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: 10612906
• 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 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10612906
• 关键词: Acceleration; 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; Optical Coherence Tomography; Optics; Outcome; Patients; Performance; Peripheral; Phenotype; Pilot Projects; Population; Prognosis; Proliferating; Publishing; Quantitative Evaluations; Retina; Retinal Detachment; Retinal Neovascularization; Retinopathy of Prematurity; Risk Reduction; 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; artificial intelligence method; awake; 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; ophthalmic examination; 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 该项目的长期目标是确定光学相干断层扫描（OCT）和OCT是否是否 血管造影（OCTA）可能会领导更准确和客观的诊断，更早的干预并改善 早产视网膜病变（ROP）的结果。国际共识和国家卫生研究院（NIH） 在过去30年中，资助的临床试验定义了表型分类，自然史，预后， 和ROP的管理。但是，众所周知，由于眼镜的主观性 考试和检查人员之间的系统偏见，治疗最大的差异很大 在现实世界中，ROP的严重形式。这既导致治疗不足（由于视网膜导致的结果不佳 分离）和过度治疗（将新生儿暴露于眼部和全身治疗风险）。大致 由于ROP，每年有20,000名婴儿发展视网膜支队（RD），并且有强有力的证据表明大多数 这些是可以预防的。在成年视网膜血管疾病中，最著名的是糖尿病性视网膜病（DR），OCT和八 可以检测和量化疾病特征，例如糖尿病性黄斑水肿（DME）和视网膜新血管化 （NV）在临床上注意到它们之前，可以早期治疗并降低了RD失明的风险。 但是，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 对于超宽场（UWF）OCT和Octa成像而言，市售的手持式OCT太慢。 几个小组（包括我们自己的组）已使用100至200 kHz的原型发布了初步结果。 来源（SS）OCT系统，但是由于缺乏固定，一致的数据获取仍然是挑战 并随后在清醒的新生儿中进行动议，该动议限制了对的评估 该人群的技术。最近，人们对使用人工智能（AI）引起了极大的兴趣 （特别是深度学习），这依赖于高速图形处理单元（GPU）提供实时时间 OCT图像处理，细分和跟踪。该申请介绍了2个基本差距 知识：（1）我们可以通过开发更快的超速场克服技术挑战 查看SS-OCT系统与启用GPU的DL软件系统相结合，以启用一致的数据采集 新生儿？ （2）将量化ROP诊断的ROP改善客观性的目标指标并检测 疾病进展的亚临床迹象，可以使早期干预和改善的预后 未来。通过利用我们机构的OCT，AI和ROP专业知识，我们将在三个中解决这些问题 具体目的：（1）开发超高速度，手持式，全景超宽赛oct/八颗系统。 （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。将该技术成功地转换给ROP人群可以改善 ROP诊断的准确性和目标，并导致早期干预和患者的预后改善 与严重的ROP。

项目成果

Phase-corrected buffer averaging for enhanced OCT angiography using FDML laser.

使用 FDML 激光对增强 OCT 血管造影进行相位校正缓冲平均。

• DOI: 10.1364/ol.430915
• 发表时间: 2021
• 期刊: Optics letters
• 影响因子: 3.6
• 作者: Miao,Yusi;Siadati,Mahsa;Song,Jun;Ma,Da;Jian,Yifan;Beg,MirzaFaisal;Sarunic,MarinkoV;Ju,MyeongJin
• 通讯作者: Ju,MyeongJin

Association of Optical Coherence Tomography-Measured Fibrovascular Ridge Thickness and Clinical Disease Stage in Retinopathy of Prematurity.

• DOI: 10.1001/jamaophthalmol.2022.4173
• 发表时间: 2022-10-13
• 期刊: JAMA OPHTHALMOLOGY
• 影响因子: 8.1
• 作者: Nguyen, Thanh-Tin P.;Ni, Shuibin;Ostmo, Susan;Rajagopalan, Archeta;Coyner, Aaron S.;Woodward, Mani;Chiang, Michael F.;Jia, Yali;Huang, David;Campbell, J. Peter;Jian, Yifan
• 通讯作者: Jian, Yifan

Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence-Based Vascular Severity Score.

• DOI: 10.1001/jamanetworkopen.2022.51512
• 发表时间: 2023-01-03
• 期刊: JAMA NETWORK OPEN
• 影响因子: 13.8
• 作者: Eilts, Sonja K.;Pfeil, Johanna M.;Poschkamp, Broder;Krohne, Tim U.;Eter, Nicole;Barth, Teresa;Guthoff, Rainer;Lagreze, Wolf;Grundel, Milena;Bruender, Marie-Christine;Busch, Martin;Kalpathy-Cramer, Jayashree;Chiang, Michael F.;Chan, R. V. Paul;Coyner, Aaron S.;Ostmo, Susan;Campbell, J. Peter;Stahl, Andreas
• 通讯作者: Stahl, Andreas