AIMC Topic: Intraocular Pressure

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Predicting glaucoma progression using deep learning framework guided by generative algorithm.

Scientific reports
Glaucoma is a slowly progressing optic neuropathy that may eventually lead to blindness. To help patients receive customized treatment, predicting how quickly the disease will progress is important. Structural assessment using optical coherence tomog...

Assessment of angle closure disease in the age of artificial intelligence: A review.

Progress in retinal and eye research
Primary angle closure glaucoma is a visually debilitating disease that is under-detected worldwide. Many of the challenges in managing primary angle closure disease (PACD) are related to the lack of convenient and precise tools for clinic-based disea...

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

Scientific reports
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-Mi...

Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements.

American journal of ophthalmology
PURPOSE: To develop deep learning (DL) models estimating the central visual field (VF) from optical coherence tomography angiography (OCTA) vessel density (VD) measurements.

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

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
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.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
BACKGROUND: The use of artificial intelligence is becoming more prevalence in medicine with numerous successful examples in ophthalmology. However, much of the work has been focused on replicating the works of ophthalmologists. Given the analytical p...

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.

Journal of glaucoma
PRCIS: An optical coherence tomography (OCT)-based multimodal deep learning (DL) classification model, including texture information, is introduced that outperforms single-modal models and multimodal models without texture information for glaucoma di...

Deep Learning Classification of Angle Closure based on Anterior Segment OCT.

Ophthalmology. Glaucoma
PURPOSE: To assess the performance and generalizability of a convolutional neural network (CNN) model for objective and high-throughput identification of primary angle-closure disease (PACD) as well as PACD stage differentiation on anterior segment s...

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

Scientific reports
Myopia is one of the risk factors for glaucoma, making accurate diagnosis of glaucoma in myopic eyes particularly important. However, diagnosis of glaucoma in myopic eyes is challenging due to the frequent associations of distorted optic disc and dis...

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

Journal of glaucoma
PRCIS: We developed a deep learning-based classifier that can discriminate primary angle closure suspects (PACS), primary angle closure (PAC)/primary angle closure glaucoma (PACG), and also control eyes with open angle with acceptable accuracy.