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.

本应用的目的是进行必要的临床研究，以寻求监管批准 人工智能（AI）软件作为医疗设备（SAMD），用于预性诊断（ROP）诊断。 ROP是全球儿童失明的主要原因，大约有50,000个婴儿失明 每年，大多数可以通过准确及时的诊断来预防。 I-ROP DL算法是 由I-Rop研究联盟开发，已被证明提供了Plus的专家级诊断 疾病是严重ROP的一个组成部分，基于Retcam（Natus，Middleton，Wi）数字基础的图像 相机。输出是与疾病谱相对应的血管严重程度评分（VSS） 通过国际ROP分类，并得到食品药品监督管理局（FDA）的认可 ROP SAMD的适当输出。如果纳入临床工作流程，该技术可以提供 自动化，直接的专家级诊断ROP到床边，解决了护理的关键差距之一 导致全球可预防的失明。该项目的第一个目的是更新和重新训练I-ROP DL 算法以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ROP远程医疗软件平台的Itelegen数据管理系统中。 第二个目的是为两个提出的使用指示（IFU）进行必要的临床研究： 首先，IFU将是一项辅助诊断研究，以改善具有调节性的疾病的临床诊断 基于多阅读器多案例研究的批准，其主要结果是改善对疾病的诊断， 基于使用VSS的五个专家参考标准诊断。第二个IFU将用于 自主ROP筛选比轻度ROP多（MTMROP，定义为2型或更糟 ROP研究定义的早期治疗）。关键研究将具有85％敏感性的主要结果，并且 MTMROP诊断的85％特异性，次要结果超过95％的敏感性 检测需要治疗的ROP。该提案的第三个目的是验证A上的I-ROP DL算法 由Forus Health（印度班加罗尔）制造的数字眼底相机，一家数字眼保健公司，带有ROP摄像头 在20多个国家 /地区分发。如果成功，那么一旦在retcam上获得FDA批准， 通过510k的过程扩展到比RetCAM更实惠且广泛可用的相机 在低收入和中等收入国家。这项工作将由Siloam Vision完成，这是一家由两个公司创立的公司 I-ROP DL算法的发明者与俄勒冈健康与科学大学一起。在 研究期，目标是获得必要的数据，以支持FDA的I-ROP DL算法的批准 两个在两个数字眼镜摄像机上的IFU，并且更接近将这项技术带到床边 减少来自全球ROP的婴儿的数量。