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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要该项目的长期目标是确定光学相干断层扫描 (OCT) 和 OCT 是否血管造影（OCTA）可能会带来更准确和客观的诊断、更早的干预并改善早产儿视网膜病变 (ROP) 的结果国际共识和美国国立卫生研究院 (NIH)。过去 30 年资助的临床试验定义了表型分类、自然史、预后、然而，由于检眼镜的主观性，ROP 的治疗已得到充分证实。以及检查者之间的系统偏差，大多数人的治疗存在显着差异现实世界中存在严重的 ROP，这会导致治疗不足（以及由于视网膜导致的不良结果）。脱离）和过度治疗（使新生儿面临治疗的眼部和全身风险）。每年有 20,000 名婴儿因 ROP 而发生视网膜脱离 (RD)，有强有力的证据表明，大多数婴儿对于成人视网膜血管疾病，尤其是糖尿病视网膜病变 (DR)，OCT 和 OCTA 是可以预防的。可以检测和量化疾病特征，例如糖尿病黄斑水肿 (DME) 和视网膜新生血管形成 (NV) 在临床上注意到之前，可以进行早期治疗并降低 RD 导致失明的风险。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，并且商用手持式 OCT 对于超宽场 (UWF) OCT 和 OCTA 成像来说速度太慢。几个小组（包括我们自己的小组）已经发表了使用原型 100 至 200 kHz 扫频的初步结果源 (SS) OCT 系统，但由于缺乏固定，一致的数据采集仍然具有挑战性以及清醒新生儿随后的运动，这限制了对潜在益处的评估最近，人们对使用人工智能（AI）产生了浓厚的兴趣。（特别是深度学习），它依赖于高速图形处理单元（GPU）来提供实时该应用程序解决了 OCT 图像处理、分割和跟踪方面的两个基本差距。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 软件系统相结合，以实现一致的数据采集 (2) ROP 的定量客观指标能否提高 ROP 诊断和检测的客观性疾病进展的亚临床迹象，这可能有助于早期干预并改善结果未来，通过利用我们机构的 OCT、AI 和 ROP 专业知识，我们将通过三个方面解决这些问题。具体目标： (1) 开发超高速、手持式、全景超宽视场 OCT/OCTA 系统 (2)。开发实时GPU加速智能图像采集软件（3）评估临床意义。 OCT 衍生的生物标志物将该技术成功转化为 ROP 人群可以改善 ROP 诊断的准确性和客观性，并导致早期干预和改善患者的预后患有严重的 ROP。