Evaluation of the Efficacy of Artificial Intelligence–Based Screening for Diabetic Retinopathy in a Predominantly Non-White Population

OBJECTIVE: To evaluate the diagnostic accuracy of Luminetics Core, a Food and Drug Administration–cleared artificial intelligence–based screening system, in the detection of diabetic retinopathy (DR) in a predominately non-White population and identify potential shortcomings in clinical implementation that should be addressed to mitigate disparities in health care. METHODS: Data were acquired via retrospective chart review and included 225 patients with a diagnosis of diabetes mellitus who were screened for DR using the LumineticsCore system (formerly IDx-DR) at the University Medical Center New Orleans (Louisiana). Metrics to assess DR detection efficacy included sensitivity, specificity, positive and negative predictive values, likelihood ratios, and indeterminate screening result rates. Stratified analyses regarding associated medical comorbidities also were performed. Clinic follow-up rates and time frames also were noted per screen result group. RESULTS: The study population had a diverse demographic profile, with 69.0% of subjects identifying as African American, 13.1% Hispanic, 10.7% White, 3.6% Asian, and 3.6% of subjects who either did not identify with the above racial/ethnic groups or declined to self-identify. The system yielded favorable performance measures in the study population regarding detection accuracy. It was found, however, that although most patients with a positive screen had ophthalmology referrals placed, 29.8% of patients with positive screen results did not attend their scheduled ophthalmology visit. CONCLUSIONS: The LumineticsCore system was found to be a reliable screening test for the detection of DR in the study population. The relatively high no-show rate for scheduled ophthalmology referrals in patients with positive screen results, however, sheds light on an implementation system issue in need of further evaluation.