Glaucoma

Predicting Glaucoma Progression Requiring Surgery Using Clinical Free-Text Notes and Transfer Learning With Transformers.

PURPOSE: We evaluated the use of massive transformer-based language models to predict glaucoma progr...

Peripapillary Atrophy Segmentation and Classification Methodologies for Glaucoma Image Detection: A Review.

Information-based image processing and computer vision methods are utilized in several healthcare or...

AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning.

PURPOSE: Optic nerve damage is the principal feature of glaucoma and contributes to vision loss in m...

Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field.

PURPOSE: To investigate whether a correction based on a Humphrey field analyzer (HFA) 24-2/30-2 visu...

Updates in deep learning research in ophthalmology.

Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical ...

Identification of glaucoma from fundus images using deep learning techniques.

PURPOSE: Glaucoma is one of the preeminent causes of incurable visual disability and blindness acros...

Detection of Optic Disc Abnormalities in Color Fundus Photographs Using Deep Learning.

BACKGROUND: To date, deep learning-based detection of optic disc abnormalities in color fundus photo...

Deep Learning-based Diagnosis of Glaucoma Using Wide-field Optical Coherence Tomography Images.

PURPOSE: (1) To evaluate the performance of deep learning (DL) classifier in detecting glaucoma, bas...

Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest.

PURPOSE: To compare change over time in eye-specific optical coherence tomography (OCT) retinal nerv...

An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography.

Glaucoma is a chronic ocular degenerative disease that can cause blindness if left untreated in its ...

Rapid classification of glaucomatous fundus images.

We propose a new method for training convolutional neural networks (CNNs) and use it to classify gla...

Strategies to Improve Convolutional Neural Network Generalizability and Reference Standards for Glaucoma Detection From OCT Scans.

PURPOSE: To develop and evaluate methods to improve the generalizability of convolutional neural net...

Detection of glaucoma using retinal fundus images: A comprehensive review.

Content-based image analysis and computer vision techniques are used in various health-care systems ...

Artificial intelligence and complex statistical modeling in glaucoma diagnosis and management.

PURPOSE OF REVIEW: The field of artificial intelligence has grown exponentially in recent years with...

Use of Telepresence Robots in Glaucoma Patient Education.

PRCIS: Telepresence robots (TR) present the versatility to effectively provide remote educational se...

Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images.

We propose a conceptually simple framework for fast COVID-19 screening in 3D chest CT images. The fr...

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).

The potential application of artificial intelligence for diagnosis and management of glaucoma in adults.

BACKGROUND: Glaucoma is the most frequent cause of irreversible blindness worldwide. There is no cur...

Artificial intelligence in cornea, refractive, and cataract surgery.

PURPOSE OF REVIEW: The subject of artificial intelligence has recently been responsible for the adva...

Diagnosing Glaucoma With Spectral-Domain Optical Coherence Tomography Using Deep Learning Classifier.

UNLABELLED: PRéCIS:: A spectral-domain optical coherence tomography (SD-OCT) based deep learning sys...

Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans.

IMPORTANCE: Conventional segmentation of the retinal nerve fiber layer (RNFL) is prone to errors tha...