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.
- To develop automated algorithms for the detection of posterior vitreous detachment (PVD) using OCT imaging.
- 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.
- A deep learning model was developed to detect nonexudative macular neovascularization (neMNV) using OCT B-scans.
- 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.
- To assess 3-dimensional surface shape patterns of the optic nerve head (ONH) and peripapillary retinal nerve fiber layer (RNFL) in glaucoma with unsupervised artificial intelligence (AI).
- To evaluate the performance of a federated learning framework for deep neural network-based retinal microvasculature segmentation and referable diabetic retinopathy (RDR) classification using OCT and OCT angiography (OCTA).