SCREEN-DR: Image Analysis and Machine Learning Platform for Innovation in Diabetic Retinopathy Screening

Research teams: CMU; INESC TEC; UA
Organizations: ARSN; BMD; First Solutions; CHSJ
Main Research Area: Medical image analysis; Computer-aided diagnosis; Collaborative PACS-Cloud; Diabetic retinopathy screening
CMU Portugal Program webpage:
Principal Investigators: Aurélio Campilho (FEUP; INESC TEC); Asim Smailagic (CMU; ICES)

Diabetic Retinopathy (DR) is a leading cause of blindness in the industrialized world that can be avoided with early treatment. The Portuguese North Health Administration (ARSN) is implementing a mass screening for DR, with the goal of examining about 75% of identified diabetics, from an estimated diabetic population of 250.000, in the North of Portugal.

SCREEN-DR is the platform to be developed in this project to face three main challenges. The first challenge is to automatically evaluate image quality, and consequently remove the low quality images from the workflow. The second is to automatically detect the non-pathological cases. If these two challenges are overcome, the ophthalmologists need only to analyze about 25% of the cases, which will be an important gain in terms of time-to-decision efficacy. Additionally, the third challenge is to automatically grade DR in several scales of the disease severity. Each one of these challenges corresponds to an image module that will be remotely accessible by an image web service.

Under this context, the project’s goal is to create a distributed and automatic screening platform for DR, based on the state-of-the-art Information and Communication Technologies (ICT), including advanced Picture Archiving and Communication Systems (PACS) management, Machine Learning and Image Analysis, enabling immediate response from health carers, allowing accurate follow-up strategies, and fostering technological innovation.