AIMC Topic: Ophthalmologists
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Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images.
Scientific reports
Feb 9, 2022
Neovascular age-related macular degeneration (nAMD) is among the main causes of visual impairment worldwide. We built a deep learning model to distinguish the subtypes of nAMD using spectral domain optical coherence tomography (SD-OCT) images. Data f...
Necessity of Local Modification for Deep Learning Algorithms to Predict Diabetic Retinopathy.
International journal of environmental research and public health
Jan 21, 2022
Deep learning (DL) algorithms are used to diagnose diabetic retinopathy (DR). However, most of these algorithms have been trained using global data or data from patients of a single region. Using different model architectures (e.g., Inception-v3, Res...
Deep learning versus ophthalmologists for screening for glaucoma on fundus examination: A systematic review and meta-analysis.
Clinical & experimental ophthalmology
Sep 22, 2021
BACKGROUND: In this systematic review and meta-analysis, we aimed to compare deep learning versus ophthalmologists in glaucoma diagnosis on fundus examinations.
A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images.
PloS one
Jun 3, 2021
PURPOSE: Infiltration of activated dendritic cells and inflammatory cells in cornea represents an important marker for defining corneal inflammation. Deep transfer learning has presented a promising potential and is gaining more importance in compute...
Detection of oedema on optical coherence tomography images using deep learning model trained on noisy clinical data.
Acta ophthalmologica
May 15, 2021
PURPOSE: To meet the demands imposed by the continuing growth of the Age-related macular degeneration (AMD) patient population, automation of follow-ups by detecting retinal oedema using deep learning might be a viable approach. However, preparing an...
Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study.
American journal of ophthalmology
Feb 9, 2021
PURPOSE: To compare the performance of a novel convolutional neural network (CNN) classifier and human graders in detecting angle closure in EyeCam (Clarity Medical Systems, Pleasanton, California, USA) goniophotographs.
Glaucoma, Angle-Closure
Gonioscopy
Specialization
Asian
Photography
Anterior Eye Segment
Ophthalmologists
Cross-Sectional Studies
Male
Middle Aged
Diagnosis, Computer-Assisted
China
Image Processing, Computer-Assisted
Area Under Curve
Expert Systems
Female
Reproducibility of Results
Aged
Aged, 80 and over
Humans
Neural Networks, Computer
Retrospective Studies
Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data.
Ophthalmology
Dec 25, 2020
PURPOSE: Rule-based approaches to determining glaucoma progression from visual fields (VFs) alone are discordant and have tradeoffs. To detect better when glaucoma progression is occurring, we used a longitudinal data set of merged VF and clinical da...
How to Extract More Information With Less Burden: Fundus Image Classification and Retinal Disease Localization With Ophthalmologist Intervention.
IEEE journal of biomedical and health informatics
Dec 4, 2020
Image classification using convolutional neural networks (CNNs) outperforms other state-of-the-art methods. Moreover, attention can be visualized as a heatmap to improve the explainability of results of a CNN. We designed a framework that can generat...
Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience.
Scientific reports
Nov 11, 2020
We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists without coding experience using automated ML (AutoML). The study was a retrospective cohort study of 506...
Optic Disc Classification by Deep Learning versus Expert Neuro-Ophthalmologists.
Annals of neurology
Aug 7, 2020
OBJECTIVE: To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance.
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