Glaucoma

Deep learning visual field global index prediction with optical coherence tomography parameters in glaucoma patients.

The aim of this study was to predict three visual filed (VF) global indexes, mean deviation (MD), pattern standard deviation (PSD), and visual field index (VFI), from optical coherence tomography (OCT) parameters including Bruch's Membrane Opening-Minimum Rim Width (BMO-MRW) and retinal nerve fiber layer (RNFL) based on a deep-learning model. Subjects consisted of 224 eyes with Glaucoma suspects (...

Deep Learning Application to Detect Glaucoma with a Mixed Training Approach: Public Database and Expert-Labeled Glaucoma Population.

INTRODUCTION: Artificial intelligence has real potential for early identification of ocular diseases such as glaucoma. An important challenge is the requirement for large databases properly selected, which are not easily obtained. We used a relatively original strategy: a glaucoma recognition algorithm trained with fundus images from public databases and then tested and retrained with a carefully ...

Autonomous assessment of spontaneous retinal venous pulsations in fundus videos using a deep learning framework.

The presence or absence of spontaneous retinal venous pulsations (SVP) provides clinically significant insight into the hemodynamic status of the opti...

The Relationship Between Artificial Intelligence-Assisted OCT Angiography-Derived Foveal Avascular Zone Parameters and Visual-Field Defect Progression in Eyes with Open-Angle Glaucoma.

PURPOSE: To investigate clinical factors associated with foveal avascular zone (FAZ) parameters obtained using OCT angiography (OCTA) with assistance ...

Smartphone Eye Examination: Artificial Intelligence and Telemedicine.

The current medical scenario is closely linked to recent progress in telecommunications, photodocumentation, and artificial intelligence (AI). Smartp...

A deep learning approach to investigate the filtration bleb functionality after glaucoma surgery: a preliminary study.

PURPOSE: To distinguish functioning from failed filtration blebs (FBs) implementing a deep learning (DL) model on slit-lamp images.

Early Detection of Optic Nerve Changes on Optical Coherence Tomography Using Deep Learning for Risk-Stratification of Papilledema and Glaucoma.

BACKGROUND: The use of artificial intelligence is becoming more prevalence in medicine with numerous successful examples in ophthalmology. However, mu...

Deep Learning for Inference of Hepatic Proton Density Fat Fraction From T1-Weighted In-Phase and Opposed-Phase MRI: Retrospective Analysis of Population-Based Trial Data.

The confounder-corrected chemical shift-encoded MRI (CSE-MRI) sequence used to determine proton density fat fraction (PDFF) for hepatic fat quantific...

Multimodal Deep Learning Classifier for Primary Open Angle Glaucoma Diagnosis Using Wide-Field Optic Nerve Head Cube Scans in Eyes With and Without High Myopia.

PRCIS: An optical coherence tomography (OCT)-based multimodal deep learning (DL) classification model, including texture information, is introduced th...

Unraveling the complexity of Optical Coherence Tomography image segmentation using machine and deep learning techniques: A review.

Optical Coherence Tomography (OCT) is an emerging technology that provides three-dimensional images of the microanatomy of biological tissue in-vivo a...

Visualization Techniques to Enhance the Explainability and Usability of Deep Learning Models in Glaucoma.

Review of Visualization Approaches in Deep Learning Models of Glaucoma.

Glaucoma is a major cause of irreversible blindness worldwide. As glaucoma often presents without symptoms, early detection and intervention are impor...

Deep-learning approach to detect childhood glaucoma based on periocular photograph.

Childhood glaucoma is one of the major causes of blindness in children, however, its diagnosis is of great challenge. The study aimed to demonstrate a...

FundusQ-Net: A regression quality assessment deep learning algorithm for fundus images quality grading.

OBJECTIVE: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and ...

Development of a deep learning system to detect glaucoma using macular vertical optical coherence tomography scans of myopic eyes.

Myopia is one of the risk factors for glaucoma, making accurate diagnosis of glaucoma in myopic eyes particularly important. However, diagnosis of gla...

Diagnostic ability of macular microvasculature with swept-source OCT angiography for highly myopic glaucoma using deep learning.

Macular OCT angiography (OCTA) measurements have been reported to be useful for glaucoma diagnostics. However, research on highly myopic glaucoma is l...

Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19.

INTRODUCTION: Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision l...

Deep Learning-Based Classification of Subtypes of Primary Angle-Closure Disease With Anterior Segment Optical Coherence Tomography.

PRCIS: We developed a deep learning-based classifier that can discriminate primary angle closure suspects (PACS), primary angle closure (PAC)/primary ...

Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State.

Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for d...

A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach.

Glaucoma is a leading cause of irreversible blindness, and its worsening is most often monitored with visual field (VF) testing. Deep learning models ...