This is a resubmission of a well-developed research proposal to compare human assessment of diabetic retinopathy to that of computer assisted assessment that generated a lot of interest and discussion. The applicant provided a good response to the previous critiques. This proposal has the potential to result in immediate benefits to efficiency of the health care system and reduce overall costs, while having no undesirable effect on patients themselves. There remain concerns about if or how the algorithm will be improved as a result of this study, and questions about intellectual property.

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 inter-observer variability, and expert readers are scarce, costly and have high turnover rate. Computer detection of DR as developed by the applicant has the potential to increase the cost-effectiveness, scalability, reproducibility and sustainability of photoscreening programs. However, skeptics remain within the scientific community and computer detection of DR is unproven in clinical practice. The applicant 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 #
1I01CX000119-01A1
Application #
7791934
Study Section
Neurobiology C (NURC)
Project Start
2009-10-01
Project End
2013-12-31
Budget Start
2009-10-01
Budget End
2011-12-31
Support Year
1
Fiscal Year
2009
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

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