Purpose: To determine the effectiveness of detecting glaucomatous progression by a qualitative evaluation of wide-field (12 × 9 mm) scans on optical coherence tomography imaging. This method was compared to a conventional quantitative analysis of the global circumpapillary retinal nerve fiber layer (cpRNFL) thickness.
Methods: A total of 409 eyes with a clinical diagnosis of glaucoma or suspected glaucoma for which two wide-field scans were obtained at least 1 year apart ( = 125) and within one session ( = 284) were included to determine the sensitivity of detecting progression at 95% specificity. Qualitative OCT evaluation was performed in a similar manner to flicker chronoscopy by superimposing the two scans, and the progression probability was graded. A quantitative event-based analysis of the global cpRNFL thickness also was performed.
Results: Thirty-three and 25 eyes were deemed to have progressed based on qualitative and quantitative approaches, respectively ( = 0.152). A post hoc review of cases where the two methods disagreed revealed that all eyes missed by the quantitative analysis had established glaucomatous damage that appeared to show characteristic patterns of progression. All eyes missed by the qualitative evaluation appeared to be free of such established damage, and instead showed a generalized reduction in cpRNFL thickness.
Conclusions: Qualitative evaluation of OCT imaging information more frequently detected change consistent with known patterns of glaucomatous progression than global cpRNFL thickness, warranting further studies to evaluate its value.
Translational Relevance: A framework for qualitatively evaluating progressive glaucomatous changes on OCT imaging clinically shows promise.
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http://dx.doi.org/10.1167/tvst.7.3.5 | DOI Listing |
Transl Vis Sci Technol
January 2025
Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA.
Purpose: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of various DL models in enhancing OCT capabilities and addresses the challenges associated with their clinical implementation.
Methods: A review of articles utilizing DL models was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, and large language models (LLMs).
Vision (Basel)
January 2025
Wilmer Eye Institute, Johns Hopkins Hospital, Baltimore, MD 21287, USA.
Background: The outcomes of pediatric glaucoma suspects with a history of ocular trauma remains unknown; we describe the rate of conversion to glaucoma of this population of patients at a research-intensive academic center.
Methods: We conducted a retrospective case series of pediatric patients with a history of open- or closed-globe trauma who were being monitored as pediatric glaucoma suspects at the Wilmer Eye Institute between 2005 and 2016.
Results: A total of 62 eyes from 62 patients with a history of ocular trauma were identified with a median age at presentation of 9.
JCI Insight
January 2025
Gavin Herbert Eye Institute-Center for Translational Vision Research, Depar, University of California Irvine School of Medicine, Irvine, United States of America.
Elevation of intraocular pressure (IOP) due to trabecular meshwork (TM) dysfunction, leading to neurodegeneration, is the pathological hallmark of primary open-angle glaucoma (POAG). Impaired axonal transport is an early and critical feature of glaucomatous neurodegeneration. However, a robust mouse model that accurately replicates these human POAG features has been lacking.
View Article and Find Full Text PDFInt J Ophthalmol
January 2025
Department of Ophthalmology, the Second Affiliated Hospital of Xi'an Medical University, Xi'an 710038, Shaanxi Province, China.
Glaucoma is a group of diseases characterized by progressive optic nerve degeneration, with the characteristic pathological change being death of retinal ganglion cells (RGCs), which ultimately causes visual field loss and irreversible blindness. Elevated intraocular pressure (IOP) remains the most important risk factor for glaucoma, but the exact mechanism responsible for the death of RGCs is currently unknown. Neurotrophic factor deficiency, impaired mitochondrial structure and function, disrupted axonal transport, disturbed Ca homeostasis, and activation of apoptotic and autophagic pathways play important roles in RGC death in glaucoma.
View Article and Find Full Text PDFPLoS One
January 2025
School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.
Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.
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