Automated end-to-end retinal screening system with robotic image capture and deep learning analysis - Abstract
In this SBIR project, we propose EyeScreenBot, an end-to-end automated retinal im-
age capture and analysis system, comprising a self-driven, robotic fundus camera plat-
form for automated image capture and a deep learning-based image analysis engine for
generation of automated screening outcome. With the large, growing, and aging popula-
tion and the increased prevalence of diabetes, a large number of people are at risk for
vision loss due to several eye diseases including diabetic retinopathy (DR), age-related
macular degeneration (AMD), and glaucoma. Although eye screening is effective in re-
ducing vision loss, there are not enough clinical personnel and eye-care experts for pop-
ulation-wide eye screening. Recent advances with automated image analysis are helping
alleviate the situation, but they are still limited by the need for good quality images of the
patients captured by trained technicians or expensive retinal cameras equipped for auto-
mated capture. EyeScreenBot 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 op-
tometrist sites, addressing both automated capture and subsequent automated analysis,
all without the need for trained technicians or eye experts at the point of care. When
deployed and commercialized, this device will rapidly aid scaling of eye screening for the
masses, thereby having an enormous impact in improving the quality and accessibility of
eye care and helping reduce preventable vision loss.
