AIMC Topic: Retina

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Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients.

The British journal of ophthalmology Jun 30, 2020
BACKGROUND/AIMS: Human grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence...
Middle Aged Adolescent Child Image Processing, Computer-Assisted Mass Screening Aged, 80 and over Young Adult Adult Male Follow-Up Studies Retina Humans Diabetic Retinopathy Artificial Intelligence Algorithms Prospective Studies Retrospective Studies Female Reproducibility of Results Aged
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Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa.

Sensors (Basel, Switzerland) Jun 18, 2020
Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and hypertensive retinopathy. RP is a genetic retinal disorder that leads to progressive vision degenerat...
Fundus Oculi Retina Retinitis Pigmentosa Humans Deep Learning Tomography, Optical Coherence
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Diabetic Retinopathy Screening with Automated Retinal Image Analysis in a Primary Care Setting Improves Adherence to Ophthalmic Care.

Ophthalmology. Retina Jun 17, 2020
PURPOSE: Retinal screening examinations can prevent vision loss resulting from diabetes but are costly and highly underused. We hypothesized that artificial intelligence-assisted nonmydriatic point-of-care screening administered during primary care v...
Humans Diabetic Retinopathy Retina Artificial Intelligence Ambulatory Care Facilities Middle Aged Prospective Studies Primary Health Care Female Image Processing, Computer-Assisted Male Mass Screening Follow-Up Studies
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A Neural Network Approach to Quantify Blood Flow from Retinal OCT Intensity Time-Series Measurements.

Scientific reports Jun 15, 2020
Many diseases of the eye are associated with alterations in the retinal vasculature that are possibly preceded by undetected changes in blood flow. In this work, a robust blood flow quantification framework is presented based on optical coherence tom...
Humans Neural Networks, Computer Deep Learning Time Factors Tomography, Optical Coherence Retina Blood Flow Velocity Retinal Vessels
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An innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms.

Pharmacological research Jun 2, 2020
Current methods of evaluating the degree of diabetic retinopathy are highly subjective and have no quantitative standard. To objectively evaluate the slight changes in tissue structure during the early stage of retinal diseases, a subjective interpre...
Diabetes Mellitus, Type 2 Mice Rats, Wistar Diabetes Mellitus, Type 1 Hypoglycemic Agents Image Interpretation, Computer-Assisted Diabetic Retinopathy Early Diagnosis Predictive Value of Tests Animals Retinal Ganglion Cells Drugs, Chinese Herbal Male Pattern Recognition, Automated Diabetes Mellitus, Experimental Severity of Illness Index Retinal Vessels Retina Deep Learning Microscopy
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Deep learning architectures analysis for age-related macular degeneration segmentation on optical coherence tomography scans.

Computer methods and programs in biomedicine May 26, 2020
BACKGROUND AND OBJECTIVES: Aged people usually are more to be diagnosed with retinal diseases in developed countries. Retinal capillaries leakage into the retina swells and causes an acute vision loss, which is called age-related macular degeneration...
Deep Learning Tomography, Optical Coherence Retina Macular Degeneration Humans Neural Networks, Computer Aged
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CytoCensus, mapping cell identity and division in tissues and organs using machine learning.

eLife May 19, 2020
A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends conv...
Animals Male Larva Time Factors Image Processing, Computer-Assisted Mice Mutation Retina Zebrafish Embryo, Mammalian Machine Learning Phenotype Brain Female Reproducibility of Results Tissue Culture Techniques Animals, Genetically Modified Time-Lapse Imaging Automation Microscopy, Video Drosophila melanogaster Organoids Cell Division
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Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China.

PloS one May 14, 2020
Retinal fundus photography provides a non-invasive approach for identifying early microcirculatory alterations of chronic diseases prior to the onset of overt clinical complications. Here, we developed neural network models to predict hypertension, h...
Dyslipidemias Middle Aged Humans Aged, 80 and over Hyperglycemia Deep Learning Female Chronic Disease Young Adult China Fundus Oculi Risk Factors Adult ROC Curve Models, Biological Aged Cross-Sectional Studies Hypertension Retina Male
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Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis.

Nature communications May 11, 2020
Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding a...
Retina Gene Expression Regulation Macaca Islets of Langerhans Humans RNA-Seq Algorithms Mice Monocytes Animals Cluster Analysis Leukocytes, Mononuclear Bone Marrow Deep Learning Single-Cell Analysis
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Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.

The British journal of ophthalmology May 6, 2020
BACKGROUND: Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an A...
Slit Lamp Microscopy Diagnostic Imaging Retina Diabetic Retinopathy Diagnostic Tests, Routine Artificial Intelligence Humans Aged Middle Aged False Positive Reactions Algorithms Adult Reference Standards Female Image Processing, Computer-Assisted Sensitivity and Specificity Reproducibility of Results Microscopy, Confocal Cross-Sectional Studies Predictive Value of Tests Male
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