Risk Factors for Nondiagnostic Imaging in a Real-World Deployment of Artificial Intelligence Diabetic Retinal Examinations in an Integrated Healthcare System: Maximizing Workflow Efficiency Through Predictive Dilation
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Objective: In the pivotal clinical trial that led to Food and Drug Administration De Novo “approval” of the first fully autonomous artificial intelligence (AI) diabetic retinal disease diagnostic system, a reflexive dilation protocol was used. Using real-world deployment data before implementation of reflexive dilation, we identified factors associated with nondiagnostic results. These factors allow a novel predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori to maximize efficiency and patient satisfaction. Methods: Retrospective review of patients who were assessed with autonomous AI at Johns Hopkins Medicine (8/2020 to 5/2021). We constructed a multivariable logistic regression model for nondiagnostic results to compare characteristics of patients with and without diagnostic results, using adjusted odds ratio (aOR). P < .05 was considered statistically significant. Results: Of 241 patients (59% female; median age = 59), 123 (51%) had nondiagnostic results. In multivariable analysis, type 1 diabetes (T1D, aOR = 5.82, 95% confidence interval [CI]: 1.45-23.40, P = .01), smoking (aOR = 2.86, 95% CI: 1.36-5.99, P = .005), and age (every 10-year increase, aOR = 2.12, 95% CI: 1.62-2.77, P < .001) were associated with nondiagnostic results. Following feature elimination, a predictive model was created using T1D, smoking, age, race, sex, and hypertension as inputs. The model showed an area under the receiver-operator characteristics curve of 0.76 in five-fold cross-validation. Conclusions: We used factors associated with nondiagnostic results to design a novel, predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori . This new workflow has the potential to be more efficient than reflexive dilation, thus maximizing the number of at-risk patients receiving their diabetic retinal examinations.
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Eyenuk's commercial launch of EyeArt began in the European Union in 2016 after receiving a Conformité Européenne Mark, when the system was only approved for investigational use in the United States.
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From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [31]
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- Authors
- Shou, B. L., Venkatesh, K., Chen, C., Ghidey, R., Lee, J. H., Wang, J., Channa, R., Wolf, R. M., Abramoff, M. D., & Liu, T. Y. A.
- Affiliations
- Johns Hopkins University (US)
- University of Wisconsin–Madison (US)
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA
- Corresponding author
- Liu, T. Y. Alvin
- ORCID
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2023-10-05
- Volume
- 18
- Issue
- 2
- Pages
- 302-308
- ISSN
- 1932-2968 (ISSN-L)
- PMC
- PMC10973867
- OpenAlex
- W4387392205
- Cited by
- 5
- References
- 21
- Citations by year
- 2025: 5
- Type
- conference abstract
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Diabetes Science and Technology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- diabetic retinopathy, artificial intelligence diagnosis, type 1 diabetes, smoking, age, risk factors, workflow efficiency, field deployment
- MeSH terms
- Artificial Intelligence, Clinical Trials as Topic, Diabetes Mellitus, Type 1, Diabetic Retinopathy, Dilatation, Female, Humans, Male, Middle Aged, Retrospective Studies, Risk Factors, United States, Delivery of Health Care, Integrated, Workflow
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