Project Narrative Diabetic retinopathy is a feared complication of diabetes and an important cause of blindness in veterans. The VA, through the Office of Care Coordination, has been rapidly moving to teleretinal screening for all veterans with diabetes, using digital cameras and certified independent licensed practitioners as readers. Expert reading suffers from intra-and interobserver reliability, and expert readers are scarce, costly and have high turnover rate. Computer detection has the potential to increase the cost-effectiveness, scalability and sustainability of teleretinal imaging programs while adding a strong dimension to the reading process. However, there remains scepsis within the scientific community and automated lesion detection is unproven in clinical practice. We will compare the performance of human experts, human experts aided by computer and standalone computer on sensitivity, specificity, time to diagnosis, and perform a cost-effectiveness analysis on automated lesion and diabetic retinopathy detection in a prospective clinical trial on a large number of veterans. Dr. Abramoff is Associate Professor of Ophthalmology at the University of Iowa, and is a leader in studies to develop automated systems for evaluation of retinal disease. He has assembled an expert team to assist in this effort, including ophthalmologists, biostatisticians, and experts in telemedicine. 3

Public Health Relevance

Diabetic retinopathy is a feared complication of diabetes and an important cause of blindness in veterans. The VA, through the Office of Care Coordination, has been rapidly moving to teleretinal screening for all veterans with diabetes, using digital cameras and certified independent licensed practitioners as readers. Expert reading suffers from intra-and interobserver reliability, and expert readers are scarce, costly and have high turnover rate. Computer detection has the potential to increase the cost-effectiveness, scalability and sustainability of teleretinal imaging programs while adding a strong dimension to the reading process. However, there remains scepsis within the scientific community and automated lesion detection is unproven in clinical practice. We will compare the performance of human experts, human experts aided by computer and standalone computer on sensitivity, specificity, time to diagnosis, and perform a cost-effectiveness analysis on automated lesion and diabetic retinopathy detection in a prospective clinical trial on a large number of veterans. Dr. Abramoff is Associate Professor of Ophthalmology at the University of Iowa, and is a leader in studies to develop automated systems for evaluation of retinal disease. He has assembled an expert team to assist in this effort, including ophthalmologists, biostatisticians, and experts in telemedicine. 3

Agency
National Institute of Health (NIH)
Institute
Veterans Affairs (VA)
Type
Non-HHS Research Projects (I01)
Project #
5I01CX000119-03
Application #
8392112
Study Section
Neurobiology C (NURC)
Project Start
2009-10-01
Project End
2013-12-31
Budget Start
2013-01-01
Budget End
2013-12-31
Support Year
3
Fiscal Year
2013
Total Cost
Indirect Cost
Name
Iowa City VA Medical Center
Department
Type
DUNS #
028084333
City
Iowa City
State
IA
Country
United States
Zip Code
52245
Zarei, Kasra; Scheetz, Todd E; Christopher, Mark et al. (2016) Automated Axon Counting in Rodent Optic Nerve Sections with AxonJ. Sci Rep 6:26559
Sohn, Elliott H; van Dijk, Hille W; Jiao, Chunhua et al. (2016) Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus. Proc Natl Acad Sci U S A 113:E2655-64
Hu, Qiao; Abràmoff, Michael D; Garvin, Mona K (2015) Automated construction of arterial and venous trees in retinal images. J Med Imaging (Bellingham) 2:044001
Christopher, Mark; Abràmoff, Michael D; Tang, Li et al. (2015) Stereo Photo Measured ONH Shape Predicts Development of POAG in Subjects With Ocular Hypertension. Invest Ophthalmol Vis Sci 56:4470-9
Xiayu Xu; Kyungmoo Lee; Li Zhang et al. (2015) Stratified Sampling Voxel Classification for Segmentation of Intraretinal and Subretinal Fluid in Longitudinal Clinical OCT Data. IEEE Trans Med Imaging 34:1616-1623
Springelkamp, Henriët; Lee, Kyungmoo; Wolfs, Roger C W et al. (2014) Population-based evaluation of retinal nerve fiber layer, retinal ganglion cell layer, and inner plexiform layer as a diagnostic tool for glaucoma. Invest Ophthalmol Vis Sci 55:8428-38
Abràmoff, Michael D; Mullins, Robert F; Lee, Kyungmoo et al. (2013) Human photoreceptor outer segments shorten during light adaptation. Invest Ophthalmol Vis Sci 54:3721-8
Christopher, Mark; Scheetz, Todd E; Mullins, Robert F et al. (2013) Selection of Phototransduction Genes in Homo sapiens. Invest Ophthalmol Vis Sci 54:5489-96
Trucco, Emanuele; Ruggeri, Alfredo; Karnowski, Thomas et al. (2013) Validating retinal fundus image analysis algorithms: issues and a proposal. Invest Ophthalmol Vis Sci 54:3546-59
Zhang, Li; Lee, Kyungmoo; Niemeijer, Meindert et al. (2012) Automated segmentation of the choroid from clinical SD-OCT. Invest Ophthalmol Vis Sci 53:7510-9

Showing the most recent 10 out of 11 publications