5.1Introduction

The application of computer-based imaging algorithms to the diagnosis of human disease is already a reality, used routinely today in radiology, mammography, and pathology [1–3]. Recent advances in the imaging of the eye, in particular nonmydriatic and cross-sectional images of the retina, now provide high-quality digital data to diagnose and quantify features of many diseases, including diabetic retinopathy (DR). The potential of these imaging methods is clear. New computer-based systems and diagnostic algorithms hold the promise of producing low-cost, potentially automated, diagnostic imaging systems for managing diseases like DR on a societal scale.

T.P. Karnowski ( )

Oak Ridge National Laboratory,

Oak Ridge, TN 37831, USA

Y. Li

Department of Ophthalmology,

University of Tennessee Health Science Center,

Memphis, TN 38163, USA

L. Giancardo • D. Aykac • K.W. Tobin

Oak Ridge National Laboratory,

Oak Ridge, TN 37831, USA

E. Chaum

Departments of Ophthalmology and Biomedical

Engineering, University of Tennessee Health Science

Center, Memphis, TN 38163, USA

An estimated 285 million people, 6.4% of the world’s adult population, are living with diabetes in 2010. This number is expected to grow to more than 366 million people worldwide by 2030. Each year, seven million more people develop diabetes, and the International Diabetes Federation estimates that by 2030, an additional 472 million people will have impaired glucose tolerance (“prediabetes”). Soon, effective management of DR will require that we screen more than 1 million patients every day, worldwide.

The World Health Organization (WHO) has published organizing principles for delivering care across a spectrum of health-care infrastructures and economies worldwide that includes access to and accuracy of examinations, implementation of evolving telemedical photographic systems with the potential for expert interpretation, real-time management of digital images, and improving follow-up examination compliance [4]. Proposed metrics for assessing the efficacy of new technologies in DR screening include identifying any level of disease versus real-time stratification and management, validating new methods by performance against standard reading center or clinical exam models, and determining performance and outcomes across different health delivery systems, among others.

The WHO has recommended the use of the International Clinical Classification of Diabetic Retinopathy grading as an acceptable minimum standard for DR screening programs to establish

a threshold for treatment referral [5]. Although a dilated seven-standard field examination is the accepted gold standard, the WHO proposal notes that the performance of one or two field photographic systems is similar to or better than exams by general ophthalmologists and other eye care providers [6]. Adequate levels of accuracy for the purpose of screening for any level of retinopathy can be achieved using current photographic systems, and these systems appear to perform at least as well as many health-care providers.

Visual disability and blindness have a profound socioeconomic impact upon the diabetic population, and DR is the leading cause of new blindness in working-age adults in the industrialized world. Currently, 80% of people with diabetes live in lowand middle-income countries with less developed health-care delivery models and services. Almost 20 years ago, it was estimated that as much as $167 million dollars and 71–85,000 sight-years could be saved annually in the USA alone with improved screening methods for diabetic retinopathy [7]. Since the publication of that report, the prevalence of diabetes in the USA [8] has more than doubled to over 21 million patients. The effective implementation of inexpensive, broad-based, screening programs for DR would have a significant impact on the economic and social consequences of vision loss from this disease. We can treat DR; our challenge lies in finding an efficient and cost-effective approach to population-based screening and disease management on an international scale to identify, follow, and appropriately refer those who require treatment.

5.1.1Automated, Remote Image Analysis of Retinal Diseases

The objectives of automated image analysis for DR screening have not yet been met for several reasons. For example, the presence of identifiable lesions may not predict vision-threatening disease; dot/blot hemorrhages are commonly present in the absence of macular edema. The location of lesions relative to the fovea is important but has not previously been considered in diagnostic

algorithms. Conversely, important clinical data (“metadata”) such as the type and duration of diabetes, historical hemoglobin A1C values (an indicator of chronic blood sugar control), and previous history of laser treatment, which may be relevant to the status of retinopathy and risk of disease progression, are not utilized at all in pure image-based diagnostic algorithms. The optimal imaging method would not only screen for visionthreatening lesions in the central retina including the macula and optic nerve but would also detect and quantify the nature, location, and extent of retinal pathology. It would also determine whether the DR was visually significant by its location relative to the fovea, and generate a real-time diagnosis using clinically relevant metadata in the overall risk analysis.

5.1.2Telehealth

Telehealth (telemedicine) can be defined as the delivery of health care to patients at a distance. Current telehealth protocols include the collection and storage of digital health information for later review, the transfer of patient data in real time, and distance video conferencing, to name a few. The goal of telehealth is to provide care to patients in areas where specific types of medical care resources are scarce or absent. Ocular telehealth using digital images has proven to be an accurate and reliable method of consultation for patients with DR and demonstrates that telehealth is a valid solution for delivery of ophthalmic care [9–11].

Ocular telehealth (teleophthalmology) networks are already in place in the Veterans Administration hospital system and elsewhere and are being used for the management of DR [12]. In recent years, the American Telemedicine Association conferences have hosted telehealth papers on digital imaging calibration, outcomes measures, real-time analysis, and imaging standards for DR screening. As a new technology, telehealth has been promoted as an efficient and cost-effective way to deliver health care; however, reports in the literature that address cost savings have, as of yet, presented insufficient data to determine if telehealth is truly cost-effective.