Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs

Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs

Purpose: Significant visual impairment due to glaucoma is largely caused by the disease being detected too late. Objective: To build a labeled data set for training artificial intelligence (AI) algorithms for glaucoma screening by fundus photography, to assess the accuracy of the graders, and to cha...

Full description

Bibliographic Details
Main Authors:	Hans G. Lemij, MD, PhD, Coen de Vente, MSc, Clara I. Sánchez, PhD, Koen A. Vermeer, PhD
Format:	Article
Language:	English
Published:	Elsevier 2023-09-01
Series:	Ophthalmology Science
Subjects:	Artificial intelligence Clinical features color fundus photographs glaucoma screening labeled data set
Online Access:	http://www.sciencedirect.com/science/article/pii/S2666914523000325

Similar Items

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions
by: Rui Fan, PhD, et al.
Published: (2023-03-01)

Artificial intelligence in glaucoma detection using color fundus photographs
by: Zubin Sidhu, et al.
Published: (2024-01-01)

Primary Open-Angle Glaucoma Diagnosis from Optic Disc Photographs Using a Siamese Network
by: Mingquan Lin, PhD, et al.
Published: (2022-12-01)

Artificial Intelligence to Differentiate Pediatric Pseudopapilledema and True Papilledema on Fundus Photographs
by: Melinda Y. Chang, MD, et al.
Published: (2024-07-01)

Classification of Color Fundus Photographs Using Fusion Extracted Features and Customized CNN Models
by: Jing-Zhe Wang, et al.
Published: (2023-08-01)

Application of a deep learning system in glaucoma screening and further classification with colour fundus photographs: a case control study
by: Kuo-Hsuan Hung, et al.
Published: (2022-12-01)

Deep Learning-Based Glaucoma Screening Using Regional RNFL Thickness in Fundus Photography
by: Hyunmo Yang, et al.
Published: (2022-11-01)

Color fundus photograph registration based on feature and intensity for longitudinal evaluation of diabetic retinopathy progression
by: Jingxin Zhou, et al.
Published: (2022-09-01)

Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs?
by: Sangeeta Biswas, et al.
Published: (2022-06-01)

Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets
by: Yu-Chieh Ko, et al.
Published: (2022-06-01)

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs
by: Yu-Ke Ji, et al.
Published: (2024-01-01)

COMPUTER AIDED DIAGNOSIS OF GLAUCOMA DETECTION USING DIGITAL FUNDUS IMAGE
by: Ganeshbabu T R
Published: (2015-12-01)

Comparative Evaluation of Fundus Image Interpretation Accuracy in Glaucoma Screening Among Different Physician Groups [Letter]
by: Sadeghi M, et al.
Published: (2024-03-01)

CNNs for automatic glaucoma assessment using fundus images: an extensive validation
by: Andres Diaz-Pinto, et al.
Published: (2019-03-01)

The application of artificial intelligence in glaucoma diagnosis and prediction
by: Linyu Zhang, et al.
Published: (2023-05-01)

Comparative Evaluation of Fundus Image Interpretation Accuracy in Glaucoma Screening Among Different Physician Groups
by: Wada-Koike C, et al.
Published: (2024-02-01)

Multi-Dataset Comparison of Vision Transformers and Convolutional Neural Networks for Detecting Glaucomatous Optic Neuropathy from Fundus Photographs
by: Elizabeth E. Hwang, et al.
Published: (2023-10-01)

Multi-label classification of fundus images based on graph convolutional network
by: Yinlin Cheng, et al.
Published: (2021-07-01)

Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images
by: Pachiyappan Arulmozhivarman, et al.
Published: (2012-06-01)

A Study of the Interpretability of Fundus Analysis with Deep Learning-Based Approaches for Glaucoma Assessment
by: Jing-Ming Guo, et al.
Published: (2023-04-01)

Deep Learning Models for Segmenting Non-perfusion Area of Color Fundus Photographs in Patients With Branch Retinal Vein Occlusion
by: Jinxin Miao, et al.
Published: (2022-06-01)

Automated detection of myopic maculopathy from color fundus photographs using deep convolutional neural networks
by: Jun Li, et al.
Published: (2022-04-01)

Glaucoma Incidence and Progression in Diabetics: The Canary Islands Study Using the Laguna ONhE Application
by: Marta Gonzalez-Hernandez, et al.
Published: (2022-12-01)

Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images
by: Telmo Baptista, et al.
Published: (2023-10-01)

Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fundus photographs
by: Kaiqun Liu, et al.
Published: (2023-06-01)

The effect of photographic light brightness on cup to disc ratio grading
by: Matthew J. McSoley, et al.
Published: (2021-12-01)

Detecting glaucoma from multi-modal data using probabilistic deep learning
by: Xiaoqin Huang, et al.
Published: (2022-09-01)

Predicting Optical Coherence Tomography-Derived High Myopia Grades From Fundus Photographs Using Deep Learning
by: Zhenquan Wu, et al.
Published: (2022-03-01)

Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey
by: M. Caroline Viola Stella Mary, et al.
Published: (2016-01-01)

Early Glaucoma Detection by Using Style Transfer to Predict Retinal Nerve Fiber Layer Thickness Distribution on the Fundus Photograph
by: Henry Shen-Lih Chen, MD, MBA, et al.
Published: (2022-09-01)

Prediction of White Matter Hyperintensity in Brain MRI Using Fundus Photographs via Deep Learning
by: Bum-Joo Cho, et al.
Published: (2022-06-01)

Diabetic retinopathy detection using principal component analysis multi-label feature extraction and classification
by: Tiwalade Modupe Usman, et al.
Published: (2023-06-01)

Multi-Label Classification of Fundus Images With EfficientNet
by: Jing Wang, et al.
Published: (2020-01-01)

A Neural Network for Automated Image Quality Assessment of Optic Disc Photographs
by: Ella Bouris, et al.
Published: (2023-02-01)

An efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigm
by: Subbarayudu Yerragudipadu, et al.
Published: (2024-01-01)

The use of artificial intelligence in detecting papilledema from fundus photographs
by: Lazuardiah Anandi, et al.
Published: (2023-01-01)

Automatic Determination of Vertical Cup-to-Disc Ratio in Retinal Fundus Images for Glaucoma Screening
by: Jiapan Guo, et al.
Published: (2019-01-01)

Detection of glaucoma using three-stage training with EfficientNet
by: I. de Zarzà, et al.
Published: (2022-11-01)

Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images
by: Silvia Alayón, et al.
Published: (2023-11-01)

Detection of Glaucoma on Fundus Images Using Deep Learning on a New Image Set Obtained with a Smartphone and Handheld Ophthalmoscope
by: Clerimar Paulo Bragança, et al.
Published: (2022-11-01)