Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial

Risa M Wolf; Roomasa Channa; T Y Alvin Liu; Anum Zehra; Lee Bromberger; Dhruva Patel; Ajaykarthik Ananthakrishnan; Elizabeth A Brown; Laura Prichett; Harold P Lehmann; Michael D Abramoff

doi:10.1038/s41467-023-44676-z

Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial

Nat Commun. 2024 Jan 11;15(1):421. doi: 10.1038/s41467-023-44676-z.

Authors

Risa M Wolf¹, Roomasa Channa², T Y Alvin Liu³, Anum Zehra⁴, Lee Bromberger⁴, Dhruva Patel⁴, Ajaykarthik Ananthakrishnan⁴, Elizabeth A Brown⁴, Laura Prichett⁵, Harold P Lehmann⁶, Michael D Abramoff^{7

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Affiliations

¹ Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA. [email protected].
² Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA.
³ Wilmer Eye Institute at the Johns Hopkins School of Medicine, Baltimore, MD, USA.
⁴ Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
⁵ Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core, Baltimore, MD, USA.
⁶ Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA.
⁷ Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA.
⁸ Digital Diagnostics Inc, Coralville, IA, USA.
⁹ Iowa City VA Medical Center, Iowa City, IA, USA.
¹⁰ Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA.
¹¹ Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA.

Abstract

Diabetic retinopathy can be prevented with screening and early detection. We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates in a racially and ethnically diverse youth population. AI for Children's diabetiC Eye ExamS (NCT05131451) is a parallel randomized controlled trial that randomized youth (ages 8-21 years) with type 1 and type 2 diabetes to intervention (autonomous artificial intelligence diabetic eye exam at the point of care), or control (scripted eye care provider referral and education) in an academic pediatric diabetes center. The primary outcome was diabetic eye exam completion rate within 6 months. The secondary outcome was the proportion of participants who completed follow-through with an eye care provider if deemed appropriate. Diabetic eye exam completion rate was significantly higher (100%, 95%CI: 95.5%, 100%) in the intervention group (n = 81) than the control group (n = 83) (22%, 95%CI: 14.2%, 32.4%)(p < 0.001). In the intervention arm, 25/81 participants had an abnormal result, of whom 64% (16/25) completed follow-through with an eye care provider, compared to 22% in the control arm (p < 0.001). Autonomous AI increases diabetic eye exam completion rates in youth with diabetes.

Publication types

Randomized Controlled Trial

MeSH terms

Adolescent
Artificial Intelligence
Child
Diabetes Mellitus, Type 2*
Diabetic Retinopathy* / diagnosis
Follow-Up Studies
Humans
Referral and Consultation

Abstract

Publication types

MeSH terms

Grants and funding