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

• Published in: Journal of diabetes science and technology Vol. 18; no. 2; pp. 302 - 308
• Main Authors: Shou, Benjamin L., Venkatesh, Kesavan, Chen, Chang, Ghidey, Ronel, Lee, Jae Hyoung, Wang, Jiangxia, Channa, Roomasa, Wolf, Risa M., Abramoff, Michael D., Liu, T. Y. Alvin
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.03.2024
• Subjects: Artificial Intelligence; Clinical Trials as Topic; Delivery of Health Care, Integrated; Diabetes Mellitus, Type 1; Diabetic Retinopathy - diagnostic imaging; Dilatation; Female; Humans; Male; Middle Aged; Retrospective Studies; Risk Factors; Special Section: Algorithms for Predicting Complications of Diabetes; United States; Workflow; United States; diabetic retinal disease; pupillary dilation; deep learning; clinical decision support; nondiagnostic examination; machine learning; artificial intelligence; implementation science
• ISSN: 1932-2968; 1932-3107; 1932-3107
• DOI: 10.1177/19322968231201654

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.