September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Real world, Large-scale Study of Safety and Effectiveness of a Fully-automated Diabetic Retinopathy Screening System
Author Affiliations & Notes
  • Kaushal Solanki
    Eyenuk, Inc., Woodland Hills, California, United States
  • Chaithanya Ramachandra
    Eyenuk, Inc., Woodland Hills, California, United States
  • Sandeep Bhat
    Eyenuk, Inc., Woodland Hills, California, United States
  • Malavika Bhaskaranand
    Eyenuk, Inc., Woodland Hills, California, United States
  • Muneeswar gupta Nittala
    Doheny Eye Institute, Los Angeles, California, United States
  • Srinivas R Sadda
    Doheny Eye Institute, Los Angeles, California, United States
  • Jorge Cuadros
    EyePACS LLC., San Jose, California, United States
  • Footnotes
    Commercial Relationships   Kaushal Solanki, Eyenuk, Inc. (E), Eyenuk, Inc. (P); Chaithanya Ramachandra, Eyenuk, Inc. (E), Eyenuk, Inc. (P); Sandeep Bhat, Eyenuk, Inc. (E), Eyenuk, Inc. (P); Malavika Bhaskaranand, Eyenuk, Inc. (E), Eyenuk, Inc. (P); Muneeswar Nittala, DEI (E); Srinivas Sadda, DEI (E); Jorge Cuadros, EyePACS, LLC (E)
  • Footnotes
    Support  Research reported in this publication was supported by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R44EB013585. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 1720. doi:
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      Kaushal Solanki, Chaithanya Ramachandra, Sandeep Bhat, Malavika Bhaskaranand, Muneeswar gupta Nittala, Srinivas R Sadda, Jorge Cuadros; Real world, Large-scale Study of Safety and Effectiveness of a Fully-automated Diabetic Retinopathy Screening System. Invest. Ophthalmol. Vis. Sci. 2016;57(12):1720.

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

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Abstract

Purpose : In this study, we evaluate the fully-automated DR screening system, EyeArt version 2.0, on a large (30,314) consecutive patient cases dataset from EyePACS telescreening setup. Current manual DR screening setups cannot scale to triage the ever-growing diabetic population at risk of vision loss. EyeArt can meet this growing need with automated, cloud-based screening.

Methods : EyeArt’s hybrid approach combines novel morphological image analysis with state-of-the-art deep learning techniques to create a robust DR screening system engineered for large scale deployment on the cloud. EyeArt automatically analyzes multiple fundus images of a patient to generate patient-level DR screening recommendation, while flagging and excluding external eye images and poor quality images.
182,142 color retinal fundus images of 30,314 diabetic patient visits or encounters from the EyePACS setup were automatically analyzed by EyeArt 2.0 in under 15 hours. The encounters had 1-17 images including external eye images. EyePACS human graders provided the ICDR severity level and Clinically Significant Macular Edema (CSME) surrogate markers present/absent for use as reference standard to evaluate EyeArt’s DR screening recommendations. A patient was deemed non-referable if there was mild or no signs of DR and no CSME surrogate markers in both eyes

Results : In the 30,314 encounters, 19.0% of encounters had moderate NPDR or higher or had surrogate markers for CSME and 5.2% of encounters had potentially treatable DR (severe NPDR or PDR). EyeArt’s screening sensitivity was 92.3% (95% CI: 91.6 - 93.0%) with specificity of 90.4% (95% CI: 90.0 - 90.8%). This corresponds to 7,488 “refer” cases and 438 false negatives out of which 93.2% did not meet the treatment criteria (i.e. had moderate NPDR). The AUROC was 0.958 (95% CI: 0.955 – 0.960). EyeArt’s sensitivity for detecting potentially treatable DR was 98.1%. 719 cases (2.5%) deemed non-screenable due to lack of at least 2 gradable images were given “refer” recommendations and included in results

Conclusions : EyeArt 2.0 automated system has high screening sensitivity and specificity on a large real world dataset making it safe and effective for DR screening.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

Fig1: Example patient episodes from the EyePACS telescreening setup.

Fig1: Example patient episodes from the EyePACS telescreening setup.

 

Fig2: ROC plot for EyeArt 2.0 to identify referable DR cases on a large 30,314 consecutive patient visits dataset from EyePACS.

Fig2: ROC plot for EyeArt 2.0 to identify referable DR cases on a large 30,314 consecutive patient visits dataset from EyePACS.

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