Automated Detection of Malarial Retinopathy in Digital Fundus Images for Improved Diagnosis in Malawian Children with Clinically Defined Cerebral Malaria

Joshi, Vinayak, Agurto, Carla, Barriga, Simon, Nemeth, Sheila, Soliz, Peter, MacCormick, Ian J, Lewallen, Susan, Taylor, Terrie E and Harding, Simon P ORCID: 0000-0003-4676-1158 (2017) Automated Detection of Malarial Retinopathy in Digital Fundus Images for Improved Diagnosis in Malawian Children with Clinically Defined Cerebral Malaria. Scientific Reports, 7 (1). 42703-.

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Official URL: https://www.nature.com/articles/srep42703

Abstract

Cerebral malaria (CM), a complication of malaria infection, is the cause of the majority of malaria-associated deaths in African children. The standard clinical case definition for CM misclassifies ~25% of patients, but when malarial retinopathy (MR) is added to the clinical case definition, the specificity improves from 61% to 95%. Ocular fundoscopy requires expensive equipment and technical expertise not often available in malaria endemic settings, so we developed an automated software system to analyze retinal color images for MR lesions: retinal whitening, vessel discoloration, and white-centered hemorrhages. The individual lesion detection algorithms were combined using a partial least square classifier to determine the presence or absence of MR. We used a retrospective retinal image dataset of 86 pediatric patients with clinically defined CM (70 with MR and 16 without) to evaluate the algorithm performance. Our goal was to reduce the false positive rate of CM diagnosis, and so the algorithms were tuned at high specificity. This yielded sensitivity/specificity of 95%/100% for the detection of MR overall, and 65%/94% for retinal whitening, 62%/100% for vessel discoloration, and 73%/96% for hemorrhages. This automated system for detecting MR using retinal color images has the potential to improve the accuracy of CM diagnosis.

Item Type:	Article
Uncontrolled Keywords:	Retinal Vessels, Retina, Humans, Malaria, Cerebral, Retinal Hemorrhage, Retinal Diseases, Ophthalmoscopy, ROC Curve, Algorithms, Image Processing, Computer-Assisted, Child, Female, Male
Depositing User:	Symplectic Admin
Date Deposited:	18 Jul 2017 07:24
Last Modified:	19 Jan 2023 06:59
DOI:	10.1038/srep42703
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3008529