VSI: Artificial Intelligence and Big Data
Ophthalmology Science has published a special virtual issue featuring Big Data & Artificial Intelligence (AI) articles. The collection includes novel methodologies, such as non-traditional statistical approaches that can be applied to Big Data and machine learning/deep learning studies. Our goal for this special issue is to initiate discussions of current challenges and potential strategies to overcome them. Some examples include standardization of datasets, data sharing processes, data privacy, and obstacles for transferring research findings into clinical care. Understanding both the potential and limitations of Big Data and AI approaches through an array of diverse studies and commentaries is the primary goal of this Ophthalmology Science special issue. We are grateful for authors who submitted to this special collection and we thank our guest editors, James D. Brandt, MD (University of California Davis Medical Center), Aaron Yuntai Lee, MD, MSCI (University of Washington), and Cecilia Lee, MD (University of Washington) for sharing their time and expertise.
Macular Telangiectasia Type 2: A Classification System Using MultiModal Imaging MacTel Project Report Number 10
To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging.SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease
Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving artificial intelligence-enabled diagnosis of IRDs using generative adversarial networks (GANs).Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis
To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis.Automated Detection of Posterior Vitreous Detachment on OCT Using Computer Vision and Deep Learning Algorithms
To develop automated algorithms for the detection of posterior vitreous detachment (PVD) using OCT imaging.DDLSNet: A Novel Deep Learning-Based System for Grading Funduscopic Images for Glaucomatous Damage
To report an image analysis pipeline, DDLSNet, consisting of a rim segmentation (RimNet) branch and a disc size classification (DiscNet) branch to automate estimation of the disc damage likelihood scale (DDLS).A Platform for Tracking Surgeon and Observer Gaze as a Surrogate for Attention in Ophthalmic Surgery
To develop and validate a platform that can extract eye gaze metrics from surgeons observing cataract and vitreoretinal procedures and to enable post hoc data analysis to assess potential discrepancies in eye movement behavior according to surgeon experience.RimNet: A Deep Neural Network Pipeline for Automated Identification of the Optic Disc Rim
Accurate neural rim measurement based on optic disc imaging is important to glaucoma severity grading and often performed by trained glaucoma specialists. We aim to improve upon existing automated tools by building a fully automated system (RimNet) for direct rim identification in glaucomatous eyes and measurement of the minimum rim-to-disc ratio (mRDR) in intact rims, the angle of absent rim width (ARW) in incomplete rims, and the rim-to-disc-area ratio (RDAR) with the goal of optic disc damage grading.Deep-Learning–Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT
Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies.Predictors of Long-term Ophthalmic Complications after Closed Globe Injuries Using the IRIS® Registry (Intelligent Research in Sight)
To identify clinical factors associated with the need for future surgical intervention following closed globe ocular trauma.Use of Machine Learning to Assess Cataract Surgery Skill Level With Tool Detection
To develop a method for objective analysis of the reproducible steps in routine cataract surgery.Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization
To compare the diagnostic accuracy and explainability of a Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG) and identify the salient areas of the photographs most important for each model’s decision-making process.Development of a Cloud-Based Clinical Decision Support System for Ophthalmology Triage Using Decision Tree Artificial Intelligence
Clinical decision support systems (CDSS) are an emerging frontier in teleophthalmology, drawing on heuristic decision making to augment processes such as triage and referral. We describe the development and implementation of a novel cloud-based decision tree CDSS for on-call ophthalmology consults. The objective was to standardize the triage and referral process while providing a more accurate provisional diagnosis and urgency.Artificial Intelligence Detection of Diabetic Retinopathy: Subgroup Comparison of the EyeArt System with Ophthalmologists’ Dilated Examinations
To compare general ophthalmologists, retina specialists, and the EyeArt Artificial Intelligence (AI) system to the clinical reference standard for detecting more than mild diabetic retinopathy (mtmDR).Prevalence of and Associated Factors for Eyelid Cancer in the American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight)
To estimate the prevalence of eyelid cancers in the American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight) and evaluate the associated factors.Visual Field Prediction: Evaluating the Clinical Relevance of Deep Learning Models
Two novel deep learning methods using a convolutional neural network (CNN) and a recurrent neural network (RNN) have recently been developed to forecast future visual fields (VFs). Although the original evaluations of these models focused on overall accuracy, it was not assessed whether they can accurately identify patients with progressive glaucomatous vision loss to aid clinicians in preventing further decline. We evaluated these 2 prediction models for potential biases in overestimating or underestimating VF changes over time.Visual Acuity: Assessment of Data Quality and Usability in an Electronic Health Record System
To examine the data quality and usability of visual acuity (VA) data extracted from an electronic health record (EHR) system during ophthalmology encounters and provide recommendations for consideration of relevant VA end points in retrospective analyses.Primary Open-Angle Glaucoma Diagnosis from Optic Disc Photographs Using a Siamese Network
Primary open-angle glaucoma (POAG) is one of the leading causes of irreversible blindness in the United States and worldwide. Although deep learning methods have been proposed to diagnose POAG, these methods all used a single image as input. Contrastingly, glaucoma specialists typically compare the follow-up image with the baseline image to diagnose incident glaucoma. To simulate this process, we proposed a Siamese neural network, POAGNet, to detect POAG from optic disc photographs.Ocular Health and National Data Standards: A Case for Including Visual Acuity in the United States Core Data for Interoperability (USCDI)
Health care data standards are critical for information exchange between clinical information systems such as electronic health record systems, imaging devices, and picture and archiving communication systems. Data standards are also important for facilitating care coordination for patients across different care settings. Recent federal regulations such as the 21st Century Cures Act have brought health care data standards to the forefront of national discourse. While the need for greater adoption of imaging data standards in ophthalmology has been recently highlighted,1 there is, similarly, an ongoing need to expand other data standards for ophthalmology.Implementation of a Large-Scale Image Curation Workflow Using Deep Learning Framework
The curation of images using human resources is time intensive but an essential step for developing artificial intelligence (AI) algorithms. Our goal was to develop and implement an AI algorithm for image curation in a high-volume setting. We also explored AI tools that will assist in deploying a tiered approach, in which the AI model labels images and flags potential mislabels for human review.Association of Environmental Factors with Age-Related Macular Degeneration using the Intelligent Research in Sight Registry
Investigate associations of natural environmental exposures with exudative and nonexudative age-related macular degeneration (AMD) across the United States.Detection of Nonexudative Macular Neovascularization on Structural OCT Images Using Vision Transformers
A deep learning model was developed to detect nonexudative macular neovascularization (neMNV) using OCT B-scans.Graft Detachment after Descemet Membrane Endothelial Keratoplasty with and without Cataract Surgery
To evaluate graft detachment after Descemet membrane endothelial keratoplasty (DMEK) in pseudophakic eyes and DMEK combined with cataract surgery (triple DMEK).Early Glaucoma Detection by Using Style Transfer to Predict Retinal Nerve Fiber Layer Thickness Distribution on the Fundus Photograph
We aimed to develop a deep learning (DL)–based algorithm for early glaucoma detection based on color fundus photographs that provides information on defects in the retinal nerve fiber layer (RNFL) and its thickness from the mapping and translating relations of spectral domain OCT (SD-OCT) thickness maps.United States Cornea Graft Registry: Vision for the Future
The cornea is one of the most commonly transplanted tissues in the United States.1 During the last 3 decades, the Eye Bank Association of America (EBAA) through its member eye banks has provided > 1 645 013 corneas, both domestically and internationally.2 Over the past decade, there has been a 20% increase in keratoplasty procedures with consequent growing demand for donor tissue every year.2 Coinciding with this growth has been a shift from full-thickness penetrating keratoplasty toward endothelial keratoplasty for treatment of endothelial disorders, resulting in better patient outcomes.Characteristics and Outcomes of Patients with Scleritis in the IRIS® Registry (Intelligent Research in Sight) Database
To report patient characteristics and factors associated with poor visual acuity and abnormal intraocular pressure (IOP) in patients with scleritis in the American Academy of Ophthalmology’s IRIS® Registry (Intelligent Research in Sight).
