June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Efficacy of smartphone-based retinopathy of prematurity tele-screening with and without artificial intelligence
Author Affiliations & Notes
  • Benjamin Young
    Oregon Health & Science University Casey Eye Institute, Portland, Oregon, United States
  • Emily Cole
    University of Michigan W K Kellogg Eye Center, Ann Arbor, Michigan, United States
  • Parag Shah
    Aravind Eye Hospital Coimbatore, Coimbatore, Tamil Nadu, India
  • Susan Ostmo
    Oregon Health & Science University Casey Eye Institute, Portland, Oregon, United States
  • Prema Subramanian
    Aravind Eye Hospital Coimbatore, Coimbatore, Tamil Nadu, India
  • Andrew Tsai
    Singapore National Eye Centre, Singapore, Singapore, Singapore
  • Aaron S Coyner
    Oregon Health & Science University Casey Eye Institute, Portland, Oregon, United States
  • Aditi Gupta
    Oregon Health & Science University Casey Eye Institute, Portland, Oregon, United States
  • Praveer Singh
    Massachusetts General Hospital, Boston, Massachusetts, United States
  • Michael F Chiang
    National Eye Institute, Bethesda, Maryland, United States
  • Jayashree Kalpathy-Cramer
    University of Colorado, Denver, Colorado, United States
  • Robison Vernon Paul Chan
    Illinois Eye and Ear Infirmary, Chicago, Illinois, United States
  • J. Peter Campbell
    Oregon Health & Science University Casey Eye Institute, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Benjamin Young None; Emily Cole None; Parag Shah None; Susan Ostmo None; Prema Subramanian None; Andrew Tsai None; Aaron Coyner None; Aditi Gupta None; Praveer Singh None; Michael Chiang None; Jayashree Kalpathy-Cramer Siloam Vision, Code O (Owner); Robison Chan Siloam Vision, Code O (Owner); J. Peter Campbell Siloam Vision, Code O (Owner)
  • Footnotes
    Support  This work was supported by grants R01 EY019474, R01 EY031331, R21 EY031883, and P30 EY010572 from the National Institutes of Health (Bethesda, MD), and by unrestricted departmental funding and a Career Development Award (JPC) from Research to Prevent Blindness (New York, NY), and with support from the US Agency for International Development (USAID) and the Seva Foundation. The funding organizations had no role in the design or conduct of this research.
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 5123. doi:
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      Benjamin Young, Emily Cole, Parag Shah, Susan Ostmo, Prema Subramanian, Andrew Tsai, Aaron S Coyner, Aditi Gupta, Praveer Singh, Michael F Chiang, Jayashree Kalpathy-Cramer, Robison Vernon Paul Chan, J. Peter Campbell; Efficacy of smartphone-based retinopathy of prematurity tele-screening with and without artificial intelligence. Invest. Ophthalmol. Vis. Sci. 2023;64(8):5123.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Assess the efficacy of two smartphone-based fundus imaging (SBFI) systems used by technicians for retinopathy of prematurity (ROP) screening both by human graders, and also by artificial intelligence-based systems.

Methods : 156 babies were screened as part of an existing ROP tele-ophthalmology program in India. This prospective study compared Retcam images taken in parallel with either the Keeler MIO or MII Retcam systems using two masked readers evaluated zone, stage, plus, and vascular severity scores (VSS, from 1-9) in all images. Then, the MII and MIO images were combined, stratified by patient into training, validation, and test datasets (70%/10%/20%), and used to train a ResNet18 deep learning architecture for binary classification of normal versus pre-plus or plus disease, which were used as patient-level predictions of referral warranted(RW)- and treatment requiring(TR)-ROP. Sensitivity and specificity of detection of RW-ROP, and TR-ROP by both human graders and an AI system, and area under the receiver operating characteristic curve (AUROC) of grader-assigned VSS were calculated compared to diagnosis by Retcam images.

Results : Both SBFI devices were effective at detecting TR-ROP with a sensitivity [95% confidence interval] of 100% [54.1%,100%] for MII Retcam, and 100% [29.2%,100%] for MIO, with specificity of 85.4% [75.0%,91.5%] for MII Retcam and 83.0% [70.2%,91.9%] for MIO. AUROC with grader-assigned VSS only was 0.946 [0.907,0.985] and 0.961 [0.931,0.991] for RW- and TR-ROP, respectively. For the AI system, the sensitivity of detecting TR-ROP sensitivity was 100% [29.2%, 100.0%] and specificity was 58.6% [38.9%, 76.5%], and RW-ROP sensitivity was 80.0% [28.4%, 99.5%] and specificity was 59.3% [38.8%, 77.6%].

Conclusions : Technician-led ROP screening using SBFI devices was 100% sensitive using both human graders and an AI system for treatment-requiring ROP. Using SBFI systems with AI may be a cost- effective method to improve the global capacity for ROP screening.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

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