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.
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.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.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.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.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.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.Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia
To evaluate the performance of a deep learning (DL) algorithm for retinopathy of prematurity (ROP) screening in Nepal and Mongolia.A Deep Learning Network for Classifying Arteries and Veins in Montaged Widefield OCT Angiograms
To propose a deep-learning−based method to differentiate arteries from veins in montaged widefield OCT angiography (OCTA).Synthetic Medical Images for Robust, Privacy-Preserving Training of Artificial Intelligence: Application to Retinopathy of Prematurity Diagnosis
Developing robust artificial intelligence (AI) models for medical image analysis requires large quantities of diverse, well-chosen data that can prove challenging to collect because of privacy concerns, disease rarity, or diagnostic label quality. Collecting image-based datasets for retinopathy of prematurity (ROP), a potentially blinding disease, suffers from these challenges. Progressively growing generative adversarial networks (PGANs) may help, because they can synthesize highly realistic images that may increase both the size and diversity of medical datasets.Improved Training Efficiency for Retinopathy of Prematurity Deep Learning Models Using Comparison versus Class Labels
To compare the efficacy and efficiency of training neural networks for medical image classification using comparison labels indicating relative disease severity versus diagnostic class labels from a retinopathy of prematurity (ROP) image dataset.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.Deepfakes in Ophthalmology: Applications and Realism of Synthetic Retinal Images from Generative Adversarial Networks
Generative adversarial networks (GANs) are deep learning (DL) models that can create and modify realistic-appearing synthetic images, or deepfakes, from real images. The purpose of our study was to evaluate the ability of experts to discern synthesized retinal fundus images from real fundus images and to review the current uses and limitations of GANs in ophthalmology.
