Malarial Retinopathy Screening System for Improved Diagnosis of Cerebral Malaria

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DESCRIPTION (provided by applicant): VisionQuest Biomedical and its collaborators have assembled a team of inter-disciplinary scientists with considerable experience in automated retinal image analysis, clinical ophthalmology with specialized research in malarial retinopathy (MR), and cerebral malaria diagnosis (CM). This team will develop and test an automated MR screening software system integrated with a low-cost and portable retinal camera, iRxCam, developed by VisionQuest; to assist and improve the accuracy of CM diagnosis. CM is the most lethal clinical syndrome associated with malarial disease. It affects more than 200 million people annually and claims about 800,000 deaths worldwide which include 700,000 mortalities from African children. As a consequence of high incidence of CM, it is often misdiagnosed for other pathologies with similar symptoms, leading to an incorrect treatment. Once clinically suspected, an accurate means to confirm the presence of CM or to investigate for a non-malarial illness is critically needed to improve outcomes. Since MR is greater than 90% sensitive and specific to the presence of CM once clinically diagnosed, retinal screening for MR represents an effective means to assist and improve the specificity of CM diagnosis. Our proposed system will not replace the current CM diagnostic standard, but instead will be added to it to increase the accuracy of CM diagnosis leading to a smaller number of false positive outcomes. We propose a fully automated MR screening system that eliminates the need of clinical expertise and equipment, as well as its low-cost and portability make this system more accessible and affordable to affected population in Africa. There are three specific aims to be achieved in phase I of this project. First, we will develop new methods and adapt previously developed methods for automated detection of retinal signs of MR and test them using a retrospective image data. The second aim will focus on interfacing the automated software system with the low-cost and portable retinal camera developed by VisionQuest. Third, we will test our software system on a retrospective image data obtained from diabetic retinopathy patients imaged with our camera to demonstrate the applicability of the software system on images obtained from our camera.