ABSTRACT
PURPOSE: Nowadays, complex digital imaging systems allow detailed retinal imaging without dilating patients' pupils. These so-called non-mydriatic cameras have advantages in common circumstances (eg, for screening or emergency purposes) but present limitations in terms of image quality and field of view. We compare the usefulness of two non-mydriatic camera systems (ie, a handheld versus a stand-alone device) for fundus imaging. The primary outcome was image quality. The secondary outcomes were learning effects and quality grade-influencing factors. METHODS: The imaging procedures followed standard protocol and were all performed by the same investigator. Camera 1 (DRS®) was a stand-alone system, while Camera 2 (Smartscope® PRO) was a mobile system. In order to evaluate possible learning effects, we selected an examiner with no prior training in the use of these systems. The images were graded separately by two experienced and "blinded" ophthalmologists following a defined protocol. RESULTS: In total, 211 people were enrolled. Quality grade comparisons showed significantly better grades for Camera 1. Both systems achieved better quality grades for macular images than for disc-centered images. No remarkable learning effects could be demonstrated. CONCLUSIONS: Both camera systems are useful for fundus imaging. The greater mobility of Camera 2 was associated with lower image quality. For screening scenarios or telemedicine, it must be determined whether image quality or mobility is more important.
ABSTRACT
PURPOSE: Abnormalities of blood vessel anatomy, morphology, and ratio can serve as important diagnostic markers for retinal diseases such as AMD or diabetic retinopathy. Large cohort studies demand automated and quantitative image analysis of vascular abnormalities. Therefore, we developed an analytical software tool to enable automated standardized classification of blood vessels supporting clinical reading. METHODS: A dataset of 61 images was collected from a total of 33 women and 8 men with a median age of 38 years. The pupils were not dilated, and images were taken after dark adaption. In contrast to current methods in which classification is based on vessel profile intensity averages, and similar to human vision, local color contrast was chosen as a discriminator to allow artery vein discrimination and arterial-venous ratio (AVR) calculation without vessel tracking. RESULTS: With 83% ± 1 standard error of the mean for our dataset, we achieved best classification for weighted lightness information from a combination of the red, green, and blue channels. Tested on an independent dataset, our method reached 89% correct classification, which, when benchmarked against conventional ophthalmologic classification, shows significantly improved classification scores. CONCLUSIONS: Our study demonstrates that vessel classification based on local color contrast can cope with inter- or intraimage lightness variability and allows consistent AVR calculation. We offer an open-source implementation of this method upon request, which can be integrated into existing tool sets and applied to general diagnostic exams.
