Phase I Amount
$1,907,135
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.
Public Health Relevance Statement: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness, which is most often preventable when accurate and timely ROP diagnosis can be provided. Artificial intelligence (AI) has the potential to provide immediate, expert-level clinical diagnosis to medical images, such as digital fundus images taken as part of ROP screening in neonatal care units. In this application, Siloam Vision will perform the necessary steps required to seek regulatory approval for the first AI algorithm for ROP diagnosis, which is the first step in making this technology available worldwide and potentially reducing the number of children who go blind from ROP.
Project Terms: Algorithms; Artificial Intelligence; AI system; Computer Reasoning; Machine Intelligence; Award; Blood Vessels; vascular; Child; 0-11 years old; Child Youth; Children (0-21); kids; youngster; Classification; Systematics; Clinical Research; Clinical Study; Diagnosis; Disease; Disorder; Dropout; Eye; Eyeball; Goals; Health; India; Investments; Marketing; Medical Device; Medical Imaging; Mission; Oregon; Paper; Privatization; Reference Standards; Research; Resources; Research Resources; Retinopathy of Prematurity; Retrolental Fibroplasia; premature retinopathy; Science; Computer software; Software; Specificity; Standardization; Technology; Testing; United States Food and Drug Administration; Food and Drug Administration; USFDA; Universities; Vision; Sight; visual function; Work; case report; Case Study; Caring; customs; Custom; Telemedicine; image processing; improved; eye fundus photography; fundus camera; Fundus photography; Clinical; Phase; Medical; Ensure; Training; pediatric; Childhood; Funding; Collaborations; clinical diagnosis; Diagnostic; Severities; System; Country; Blindness; vision loss; visual loss; meetings; meeting; Services; Early Diagnosis; early detection; Performance; Speed; disease classification; disorder classification; nosology; Devices; Code; Coding System; software development; develop software; developing computer software; diagnosis standard; Manufacturer; Provider; preventing; prevent; Data; International; Reader; Small Business Innovation Research Grant; SBIR; Small Business Innovation Research; Update; Validation; validations; Process; Image; imaging; Output; digital; designing; design; blind; early therapy; Early treatment; medically under served; medically underserved; primary outcome; secondary outcome; disease diagnosis; expedited review; screenings; screening; cloud based; neonatal care; accurate diagnosis; LMIC; low and middle-income countries; improved outcome; fundus imaging; retina imaging; retinal imaging; Infrastructure; Data Management System; Data Management Resources; segmentation algorithm; under served area; under served geographic area; under served location; under served region; underserved geographic area; underserved location; underserved region; underserved area; detection sensitivity; transfer learning; artificial intelligence algorithm; AI algorithm; diagnostic tool
