intelligence (AI) describes systems capable of making decisions of high cognitive complexity; autonomous AI systems in healthcare are AI sy...

Automated detection and classification of diabetes disease based on Bangladesh demographic and health survey data, 2011 using machine learning approach.

BACKGROUND AND AIMS: Diabetes has been recognized as a continuing health challenge for the twenty-first century, both in developed and developing coun...

A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography.

Conventional glucose monitoring methods for the growing numbers of diabetic patients around the world are invasive, painful, costly and, time-consumin...

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms.

Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately ...

NFN＋: A novel network followed network for retinal vessel segmentation.

In the early diagnosis of diabetic retinopathy, the morphological attributes of blood vessels play an essential role to construct a retinal computer-a...

Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.

Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, which commonly goes undetected. Continuous glucose monitoring (CGM) devi...

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology.

Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive amounts of clinical data. In ophthalmology in partic...

Glucose outcomes of a learning-type artificial pancreas with an unannounced meal in type 1 diabetes.

BACKGROUND AND OBJECTIVES: Glycemic control with unannounced meals is the major challenge for artificial pancreas. In this study, we described the per...

Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier.

BACKGROUND/AIMS: To assess the performance of a deep learning classifier for differentiation of glaucomatous optic neuropathy (GON) from compressive o...

A health informatics transformation model based on intelligent cloud computing - exemplified by type 2 diabetes mellitus with related cardiovascular diseases.

BACKGROUND AND OBJECTIVE: Many studies regarding health analysis request structured datasets but the legacy resources provide scattered data. This stu...

Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches.

Glucose-6-Phosphate Dehydrogenase (G6PD) is a ubiquitous cytoplasmic enzyme converting glucose-6-phosphate into 6-phosphogluconate in the pentose phos...

A proposed health monitoring system using fuzzy inference system.

Due to the busy schedule of every human being in today's world, consciousness towards one's health has become quite alarming. A person suffering from ...

Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening.

BACKGROUND AND OBJECTIVE: Diabetic retinopathy (DR), which is generally diagnosed by the presence of hemorrhages and hard exudates, is one of the most...

Plus Disease in Retinopathy of Prematurity: Convolutional Neural Network Performance Using a Combined Neural Network and Feature Extraction Approach.

PURPOSE: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading re...

Artificial Intelligence in Retinopathy of Prematurity Diagnosis.

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The diagnosis of ROP is subclassified by zone, stage, and plus d...