Validation of artificial intelligence (AI) based software as medical device (SaMD) for retinopathy of prematurity (ROP)

验证基于人工智能 (AI) 的软件作为治疗早产儿视网膜病变 (ROP) 的医疗设备 (SaMD)

• 批准号: 10760401
• 负责人: John Peter Campbell
• 金额: $ 190.71万
• 依托单位: SILOAM VISION, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10760401
• 关键词: Algorithms; Artificial Intelligence; Award; Blindness; Blood Vessels; Caring; Case Study; Child; Childhood; Classification; Clinical; Clinical Research; Code; Collaborations; Computer software; Country; Custom; Data; Data Management Resources; Devices; Diagnosis; Diagnostic; Disease; Dropout; Early Diagnosis; Early treatment; Ensure; Eye; Funding; Fundus photography; Goals; Health; Image; India; Infrastructure; International; Investments; Manufacturer; Marketing; Medical; Medical Device; Medical Imaging; Mission; Oregon; Output; Paper; Performance; Phase; Privatization; Process; Provider; Reader; Reference Standards; Research; Resources; Retinopathy of Prematurity; Science; Services; Severities; Small Business Innovation Research Grant; Specificity; Speed; Standardization; System; Technology; Telemedicine; Testing; Training; United States Food and Drug Administration; Universities; Update; Validation; Vision; Work; accurate diagnosis; artificial intelligence algorithm; blind; clinical diagnosis; cloud based; design; detection sensitivity; diagnosis standard; diagnostic tool; digital; disease classification; disease diagnosis; expedited review; fundus imaging; image processing; improved; improved outcome; low and middle-income countries; medically underserved; meetings; neonatal care; prevent; primary outcome; retinal imaging; screening; secondary outcome; segmentation algorithm; software development; transfer learning; underserved area

The purpose of this application is to perform the necessary clinical studies to seek regulatory approval for an artificial intelligence (AI) software as medical device (SaMD) for retinopathy of prematurity (ROP) diagnosis. ROP is a leading cause of childhood blindness worldwide, with approximately 50,000 babies going blind annually, most of which is preventable with accurate and timely diagnosis. The i-ROP DL algorithm was developed by the i-ROP research consortium and has been shown to provide expert-level diagnosis of plus disease, a component of severe ROP, based on images from the Retcam (Natus, Middleton, WI) digital fundus camera. The output is a vascular severity score (VSS) that corresponds to spectrum of plus disease, as defined by the International Classification of ROP, and has been endorsed by the Food & Drug Administration (FDA) as an appropriate output for an ROP SaMD. If incorporated into a clinical workflow, this technology could provide automated, immediate, expert-level diagnosis of ROP to the bedside, solving one of the key gaps in care that results in preventable blindness worldwide. The first aim of this project is to update and retrain the i-ROP DL algorithm to improve speed and repeatability for clinical use, finalize the image quality and pre-processing pipeline, and integrate it into the iTeleGEN data management system, an ROP telemedicine software platform. The second aim is to perform the necessary clinical studies for the two proposed indications for use (IFU): The first IFU will be as an assistive diagnostic study to improve the clinical diagnosis of plus disease with regulatory approval based on a multi-reader multi-case study with a primary outcome of improved diagnosis of plus disease, based on a five expert reference standard diagnosis, with the use of the VSS. The second IFU will be for autonomous ROP screening for more than mild ROP (MTMROP, defined as type 2 or worse according to the Early Treatment for ROP study definition). The pivotal study will have a primary outcome of 85% sensitivity and 85% specificity for the diagnosis of MTMROP, with a secondary outcome of greater than 95% sensitivity for detection of treatment-requiring ROP. The third aim of the proposal is to validate the i-ROP DL algorithm on a digital fundus camera made by Forus Health (Bengaluru, India), a digital eye care company, with ROP camera distribution in more than 20 countries. If successful, then once FDA approval is obtained on the Retcam it may be extended through a 510K process to a camera that is more affordable than the Retcam and widely available in low- and middle-income countries. This work will be done by Siloam Vision, a company started by two of the inventors of the i-ROP DL algorithm, in conjunction with Oregon Health & Science University. At the end of the study period, the goal will be to have the necessary data to support FDA approval of the i-ROP DL algorithm for two IFUs on two digital fundus cameras and being one step closer to bringing this technology to the bedside to reduce the number of babies going blind from ROP worldwide.

本申请的目的是进行必要的临床研究，以寻求监管部门的批准 人工智能 (AI) 软件作为医疗设备 (SaMD)，用于诊断早产儿视网膜病变 (ROP)。 ROP 是全球儿童失明的主要原因，约有 50,000 名婴儿失明 每年，其中大部分是可以通过准确和及时的诊断来预防的。 i-ROP DL 算法是 由 i-ROP 研究联盟开发，已被证明可以提供专家级的 plus 诊断 疾病，严重 ROP 的一个组成部分，基于 Retcam（Natus，米德尔顿，威斯康星州）数字眼底图像 相机。输出是血管严重程度评分 (VSS)，对应于附加疾病谱，如定义 符合ROP国际分类标准，并获得美国食品药品监督管理局（FDA）认可为 ROP SaMD 的适当输出。如果纳入临床工作流程，该技术可以提供 床边自动、即时、专家级的 ROP 诊断，解决了护理中的关键差距之一 导致全世界可预防的失明。该项目的首要目标是更新和重新训练 i-ROP DL 算法提高临床使用的速度和可重复性，最终确定图像质量和预处理 管道，并将其集成到 iTeleGEN 数据管理系统（ROP 远程医疗软件平台）中。 第二个目标是针对两个拟议的使用适应症（IFU）进行必要的临床研究： 第一个 IFU 将作为一项辅助诊断研究，通过监管改善 + 疾病的临床诊断 批准基于多读者多案例研究，主要结果是改善了附加疾病的诊断， 基于五位专家的参考标准诊断，并使用 VSS。第二个 IFU 将用于 针对超过轻度 ROP 的自主 ROP 筛查（MTMROP，根据 ROP 研究定义的早期治疗）。关键研究的主要结果敏感性为 85% MTMROP 诊断的特异性为 85%，次要结果的敏感性大于 95% 检测需要治疗的 ROP。该提案的第三个目标是在 由数字眼保健公司 Forus Health（印度班加罗尔）制造的数字眼底相机，配有 ROP 相机 分布于20多个国家。如果成功，那么一旦 Retcam 获得 FDA 批准，它可能会 通过 510K 流程扩展到比 Retcam 更实惠且广泛使用的相机 在低收入和中等收入国家。这项工作将由 Siloam Vision 完成，这是一家由两位创始人创办的公司 i-ROP DL 算法的发明者，与俄勒冈健康与科学大学合作。结束时 研究期间，目标是获得必要的数据来支持 FDA 批准 i-ROP DL 算法 两个数字眼底相机上的两个 IFU，距离将这项技术应用于临床又近了一步 减少全球因 ROP 失明的婴儿数量。