Journal article
Effectiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model
NPJ digital medicine, Vol.6(1), 53
03/27/2023
DOI: 10.1038/s41746-023-00785-z
PMCID: PMC10042864
PMID: 36973403
Abstract
The effectiveness of using artificial intelligence (AI) systems to perform diabetic retinal exams ('screening') on preventing vision loss is not known. We designed the Care Process for Preventing Vision Loss from Diabetes (CAREVL), as a Markov model to compare the effectiveness of point-of-care autonomous AI-based screening with in-office clinical exam by an eye care provider (ECP), on preventing vision loss among patients with diabetes. The estimated incidence of vision loss at 5 years was 1535 per 100,000 in the AI-screened group compared to 1625 per 100,000 in the ECP group, leading to a modelled risk difference of 90 per 100,000. The base-case CAREVL model estimated that an autonomous AI-based screening strategy would result in 27,000 fewer Americans with vision loss at 5 years compared with ECP. Vision loss at 5 years remained lower in the AI-screened group compared to the ECP group, in a wide range of parameters including optimistic estimates biased toward ECP. Real-world modifiable factors associated with processes of care could further increase its effectiveness. Of these factors, increased adherence with treatment was estimated to have the greatest impact.
Details
- Title: Subtitle
- Effectiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model
- Creators
- Roomasa Channa - University of Wisconsin–MadisonRisa M Wolf - Johns Hopkins MedicineMichael D Abràmoff - Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USAHarold P Lehmann - Johns Hopkins University
- Resource Type
- Journal article
- Publication Details
- NPJ digital medicine, Vol.6(1), 53
- DOI
- 10.1038/s41746-023-00785-z
- PMID
- 36973403
- PMCID
- PMC10042864
- NLM abbreviation
- NPJ Digit Med
- eISSN
- 2398-6352
- Grant note
- 5K23EY030911-03 / U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI) R01EY033233 / U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI)
- Language
- English
- Date published
- 03/27/2023
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Fraternal Order of Eagles Diabetes Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984381025202771
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