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.
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).Predictors of Long-term Ophthalmic Complications after Closed Globe Injuries Using the Intelligent Research in Sight (IRIS®) Registry
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 Intelligent Research in Sight Registry
To estimate the prevalence of eyelid cancers in the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) Registry 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.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.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).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).Impact of Artificial Intelligence Assessment of Diabetic Retinopathy on Referral Service Uptake in a Low-Resource Setting: The RAIDERS Randomized Trial
This trial was designed to determine if artificial intelligence (AI)-supported diabetic retinopathy (DR) screening improved referral uptake in Rwanda.Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks
Develop computer vision models for image-based differentiation of bacterial and fungal corneal ulcers and compare their performance against human experts.
