AIMC Topic: Fundus Oculi
Clear Filters Showing 431 to 440 of 491 articles
DETECTION AND LOCALIZATION OF RETINAL BREAKS IN ULTRAWIDEFIELD FUNDUS PHOTOGRAPHY USING a YOLO v3 ARCHITECTURE-BASED DEEP LEARNING MODEL.
Retina (Philadelphia, Pa.)
Oct 1, 2022
PURPOSE: We aimed to develop a deep learning model for detecting and localizing retinal breaks in ultrawidefield fundus (UWF) images.
A Multicenter Clinical Study of the Automated Fundus Screening Algorithm.
Translational vision science & technology
Jul 8, 2022
PURPOSE: To evaluate the effectiveness of automated fundus screening software in detecting eye diseases by comparing the reported results against those given by human experts.
AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline.
Translational vision science & technology
Jul 8, 2022
PURPOSE: To externally validate a deep learning pipeline (AutoMorph) for automated analysis of retinal vascular morphology on fundus photographs. AutoMorph has been made publicly available, facilitating widespread research in ophthalmic and systemic ...
Multi-Expert Deep Networks for Multi-Disease Detection in Retinal Fundus Images.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2022
Automatic diagnosis of eye diseases from retinal fundus images is quite challenging. Common public datasets include images of subjects with multiple diseases with uneven distribution of labels. Rare diseases are especially challenging due to their un...
The Use of Datasets of Bad Quality Images to Define Fundus Image Quality.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2022
Screening programs for sight-threatening diseases rely on the grading of a large number of digital retinal images. As automatic image grading technology evolves, there emerges a need to provide a rigorous definition of image quality with reference to...
Investigation of the Role of Convolutional Neural Network Architectures in the Diagnosis of Glaucoma using Color Fundus Photography.
Turkish journal of ophthalmology
Jun 29, 2022
OBJECTIVES: To evaluate the performance of convolutional neural network (CNN) architectures to distinguish eyes with glaucoma from normal eyes.
A deep-learning system predicts glaucoma incidence and progression using retinal photographs.
The Journal of clinical investigation
Jun 1, 2022
BackgroundDeep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for pred...
Utilizing human intelligence in artificial intelligence for detecting glaucomatous fundus images using human-in-the-loop machine learning.
Indian journal of ophthalmology
Apr 1, 2022
PURPOSE: For diagnosing glaucomatous damage, we have employed a novel convolutional neural network (CNN) from TrueColor confocal fundus images to conquer the black box dilemma in artificial intelligence (AI). This neural network with CNN architecture...
Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning.
JAMA ophthalmology
Feb 1, 2022
IMPORTANCE: Anomaly detectors could be pursued for retinal diagnoses based on artificial intelligence systems that may not have access to training examples for all retinal diseases in all phenotypic presentations. Possible applications could include ...
Peripapillary Atrophy Segmentation and Classification Methodologies for Glaucoma Image Detection: A Review.
Current medical imaging
Jan 1, 2022
Information-based image processing and computer vision methods are utilized in several healthcare organizations to diagnose diseases. The irregularities in the visual system are identified over fundus images with a fundus camera. Among ophthalmology ...
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