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Diagnostic Techniques, Ophthalmological

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Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression.

Ophthalmology. Glaucoma
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...

BFENet: A two-stream interaction CNN method for multi-label ophthalmic diseases classification with bilateral fundus images.

Computer methods and programs in biomedicine
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
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...

AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline.

Translational vision science & technology
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 ...

Predicting demographics from meibography using deep learning.

Scientific reports
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...

A multi-feature deep learning system to enhance glaucoma severity diagnosis with high accuracy and fast speed.

Journal of biomedical informatics
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...

Combining convolutional neural networks and self-attention for fundus diseases identification.

Scientific reports
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...

Deep Learning-based Prediction of Axial Length Using Ultra-widefield Fundus Photography.

Korean journal of ophthalmology : KJO
PURPOSE: To develop a deep learning model that can predict the axial lengths of eyes using ultra-widefield (UWF) fundus photography.

Sex determination using color fundus parameters in older adults of Kumejima population study.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: Deep learning artificial intelligence can determine the sex using only fundus photographs. However, the factors used by deep learning to determine the sex are not visible. Therefore, the purpose of the study was to determine whether the sex ...

A nystagmus extraction system using artificial intelligence for video-nystagmography.

Scientific reports
Benign paroxysmal positional vertigo (BPPV), the most common vestibular disorder, is diagnosed by an examiner changing the posture of the examinee and inducing nystagmus. Among the diagnostic methods used to observe nystagmus, video-nystagmography ha...