Publications by authors named "Suria S Mannil"

Background: Deep learning (DL) is promising to detect glaucoma. However, patients' privacy and data security are major concerns when pooling all data for model development. We developed a privacy-preserving DL model using the federated learning (FL) paradigm to detect glaucoma from optical coherence tomography (OCT) images.

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Purpose: The primary purpose of this review is to provide a comprehensive summary on the technical principles of OCTA and to enumerate vascular parameters being explicated for glaucoma diagnosis and progression with emphasis on recent studies. In addition, the authors also summarize the future clinical potentials of OCTA in glaucoma and enumerate the limitations of this imaging modality in the present-day scenario.

Methods: The index study is a narrative review on OCTA in glaucoma.

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Purpose: Portable perimetric testing could be useful for community-based glaucoma screening programs. Frequency-doubling technology (FDT) and the Moorfields motion displacement test (MDT) are portable perimeters that have shown promise as potential screening tools for glaucoma. This study's goal was to determine the diagnostic accuracy of FDT and MDT for visual field defects and glaucoma.

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Background/objectives: Tablet-based perimetry could be used to test for glaucomatous visual field defects in settings without easy access to perimeters, although few studies have assessed diagnostic accuracy of tablet-based tests. The goal of this study was to determine the diagnostic accuracy of iPad perimetry using the visualFields Easy application.

Subjects/methods: This was a prospective, cross-sectional study of patients undergoing their first Humphrey Field Analyser (HFA) visual field test at a glaucoma clinic in India.

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Purpose: We aim to develop a multi-task three-dimensional (3D) deep learning (DL) model to detect glaucomatous optic neuropathy (GON) and myopic features (MF) simultaneously from spectral-domain optical coherence tomography (SDOCT) volumetric scans.

Methods: Each volumetric scan was labelled as GON according to the criteria of retinal nerve fibre layer (RNFL) thinning, with a structural defect that correlated in position with the visual field defect (i.e.

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Purpose: To develop a three-dimensional (3D) deep learning algorithm to detect glaucoma using spectral-domain optical coherence tomography (SD-OCT) optic nerve head (ONH) cube scans and validate its performance on ethnically diverse real-world datasets and on cropped ONH scans.

Methods: In total, 2461 Cirrus SD-OCT ONH scans of 1012 eyes were obtained from the Glaucoma Clinic Imaging Database at the Byers Eye Institute, Stanford University, from March 2010 to December 2017. A 3D deep neural network was trained and tested on this unique raw OCT cube dataset to identify a multimodal definition of glaucoma excluding other concomitant retinal disease and optic neuropathies.

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Purpose: To determine the diagnostic accuracy of potential screening tests for moderate to advanced glaucoma.

Design: Prospective diagnostic test accuracy study.

Participants: The study enrolled a consecutive series of patients aged ≥50 years who presented to a glaucoma clinic in South India without ever having received automated visual field testing.

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Objective: Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices.

Research Design And Methods: We trained and validated two versions of a multitask convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume scans and 2D B-scans, respectively.

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Purpose: The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes to differentiate between referable and nonreferable cases for glaucoma applied to real-world datasets to understand how this would affect the performance.

Methods: There were 2805 Cirrus optical coherence tomography (OCT) macula volumes (Macula protocol 512 × 128) of 1095 eyes from 586 patients at a single site that were used to train a fully 3D convolutional neural network (CNN). Referable glaucoma included true glaucoma, pre-perimetric glaucoma, and high-risk suspects, based on qualitative fundus photographs, visual fields, OCT reports, and clinical examinations, including intraocular pressure (IOP) and treatment history as the binary (two class) ground truth.

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Article Synopsis
  • Glaucoma is a major cause of irreversible blindness, and accurate structure and function assessments are crucial for diagnosis.
  • Optical Coherence Tomography (OCT) imaging is gaining traction for measuring eye structural changes, but automated glaucoma screening methods using OCT images are still limited.
  • The paper presents a novel method that combines structure analysis and function regression, achieving high classification performance on two large datasets, indicating its potential for automated glaucoma diagnosis.
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Background: Spectral-domain optical coherence tomography (SDOCT) can be used to detect glaucomatous optic neuropathy, but human expertise in interpretation of SDOCT is limited. We aimed to develop and validate a three-dimensional (3D) deep-learning system using SDOCT volumes to detect glaucomatous optic neuropathy.

Methods: We retrospectively collected a dataset including 4877 SDOCT volumes of optic disc cube for training (60%), testing (20%), and primary validation (20%) from electronic medical and research records at the Chinese University of Hong Kong Eye Centre (Hong Kong, China) and the Hong Kong Eye Hospital (Hong Kong, China).

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