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: 10404639
• 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/10404639
• 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; 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.

项目摘要 本项目的长期目标是确定光学相干断层扫描（OCT）和OCT 血管造影（OCTA）可提高诊断准确性和客观性，早期干预， 早产儿视网膜病变（ROP）。国际共识和美国国立卫生研究院（NIH） 在过去30年中资助的临床试验已经定义了表型分类、自然史、预后， 管理ROP。然而，众所周知，由于检眼镜的主观性， 考试，和系统之间的偏见考官，有显着的变化，在治疗的最 真实的世界中的严重ROP形式。这导致治疗不足（以及由于视网膜病变导致的不良结果）。 脱离）和过度治疗（使新生儿暴露于治疗的眼部和全身风险）。大致 每年有20，000名婴儿因ROP而患上视网膜脱离（RD），并且有强有力的证据表明，大多数 这些都是可以预防的。在成人视网膜血管疾病中，最明显的是糖尿病视网膜病变（DR），OCT和OCTA 可以检测和量化糖尿病性黄斑水肿（DME）和视网膜新生血管等疾病特征 (NV)在临床上发现之前，使早期治疗和减少RD失明的风险成为可能。 然而，评估该技术在新生儿中的使用需要高速和便携式技术， 市售的手持OCT对于超宽视场（UWF）OCT和OCTA成像来说太慢。 几个小组（包括我们自己的）已经发表了使用原型100至200 kHz扫频的初步结果， 然而，由于缺乏固定，一致的数据采集仍然具有挑战性 以及随后的清醒新生儿的运动，这限制了对 技术在这个人群中。最近，人们对使用人工智能（AI） （特别是深度学习），它依赖于高速图形处理单元（GPU）来提供真实的时间 OCT图像处理、分割和跟踪。此应用程序解决了以下两个基本差距： 知识：（1）我们能否通过开发更快的超宽视场来克服技术挑战？ 查看SS-OCT系统与支持GPU的DL软件系统耦合，以实现一致的数据采集， 新生儿？(2)ROP的定量客观度量是否会提高ROP诊断和检测的客观性 疾病进展的亚临床体征，这可能使早期干预和改善的结果， 未来通过利用我们机构的OCT，AI和ROP专业知识，我们将在三个方面解决这些问题 具体目标：（1）研制超高速、手持式、全景式超宽视场OCT/OCTA系统。（二） 开发真实的GPU加速智能图像采集软件。(3)评价其临床意义 OCT衍生的生物标志物。成功地将这项技术转化为ROP人群可以提高 ROP诊断的准确性和客观性，并导致早期干预和改善患者的结局 严重的ROP。