Diabetes

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 screeni...

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 dete...

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...

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 mo...

Updates in deep learning research in ophthalmology.

Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical ...

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 d...

Machine learning for initial insulin estimation in hospitalized patients.

OBJECTIVE: The study sought to determine whether machine learning can predict initial inpatient tota...

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 retinopa...

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 opti...

Multicolor image classification using the multimodal information bottleneck network (MMIB-Net) for detecting diabetic retinopathy.

Multicolor (MC) imaging is an imaging modality that records confocal scanning laser ophthalmoscope (...

Reducing Blindness Resulting from Retinopathy of Prematurity Using Deep Learning.

[Robot-assisted single-anastomosis duodeno-ileal bypass with sleeve gastrectomy].

Single-anastomosis duodeno-ileal bypass with sleeve gastrectomy (SADI-S) is simpler and has similar ...

DETECTION OF MORPHOLOGIC PATTERNS OF DIABETIC MACULAR EDEMA USING A DEEP LEARNING APPROACH BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.

PURPOSE: To develop a deep learning (DL) model to detect morphologic patterns of diabetic macular ed...

Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease.

Understanding the mechanics of blood flow is necessary for developing insights into mechanisms of ph...

Verification and comparison of three prediction models of ischemic stroke in young adults based on the back propagation neural networks.

This work aims to explore risk factors for ischemic stroke in young adults and analyze the Tradition...

Early Prediction of Gestational Diabetes Mellitus in the Chinese Population via Advanced Machine Learning.

CONTEXT: Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester ...

Applications of Artificial Intelligence for Retinopathy of Prematurity Screening.

OBJECTIVES: Childhood blindness from retinopathy of prematurity (ROP) is increasing as a result of i...

Addressing Artificial Intelligence Bias in Retinal Diagnostics.

PURPOSE: This study evaluated generative methods to potentially mitigate artificial intelligence (AI...

Deep Learning Detection of Sea Fan Neovascularization From Ultra-Widefield Color Fundus Photographs of Patients With Sickle Cell Hemoglobinopathy.

IMPORTANCE: Adherence to screening for vision-threatening proliferative sickle cell retinopathy is l...

Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space.

It is well known that the development of drug resistance in cancer cells can lead to changes in cell...

Clinical Presentation, Histology, and Outcomes of Immunoglobulin A Nephropathy: A Single-Center Experience from Pakistan.

Clinical presentation, kidney biopsy findings, and clinical outcomes of immunoglobulin A nephropathy...