AIMC Topic: Diagnostic Techniques, Ophthalmological

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A Retrospective Comparison of Deep Learning to Manual Annotations for Optic Disc and Optic Cup Segmentation in Fundus Photographs.

Translational vision science & technology Jun 24, 2020
PURPOSE: Optic disc (OD) and optic cup (OC) segmentation are fundamental for fundus image analysis. Manual annotation is time consuming, expensive, and highly subjective, whereas an automated system is invaluable to the medical community. The aim of ...
Deep Learning Fundus Oculi Optic Disk Diagnostic Techniques, Ophthalmological Humans Retrospective Studies
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CycleGAN-based deep learning technique for artifact reduction in fundus photography.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie May 2, 2020
PURPOSE: A low quality of fundus photograph with artifacts may lead to false diagnosis. Recently, a cycle-consistent generative adversarial network (CycleGAN) has been introduced as a tool to generate images without matching paired images. Therefore,...
Image Processing, Computer-Assisted Fundus Oculi Retina Artifacts Humans Algorithms Deep Learning Diagnostic Techniques, Ophthalmological Photography
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Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study.

The Lancet. Digital health Apr 23, 2020
BACKGROUND: Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopathy from fundus photographs. We used a cost-minimisation analysis to evaluate the potential savings ...
Photography Physical Examination Singapore Models, Biological Retina Telemedicine Cost-Benefit Analysis Diagnostic Techniques, Ophthalmological Health Care Costs Diabetes Mellitus Mass Screening Decision Trees Ophthalmology Sensitivity and Specificity Humans Middle Aged Diabetic Retinopathy Image Processing, Computer-Assisted Artificial Intelligence Machine Learning Aged Adult
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Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectures.

Scientific reports Mar 19, 2020
Computer vision has greatly advanced recently. Since AlexNet was first introduced, many modified deep learning architectures have been developed and they are still evolving. However, there are few studies comparing these architectures in the field of...
Humans Fundus Oculi Male Glaucoma Diagnostic Techniques, Ophthalmological Middle Aged Retrospective Studies Algorithms Deep Learning Female Optic Nerve Aged
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Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology.

Translational vision science & technology Feb 27, 2020
Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive amounts of clinical data. In ophthalmology in particular, the volume range of data captured in EHR systems has been growing rapidly. Yet making effectiv...
Electronic Health Records Natural Language Processing Ophthalmology Diagnostic Techniques, Ophthalmological Humans Artificial Intelligence
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Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model.

Scientific reports Feb 24, 2020
We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs). A total of 789 pairs of RNFLPs and sp...
Glaucoma Algorithms Retrospective Studies Female Humans Adult Sensitivity and Specificity Intraocular Pressure Male Aged Aged, 80 and over Young Adult Optic Nerve Tomography, Optical Coherence Fundus Oculi Translational Research, Biomedical Retinal Ganglion Cells Diagnostic Techniques, Ophthalmological Refractometry Deep Learning ROC Curve Support Vector Machine Regression Analysis Middle Aged Case-Control Studies Diagnosis, Computer-Assisted
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Automatic detection of rare pathologies in fundus photographs using few-shot learning.

Medical image analysis Jan 28, 2020
In the last decades, large datasets of fundus photographs have been collected in diabetic retinopathy (DR) screening networks. Through deep learning, these datasets were used to train automatic detectors for DR and a few other frequent pathologies, w...
Humans Eye Diseases Diabetic Retinopathy Diagnostic Techniques, Ophthalmological Deep Learning Photography Datasets as Topic Rare Diseases
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Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Jan 27, 2020
PURPOSE: To develop a deep learning approach based on deep residual neural network (ResNet101) for the automated detection of glaucomatous optic neuropathy (GON) using color fundus images, understand the process by which the model makes predictions, ...
Retrospective Studies Optic Nerve Diseases Intraocular Pressure Diagnostic Techniques, Ophthalmological Deep Learning ROC Curve Tomography, Optical Coherence Humans Optic Disk Neural Networks, Computer Retinal Ganglion Cells Glaucoma
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Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis.

Computer methods and programs in biomedicine Jan 16, 2020
BACKGROUND: Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Earlier detection and timely treatment of DR are desirable to reduce the incidence and progression of vision loss. Currently, deep learning (DL) approaches have...
Diabetic Retinopathy Humans Algorithms Deep Learning Mass Screening Diagnostic Techniques, Ophthalmological Retinal Vessels
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[Artificial Intelligence for the Development of Screening Parameters in the Field of Corneal Biomechanics].

Klinische Monatsblatter fur Augenheilkunde Dec 5, 2019
Machine learning and artificial intelligence are mostly important if data analysis by knowledge-based analytical methods is difficult and complex. In such cases, combined analytical and empirical approaches based on AI are also meaningful. The develo...
Mass Screening Humans Keratoconus Artificial Intelligence Diagnostic Techniques, Ophthalmological Cornea
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