Automated Detection of Malarial Retinopathy in Retinal Fundus Images obtained in Clinical Settings

Cerebral malaria (CM) is a life-threatening clinical syndrome associated with 5-10% of malarial infection cases, most prevalent in Africa. About 23% of cerebral malaria cases are misdiagnosed as false positives, leading to inappropriate treatment and loss of lives. Malarial retinopathy (MR) is a retinal manifestation of CM that presents with a highly specific set of lesions. The detection of MR can reduce the false positive diagnosis of CM and alert physicians to investigate for other possible causes of the clinical symptoms and apply a more appropriate clinical intervention of underlying diseases. In order to facilitate easily accessible and affordable means of MR detection, we have developed an automated software system that detects the retinal lesions specific to MR, whitening and hemorrhages, using retinal color fundus images. The individual lesion detection algorithms were combined into an MR detection model using partial least square classifier. The classifier model was trained and tested on retinal image dataset obtained from 64 patients presenting with clinical signs of CM (44 with MR, 20 without MR). The MR detection model yielded specificity of 92% and sensitivity of 68%, with an AUC of 0.82. The proposed MR detection system demonstrates potential for broad screening of MR and can be integrated with a low-cost and portable retinal camera, to provide a bed-side tool for confirming CM diagnosis.