Artificial intelligence assisted panoramic Optical Coherence Tomography Angiography for Retinopathy of Prematurity

人工智能辅助全景光学相干断层扫描血管造影治疗早产儿视网膜病变

• 批准号: 10198930
• 负责人: John Peter Campbell
• 金额: $ 37.73万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10198930
• 关键词: Address; Adult; Aftercare; Age related macular degeneration; Algorithms; Angiography; Area; Artificial Intelligence; Biological Markers; Blindness; Child; Childhood; Classification; Clinical; Clinical Trials; Computer software; Consensus; Coupled; Cross-Sectional Studies; Data; Development; Devices; Diabetic Retinopathy; Diagnosis; Disease; Disease Progression; Dyes; Early Diagnosis; Early Intervention; Early treatment; Evaluation; Eye; Fluorescein Angiography; Funding; Fundus; Future; Goals; Image; Image Analysis; Injections; Institution; Intelligence; International; Knowledge; Lasers; Lead; Length; Longitudinal Studies; Measurement; Medical Imaging; Methods; Monitor; Morphologic artifacts; Motion; Natural History; Neonatal; Ophthalmic examination and evaluation; Ophthalmoscopes; Optical Coherence Tomography; Optics; Outcome; Patients; Performance; Peripheral; Phenotype; Pilot Projects; Population; Primary Health Care; Prognosis; Publishing; Quantitative Evaluations; Retina; Retinal Detachment; Retinal Neovascularization; Retinopathy of Prematurity; Risk; Scanning; Severities; Severity of illness; Source; Speed; Structure; System; Systematic Bias; Technology; Testing; Time; Translations; United States National Institutes of Health; Variant; Vascular Diseases; Visualization; accurate diagnosis; arm; awake; base; blind; clinical Diagnosis; clinically significant; data acquisition; deep learning; design; diabetic; disease classification; disorder of macula of retina; image processing; imaging Segmentation; improved; improved outcome; instrument; interest; lens; macular edema; neonate; neovascularization; novel; overtreatment; parallel computer; portability; prototype; real-time images; research clinical testing; routine screening; sample fixation; software systems; standard of care; treatment response; treatment risk

PROJECT SUMMARY 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.

项目总结