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，以寻求监管部门对 人工智能(AI)软件作为医疗设备(SAMD)用于早产儿视网膜病变(ROP)的诊断。 ROP是全球儿童失明的主要原因，大约有5万名婴儿失明 其中大部分是可以通过准确和及时的诊断而预防的。I-ROP DL算法是 由i-ROP研究联盟开发，已被证明可提供专家级别的PLUS诊断 疾病，严重ROP的一个组成部分，基于来自Retcam(Natus，Middleton，WI)数字眼底的图像 摄影机。输出是对应于定义的PLUS疾病谱的血管严重程度评分(VSS 由国际ROP分类，并已被食品和药物管理局(FDA)认可为 适用于ROP SAMD的输出。如果整合到临床工作流程中，这项技术可以提供 自动、即时、专家级别的ROP诊断到床边，解决了以下护理领域的关键缺口之一 导致全球范围内可预防的失明。该项目的第一个目标是更新和重新培训i-ROP DL 算法提高了速度和可重复性，用于临床应用，最终完成图像质量和预处理 管道，并将其集成到iTeleGEN数据管理系统，这是一个ROP远程医疗软件平台。 第二个目标是对两个建议使用的适应症(IFU)进行必要的临床研究： 首先，IFU将作为一项辅助诊断研究，以提高PLUS疾病的临床诊断水平 基于一项多读者多病例研究的批准，初步结果是PLUS疾病的诊断有所改善， 基于五位专家参考标准诊断，配合使用VSS。第二个IFU将用于 超过轻度ROP的自主ROP筛查(MTMROP，定义为2型或更差，根据 早期治疗ROP的研究定义)。这项关键研究的初步结果将是85%的敏感性和 诊断MTMROP的特异度为85%，次要结果的灵敏度大于95% 检测需要治疗的ROP。该方案的第三个目标是验证I-ROP DL算法在一个 数字眼底相机由Forus Health(印度班加卢市)制造，配备ROP相机 分布在20多个国家和地区。如果成功，那么一旦获得FDA对Retcam的批准，它就可以 通过510K流程扩展到比Retcam更实惠且可广泛使用的摄像头 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 I-ROP DL算法的发明者，与俄勒冈健康与科学大学合作。在结束时， 在研究期间，目标将是拥有必要的数据，以支持FDA批准i-ROP DL算法 在两个数字眼底相机上安装两个IFU，距离将这项技术应用到床边又近了一步 减少全球因ROP致盲的婴儿数量。