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

Computer Aided Detection of Diabetic Retinopathy (DR) in Veterans with Diabetes DR 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 photoscreening for all veterans with diabetes, using digital cameras and licensed independent practitioners as readers. Expert reading suffers from intra-and interobserver variability, and expert readers are scarce, costly and have high turnover rate. Computer detection of DR as developed by our team has the potential to increase the cost-effectiveness, scalability, reproducibility and sustainability of photoscreening programs. However, scepsis remains within the scientific community and computer detection of DR 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 also perform a cost-effectiveness analysis of DR detection on a large number of veterans.

Agency
National Institute of Health (NIH)
Institute
Veterans Affairs (VA)
Type
Non-HHS Research Projects (I01)
Project #
5I01CX000119-02
Application #
7920185
Study Section
Neurobiology C (NURC)
Project Start
2009-10-01
Project End
2013-12-31
Budget Start
2012-01-01
Budget End
2012-12-31
Support Year
2
Fiscal Year
2012
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