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Randomized Controlled Trial
. 2024 Jan 11;15(1):421.
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

Risa M Wolf  1 , Roomasa Channa  2 , T Y Alvin Liu  3 , Anum Zehra  4 , Lee Bromberger  4 , Dhruva Patel  4 , Ajaykarthik Ananthakrishnan  4 , Elizabeth A Brown  4 , Laura Prichett  5 , Harold P Lehmann  6 , Michael D Abramoff  7   8   9   10   11
Affiliations
Randomized Controlled Trial

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

Risa M Wolf et al. Nat Commun. .

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.

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Conflict of interest statement

M.D.A. reports the following conflicts of interest: Investor, Director, Consultant, Digital Diagnostics Inc., Coralville, Iowa, USA; patents and patent applications assigned to the University of Iowa and Digital Diagnostics that are relevant to the subject matter of this manuscript; Chair Healthcare AI Coalition, Washington DC; member, American Academy of Ophthalmology (AAO) AI Committee; member, AI Workgroup Digital Medicine Payment Advisory Group (DMPAG); member, Collaborative Community for Ophthalmic Imaging (CCOI), Washington DC; Chair, Foundational Principles of AI CCOI Workgroup. R.M.W. reports receiving research support from Boehringer Ingelheim and Novo Nordisk outside the submitted work. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow diagram of patient enrollment and randomization in the ACCESS study (Based on CONSORT guideline 2010 flow diagram).
See this image and copyright information in PMC

References

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