SBIR/STTR Award attributes
AbstractIn this SBIR projectwe propose EyeScreenBotan end to end automated retinal image capture and analysis systemcomprising a self drivenrobotic fundus camera platform for automated image capture and a deep learning based image analysis engine for generation of automated screening outcomeWith the largegrowingand aging population and the increased prevalence of diabetesa large number of people are at risk for vision loss due to several eye diseases including diabetic retinopathyDRage related macular degenerationAMDand glaucomaAlthough eye screening is effective in reducing vision lossthere are not enough clinical personnel and eye care experts for population wide eye screeningRecent advances with automated image analysis are helping alleviate the situationbut they are still limited by the need for good quality images of the patients captured by trained technicians or expensive retinal cameras equipped for automated captureEyeScreenBot will be developed to provide a truly end to end screening solution that is cost effective and suitable for deployment in primary care clinics or optometrist sitesaddressing both automated capture and subsequent automated analysisall without the need for trained technicians or eye experts at the point of careWhen deployed and commercializedthis device will rapidly aid scaling of eye screening for the massesthereby having an enormous impact in improving the quality and accessibility of eye care and helping reduce preventable vision loss Narrative EyeScreenBotan end to end automated screening system with intelligent image capture and analysiswill truly enable eye screening at massive scalewhich is necessary and urgent since the population at risk for preventable vision loss due to retinal diseasessuch as diabetic retinopathyis growing at a staggering rateTriaging and identification of at risk patients will allow for timely intervention to preventslowor even reverse the disease progression and loss of vision
