AIMC Topic: Photography

Clear Filters Showing 221 to 230 of 337 articles

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs.

American journal of ophthalmology Jan 26, 2019
PURPOSE: To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT)...
Male Middle Aged Deep Learning Vision Disorders Area Under Curve Aged Aged, 80 and over Cross-Sectional Studies Intraocular Pressure Humans Visual Field Tests Algorithms Optic Nerve Diseases Retrospective Studies Photography Tomography, Optical Coherence Female Glaucoma, Open-Angle Nerve Fibers Bruch Membrane Optic Disk Retinal Ganglion Cells Visual Fields
View on PubMed DOI

From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs.

Ophthalmology Dec 20, 2018
PURPOSE: Previous approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative s...
Male Middle Aged Deep Learning ROC Curve Visual Acuity Aged Aged, 80 and over Cross-Sectional Studies Intraocular Pressure Visual Fields Visual Field Tests Optic Nerve Diseases Photography Glaucoma, Open-Angle Tomography, Optical Coherence Fundus Oculi Nerve Fibers Optic Disk Retinal Ganglion Cells Humans Algorithms Female Tonometry, Ocular Gonioscopy
View on PubMed DOI

Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy.

Ophthalmology Dec 13, 2018
PURPOSE: To understand the impact of deep learning diabetic retinopathy (DR) algorithms on physician readers in computer-assisted settings.
Algorithms Diagnosis, Computer-Assisted Humans Female Deep Learning Diabetic Retinopathy Ophthalmologists Reproducibility of Results ROC Curve Sensitivity and Specificity Reference Standards Male Photography
View on PubMed DOI

DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs.

Ophthalmology Nov 22, 2018
PURPOSE: In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming partic...
Prospective Studies Female Reproducibility of Results Risk Factors Aged Aged, 80 and over Sensitivity and Specificity Male Middle Aged Geographic Atrophy Humans Models, Theoretical Disease Progression Area Under Curve Diagnostic Techniques, Ophthalmological Severity of Illness Index Photography Diagnosis, Computer-Assisted Retinal Drusen Deep Learning
View on PubMed DOI

Development of an artificial intelligence system to classify pathology and clinical features on retinal fundus images.

Clinical & experimental ophthalmology Nov 15, 2018
IMPORTANCE: Artificial intelligence (AI) algorithms are under development for use in diabetic retinopathy photo screening pathways. To be clinically acceptable, such systems must also be able to classify other fundus abnormalities and clinical featur...
Area Under Curve Fundus Oculi Retina Macular Edema Databases, Factual Macular Degeneration Retrospective Studies Photography Reproducibility of Results Humans Sensitivity and Specificity Diabetic Retinopathy ROC Curve Glaucoma Artificial Intelligence
View on PubMed DOI

Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy.

Eye (London, England) Nov 6, 2018
Remarkable advances in biomedical research have led to the generation of large amounts of data. Using artificial intelligence, it has become possible to extract meaningful information from large volumes of data, in a shorter frame of time, with very ...
Humans Diabetic Retinopathy Neural Networks, Computer Algorithms Deep Learning ROC Curve Fundus Oculi Diagnostic Techniques, Ophthalmological Photography
View on PubMed DOI

The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment.

Medical & biological engineering & computing Oct 22, 2018
Recently, researchers have built new deep learning (DL) models using a single image modality to diagnose age-related macular degeneration (AMD). Retinal fundus and optical coherence tomography (OCT) images in clinical settings are the most important ...
Reproducibility of Results Diagnosis, Computer-Assisted Deep Learning Image Processing, Computer-Assisted Area Under Curve Humans Neural Networks, Computer Algorithms Databases, Factual Tomography, Optical Coherence Fundus Oculi Macular Degeneration Photography
View on PubMed DOI

Development of a deep residual learning algorithm to screen for glaucoma from fundus photography.

Scientific reports Oct 2, 2018
The Purpose of the study was to develop a deep residual learning algorithm to screen for glaucoma from fundus photography and measure its diagnostic performance compared to Residents in Ophthalmology. A training dataset consisted of 1,364 color fundu...
Image Interpretation, Computer-Assisted Datasets as Topic Ophthalmoscopy Photography Middle Aged Deep Learning ROC Curve Fundus Oculi Glaucoma Female Humans Aged Adult Male
View on PubMed DOI

Multimodal skin lesion classification using deep learning.

Experimental dermatology Sep 27, 2018
While convolutional neural networks (CNNs) have successfully been applied for skin lesion classification, previous studies have generally considered only a single clinical/macroscopic image and output a binary decision. In this work, we have presente...
Humans Skin Diseases Dermoscopy Metadata Deep Learning Photography Multimodal Imaging
View on PubMed DOI

Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images.

Physics in medicine and biology Sep 13, 2018
Deep learning has emerged as a powerful artificial intelligence tool to interpret medical images for a growing variety of applications. However, the paucity of medical imaging data with high-quality annotations that is necessary for training such met...
Tomography, X-Ray Computed Humans Endoscopy Swine Neural Networks, Computer Animals Deep Learning Colon Light Image Interpretation, Computer-Assisted Photography
View on PubMed DOI
Popular Topics
Recent Journals