Diabetes

A Deep Learning Algorithm for Classifying Diabetic Retinopathy Using Optical Coherence Tomography Angiography.

PURPOSE: To develop an automated diabetic retinopathy (DR) staging system using optical coherence tomography angiography (OCTA) images with a convolutional neural network (CNN) and to verify the feasibility of the system.

Identifying multi-functional bioactive peptide functions using multi-label deep learning.

The bioactive peptide has wide functions, such as lowering blood glucose levels and reducing inflammation. Meanwhile, computational methods such as machine learning are becoming more and more important for peptide functions prediction. Most of the previous studies concentrate on the single-functional bioactive peptides prediction. However, the number of multi-functional peptides is on the increase...

Contribution of Artificial Intelligence in Pregnancy: A Scoping Review.

For the past ten years, the healthcare sector and industry has witnessed a surge in Artificial Intelligence (AI) technologies being used in many diffe...

EVALUATION OF ARTIFICIAL INTELLIGENCE-BASED QUANTITATIVE ANALYSIS TO IDENTIFY CLINICALLY SIGNIFICANT SEVERE RETINOPATHY OF PREMATURITY.

PURPOSE: To evaluate the screening potential of a deep learning algorithm-derived severity score by determining its ability to detect clinically signi...

ANALYSIS OF TRANSFER LEARNING FOR SELECT RETINAL DISEASE CLASSIFICATION.

PURPOSE: To analyze the effect of transfer learning for classification of diabetic retinopathy (DR) by fundus photography and select retinal diseases ...

A MULTITASK DEEP-LEARNING SYSTEM FOR ASSESSMENT OF DIABETIC MACULAR ISCHEMIA ON OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IMAGES.

PURPOSE: We aimed to develop and test a deep-learning system to perform image quality and diabetic macular ischemia (DMI) assessment on optical cohere...

A Hybrid Protocol for Identifying Comorbidity-Based Potential Drugs for COVID-19 Using Biomedical Literature Mining, Network Analysis, and Deep Learning.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has spread on an unprecedented scale around ...

The Use of Machine Learning in MicroRNA Diagnostics: Current Perspectives.

MicroRNAs constitute small non-coding RNAs that play a pivotal role in regulating the translation and degradation of mRNA and have been associated wit...

Correlation among cystatin C, homocysteine and arteriosclerosis indexes in patients with chronic kidney disease.

OBJECTIVES: Chronic kidney disease (CKD) has become an important public health problem in the world. The occurrence of cardiovascular events is the ma...

Single-Examination Risk Prediction of Severe Retinopathy of Prematurity.

BACKGROUND AND OBJECTIVES: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Screening and treatment reduces this risk, but ...

Vitamin D and COVID-19 - Let's Explore the Relationship!

Vitamin D plays a protective role against COVID-19. Patients with deficiency of vitamin D are more prone to severe SARS-CoV-2 infections. It is known ...

In-Person Verification of Deep Learning Algorithm for Diabetic Retinopathy Screening Using Different Techniques Across Fundus Image Devices.

PURPOSE: To evaluate the clinical performance of an automated diabetic retinopathy (DR) screening model to detect referable cases at Siriraj Hospital,...

Semi-Supervised Segmentation of Renal Pathology: An Alternative to Manual Segmentation and Input to Deep Learning Training.

Kidney biopsy interpretation is the gold standard for the diagnosis and prognosis for kidney disease. Pathognomonic diagnosis hinges on the correct as...

An Efficient Deep Learning Network for Automatic Detection of Neovascularization in Color Fundus Images.

Retinopathy screening is a non-invasive method to collect retinal images and neovascularization detection from retinal images plays a significant role...

Improved Automatic Grading of Diabetic Retinopathy Using Deep Learning and Principal Component Analysis.

Diabetic retinopathy (DR) is one of the most common chronic diseases around the world. Early screening and diagnosis of DR patients through retinal fu...

Updates in deep learning research in ophthalmology.

Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garn...

Exploration of Machine Learning and Statistical Techniques in Development of a Low-Cost Screening Method Featuring the Global Diet Quality Score for Detecting Prediabetes in Rural India.

BACKGROUND: The prevalence of type 2 diabetes has increased substantially in India over the past 3 decades. Undiagnosed diabetes presents a public hea...

Machine learning for initial insulin estimation in hospitalized patients.

OBJECTIVE: The study sought to determine whether machine learning can predict initial inpatient total daily dose (TDD) of insulin from electronic heal...

Deep Learning for Automated Diabetic Retinopathy Screening Fused With Heterogeneous Data From EHRs Can Lead to Earlier Referral Decisions.

PURPOSE: Fundus images are typically used as the sole training input for automated diabetic retinopathy (DR) classification. In this study, we conside...

IMAGE EVALUATION OF ARTIFICIAL INTELLIGENCE-SUPPORTED OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IMAGING USING OCT-A1 DEVICE IN DIABETIC RETINOPATHY.

PURPOSE: To investigate the effect of denoise processing by artificial intelligence (AI) on the optical coherence tomography angiography (OCTA) images...