The long-term goal of this project is to determine whether optical coherence tomography (OCT) and OCT angiography (OCTA) might lead more accurate and objective diagnosis, earlier intervention, and improved outcomes in retinopathy of prematurity (ROP). International consensus and National Institute of Health (NIH) funded clinical trials over the last 30 years have defined the phenotypic classifications, natural history, prognosis, and management of ROP. However, it is well established that due to the subjectivity of the ophthalmoscopic examination, and systematic bias between examiners, there is significant variation in treatment of the most severe forms of ROP in the real world. This leads to both under-treatment (and poor outcomes due to retinal detachment) and over-treatment (exposing neonates to the ocular and systemic risks of treatment). Roughly 20,000 babies per year develop retinal detachments (RD) due to ROP and there is strong evidence that most of these are preventable. In adult retinal vascular diseases, most notably diabetic retinopathy (DR), OCT and OCTA can detect and quantify disease features such as diabetic macular edema (DME) and retinal neovascularization (NV) before they are noted clinically, enabling earlier treatment and reducing the risk of blindness from RD. However, evaluating the use of this technology in neonates requires high speed and portable technology, and the commercially available handheld OCTs are too slow for ultra-widefield (UWF) OCT and OCTA imaging. Several groups (including our own) have published preliminary results using prototype 100 to 200 kHz swept- source (SS) OCT systems, however consistent data acquisition remains challenging due to the lack of fixation and subsequent motion in an awake neonate, which has limited the evaluation of the potential benefits of the technology in this population. Recently, there has been much interest in using artificial intelligence (AI) (specifically deep learning), which relies on high speed graphics processing units (GPUs) to provide real time OCT image processing, segmentation, and tracking. This application addresses 2 fundamental gaps in knowledge: (1) Can we overcome the technical challenges through the development of a faster ultrawide-field view SS-OCT system coupled with a GPU-enabled DL software system to enable consistent data acquisition in neonates? (2) Would quantitative objective metrics of ROP improve objectivity of ROP diagnosis and detect subclinical signs of disease progression which may enable earlier intervention and improved outcomes in the future. By leveraging our institution?s OCT, AI, and ROP expertise, we will address these questions in three specific aims: (1) Develop an ultra-high speed, handheld, panoramic ultra-widefield OCT/OCTA system. (2) Develop real time GPU accelerated intelligent image acquisition software. (3) Evaluate the clinical significance OCT derived biomarkers. Successful translation of this technology to the ROP population could improve the accuracy and objectivity of ROP diagnosis, and lead to earlier intervention and improved outcomes in patients with severe ROP.

Public Health Relevance

Optical Coherence Tomography (OCT) and OCT angiography (OCTA) have proven the ability to detect subclinical disease, provide quantitative evaluation of disease progression, and improve outcomes in the leading causes of blindness in adults, age-related macular degeneration and diabetic retinopathy. Technological and practical limitations have limited the application of this technology in routine use for non-sedated children undergoing routine screening for retinopathy of prematurity (ROP), the leading cause of blindness in children. The proposed project will develop an ultra-high speed, handheld OCT system with graphics processing unit (GPU) enabled real-time processing to improve the feasibility of panoramic ultra-widefield OCT/OCTA imaging in non-sedated neonates and evaluate the clinical utility of OCT-derived biomarkers in ROP.

National Institute of Health (NIH)
National Eye Institute (NEI)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Shen, Grace L
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Oregon Health and Science University
Schools of Medicine
United States
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