AIMC Topic: Diagnostic Techniques, Ophthalmological
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Deep Learning-based Prediction of Axial Length Using Ultra-widefield Fundus Photography.
Korean journal of ophthalmology : KJO
Feb 9, 2023
PURPOSE: To develop a deep learning model that can predict the axial lengths of eyes using ultra-widefield (UWF) fundus photography.
Combining convolutional neural networks and self-attention for fundus diseases identification.
Scientific reports
Jan 2, 2023
Early detection of lesions is of great significance for treating fundus diseases. Fundus photography is an effective and convenient screening technique by which common fundus diseases can be detected. In this study, we use color fundus images to dist...
A multi-feature deep learning system to enhance glaucoma severity diagnosis with high accuracy and fast speed.
Journal of biomedical informatics
Oct 21, 2022
Glaucoma is the leading cause of irreversible blindness, and the early detection and timely treatment are essential for glaucoma management. However, due to the interindividual variability in the characteristics of glaucoma onset, a single feature is...
Predicting demographics from meibography using deep learning.
Scientific reports
Sep 20, 2022
This study introduces a deep learning approach to predicting demographic features from meibography images. A total of 689 meibography images with corresponding subject demographic data were used to develop a deep learning model for predicting gland m...
BFENet: A two-stream interaction CNN method for multi-label ophthalmic diseases classification with bilateral fundus images.
Computer methods and programs in biomedicine
Mar 11, 2022
BACKGROUND AND OBJECTIVE: Early fundus screening and timely treatment of ophthalmology diseases can effectively prevent blindness. Previous studies just focus on fundus images of single eye without utilizing the useful relevant information of the lef...
A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs.
Computer methods and programs in biomedicine
Mar 6, 2022
BACKGROUND AND OBJECTIVES: Patients with angle-closure glaucoma (ACG) are asymptomatic until they experience a painful attack. Shallow anterior chamber depth (ACD) is considered a significant risk factor for ACG. We propose a deep learning approach t...
Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression.
Ophthalmology. Glaucoma
Feb 24, 2022
On September 3, 2020, the Collaborative Community on Ophthalmic Imaging conducted its first 2-day virtual workshop on the role of artificial intelligence (AI) and related machine learning techniques in the diagnosis and treatment of various ophthalmi...
Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device.
Sensors (Basel, Switzerland)
Feb 14, 2022
Glaucoma is a silent disease that leads to vision loss or irreversible blindness. Current deep learning methods can help glaucoma screening by extending it to larger populations using retinal images. Low-cost lenses attached to mobile devices can inc...
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...
Asymmetry between right and left fundus images identified using convolutional neural networks.
Scientific reports
Jan 27, 2022
We analyzed fundus images to identify whether convolutional neural networks (CNNs) can discriminate between right and left fundus images. We gathered 98,038 fundus photographs from the Gyeongsang National University Changwon Hospital, South Korea, an...
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