AIMC Topic: Fundus Oculi
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IMAGE EVALUATION OF ARTIFICIAL INTELLIGENCE-SUPPORTED OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IMAGING USING OCT-A1 DEVICE IN DIABETIC RETINOPATHY.
Retina (Philadelphia, Pa.)
Aug 1, 2021
PURPOSE: To investigate the effect of denoise processing by artificial intelligence (AI) on the optical coherence tomography angiography (OCTA) images in eyes with retinal lesions.
Multicolor image classification using the multimodal information bottleneck network (MMIB-Net) for detecting diabetic retinopathy.
Optics express
Jul 5, 2021
Multicolor (MC) imaging is an imaging modality that records confocal scanning laser ophthalmoscope (cSLO) fundus images, which can be used for the diabetic retinopathy (DR) detection. By utilizing this imaging technique, multiple modal images can be ...
Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration.
Journal of the American Medical Informatics Association : JAMIA
Jun 12, 2021
OBJECTIVE: Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and typically require advanced imaging modalities such as fundus autoflu...
Rapid classification of glaucomatous fundus images.
Journal of the Optical Society of America. A, Optics, image science, and vision
Jun 1, 2021
We propose a new method for training convolutional neural networks (CNNs) and use it to classify glaucoma from fundus images. This method integrates reinforcement learning along with supervised learning and uses it for transfer learning. The training...
Detection of glaucoma using retinal fundus images: A comprehensive review.
Mathematical biosciences and engineering : MBE
Mar 2, 2021
Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glau...
Addressing Artificial Intelligence Bias in Retinal Diagnostics.
Translational vision science & technology
Feb 5, 2021
PURPOSE: This study evaluated generative methods to potentially mitigate artificial intelligence (AI) bias when diagnosing diabetic retinopathy (DR) resulting from training data imbalance or domain generalization, which occurs when deep learning syst...
Screening and identifying hepatobiliary diseases through deep learning using ocular images: a prospective, multicentre study.
The Lancet. Digital health
Feb 1, 2021
BACKGROUND: Ocular changes are traditionally associated with only a few hepatobiliary diseases. These changes are non-specific and have a low detection rate, limiting their potential use as clinically independent diagnostic features. Therefore, we ai...
Re: Phene et al.: Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs (Ophthalmology. 2019;126:1627-1639).
Ophthalmology
Aug 1, 2020
ASSESSMENT OF CENTRAL SEROUS CHORIORETINOPATHY DEPICTED ON COLOR FUNDUS PHOTOGRAPHS USING DEEP LEARNING.
Retina (Philadelphia, Pa.)
Aug 1, 2020
PURPOSE: To investigate whether and to what extent central serous chorioretinopathy (CSC) depicted on color fundus photographs can be assessed using deep learning technology.
A Multi-Label Deep Learning Model with Interpretable Grad-CAM for Diabetic Retinopathy Classification.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2020
The characteristics of diabetic retinopathy (DR) fundus images generally consist of multiple types of lesions which provided strong evidence for the ophthalmologists to make diagnosis. It is particularly significant to figure out an efficient method ...
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