The Clinical Applications for Automatic Detection of Exudates

Abstract

Nowadays, the retinal imaging technology has been widely used for segmenting and detecting the exudates in diabetic retinopathy patients. Unfortunately, the retinal images in Thailand are poorquality images. Therefore, detecting of exudates in a large number by screening programs, are very expensive in professional time and may cause human error. In this study, the clinical applications for detection of exudates from the poor quality retinal image are presented. An application incorporating function, including retinal color normalization, contrast enhancement, noise removal, color space selection and removal of the optic disc, was also designed to standardize the workflow of retinal analysis. Afterward, detection of exudate based on optimal global thresholding and improved adaptive Otsu's algorithm was applied. Two experiments were conducted to validate the detection performance with local databases and a publicly available DIARETDB1 database. The first experiment showed the average sensitivity, specificity and accuracy of 93.8, 95.3 and 94.9%, respectively. The cross validation results of the second experiment, 60% (53) of the retinal images were used for training and 40% (36) for testing, the sensitivity, specificity and accuracy are 84.2, 85.9 and 85.2%, respectively. This result indicates the proposed clinical application provides an effective tool in the screening of exudates.

Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review

“…Seven studies focused on identifying specific features on a retinal image, such as the presence of hard exudates or vessel bifurcation, which are informative when grading an image for DR 33–39…”

“…27 28 One study used an AI tool to identify macular edema from two-dimensional retinal photographs 29 ; one study used an AI tool to identify, in persons with diabetes, fundus images without DR (ie, normal fundi) 30 ; one study used heat maps to aid with the 'black box' phenomenon in an attempt to understand why an AI model might produce false positives in the context of DR grading 31 and one study used AI to inform the photographer whether images taken with a handheld smartphone without mydriasis were gradable for DR and to assess if this could reduce the number of ungradable images captured. 32 Seven studies focused on identifying specific features on a retinal image, such as the presence of hard exudates or vessel bifurcation, which are informative when grading an image for DR. [33][34][35][36][37][38][39] Two studies reported the use of an AI tool to predict the likelihood of DR progression 40 41 ; one study assessed whether AI-assisted image grading can improve human grading 42 ; two studies assessed the impact of using an AI model on patient flow within DR screening services 43 44 and one study used an AI model to collect DR prevalence data. 45 Two studies had an implementation research focus 46 47 and two studies evaluated the cost-effectiveness of using AI for DR screening.…”