A5000253790- Retinal Imaging and Analysis
- Glaucoma and retinal disorders
- Retinal Diseases and Treatments
- Brain Tumor Detection and Classification
- Retinal and Optic Conditions
- Traditional Chinese Medicine Studies
- Corneal surgery and disorders
- Medical Imaging and Analysis
- Digital Imaging for Blood Diseases
Stanford University
2019-2023
Narayana Nethralaya
2021-2022
Smith-Kettlewell Eye Research Institute
2019-2022
BackgroundSpectral-domain optical coherence tomography (SDOCT) can be used to detect glaucomatous optic neuropathy, but human expertise in interpretation of SDOCT is limited. We aimed develop and validate a three-dimensional (3D) deep-learning system using volumes neuropathy.MethodsWe retrospectively collected dataset including 4877 disc cube for training (60%), testing (20%), primary validation (20%) from electronic medical research records at the Chinese University Hong Kong Eye Centre...
Purpose: The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes differentiate between referable and nonreferable cases for glaucoma applied real-world datasets understand how would affect the performance. Methods: There were 2805 Cirrus (OCT) macula volumes (Macula protocol 512 × 128) 1095 eyes 586 patients at single site that used train fully convolutional neural network (CNN). Referable included true...
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.We trained validated two versions multitask convolution neural network (CNN) to classify (center-involved [CI-DME], non-CI-DME, or absence DME) three-dimensional (3D) volume scans 2D B-scans,...
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 cropped ONH scans. Methods: In total, 2461 Cirrus SD-OCT of 1012 eyes were obtained from the Glaucoma Clinic Imaging Database at Byers Eye Institute, Stanford University, March 2010 December 2017. A 3D neural network was trained tested this...
We aim to develop a multi-task three-dimensional (3D) deep learning (DL) model detect glaucomatous optic neuropathy (GON) and myopic features (MF) simultaneously from spectral-domain optical coherence tomography (SDOCT) volumetric scans.Each scan was labelled as GON according the criteria of retinal nerve fibre layer (RNFL) thinning, with structural defect that correlated in position visual field (i.e., reference standard). MF were graded by SDOCT en face images, defined presence...
Deep learning (DL) is promising to detect glaucoma. However, patients' privacy and data security are major concerns when pooling all for model development. We developed a privacy-preserving DL using the federated (FL) paradigm glaucoma from optical coherence tomography (OCT) images.
<a><b>Objective:</b></a> 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. <p><b>Research Design Methods:</b> trained validated two versions multi-task convolution neural network (CNN) to classify...
Background: Diabetic macular edema (DME) is the primary cause of irreversible 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.Methods: trained validated two versions multi-task network to classify (centre-involved [CI-DME], non-CI-DME, or absence DME) three-dimensional (3D) volume-scans two-dimensional...
We describe a new approach to automated Glaucoma detection in 3D Spectral Domain Optical Coherence Tomography (OCT) optic nerve scans. First, we gathered unique and diverse multi-ethnic dataset of OCT scans consisting glaucoma non-glaucomatous cases obtained from four tertiary care eye hospitals located different countries. Using this longitudinal data, achieved state-of-the-art results for automatically detecting single raw using Deep Learning system. These are close human doctors variety...
<a><b>Objective:</b></a> 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. <p><b>Research Design Methods:</b> trained validated two versions multi-task convolution neural network (CNN) to classify...