April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
EyeArt: Advanced Image Analysis Tools for Diabetic Retinopathy Screening and Telemedicine Applications
Author Affiliations & Notes
  • Kaushal Solanki
    Eyenuk LLC, Woodland Hills, CA
  • Sandeep Bhat
    Eyenuk LLC, Woodland Hills, CA
  • Chaithanya Ramachandra
    Eyenuk LLC, Woodland Hills, CA
  • Muneeswar Gupta Nittala
    Ophthalmology, USC Keck School of Medicine, Los Angeles, CA
  • Srinivas R Sadda
    Ophthalmology, USC Keck School of Medicine, Los Angeles, CA
  • Footnotes
    Commercial Relationships Kaushal Solanki, Eyenuk LLC (E); Sandeep Bhat, Eyenuk LLC (E); Chaithanya Ramachandra, Eyenuk LLC (E); Muneeswar Gupta Nittala, Eyenuk LLC (F); Srinivas Sadda, Eyenuk LLC (F)
  • Footnotes
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Investigative Ophthalmology & Visual Science April 2014, Vol.55, 5883. doi:
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      Kaushal Solanki, Sandeep Bhat, Chaithanya Ramachandra, Muneeswar Gupta Nittala, Srinivas R Sadda; EyeArt: Advanced Image Analysis Tools for Diabetic Retinopathy Screening and Telemedicine Applications. Invest. Ophthalmol. Vis. Sci. 2014;55(13):5883.

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

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Abstract
 
Purpose
 

Developing and deploying computerized screening algorithms is the only way to screen large, growing diabetic population for preventable blindness due to diabetic retinopathy (DR). To achieve this, we have developed EyeArt, a computerized DR screening system with high diagnostic efficacy, and high generalizability demonstrated via conducting cross-dataset testing. System design includes potential telemedicine integration via web-based application programming interface (API).

 
Methods
 

Our novel retinal color fundus image analysis framework comprises of the following steps: (i) image enhancement that normalizes intensity and other variations, (ii) interest region detection that selects < 1% of pixels for further analysis, (iii) descriptors set computation in a multi-scale framework, (iv) support vector machine classification at pixel, lesions, and image level. The resultant end-to-end DR screening system is engineered to work on the cloud cluster (Amazon Elastic Cloud). We evaluate EyeArt on a large 1200-image public dataset (Messidor) for two different scenarios: (a) detecting any signs of DR, and (b) detecting DR onset as defined by >5 microaneurysms (MA) or >0 hemorrhages (HM). Both “any DR” and “DR onset” were tested in two scenarios. First, we test a 50-50 split of the Messidor dataset allowing comparison with many existing approaches, and second, we conduct cross-dataset test where all 1200 images are tested upon while training on a completely different, much smaller, USC-DEI dataset.

 
Results
 

We achieve AUROC of 0.91 for “any DR” and 0.95 for “DR onset” as shown in Figure 1, which is significantly better than all competing approaches on the same Messidor dataset (Table 1). It also matches human graders’ performance on the same dataset. EyeArt also performs reasonably well (AUROC of 0.86 for “any DR” and 0.91 for “DR onset”) for cross-dataset testing.

 
Conclusions
 

We present EyeArt, a new approach for DR screening that has potential for real-life use with its demonstrated high diagnostic efficacy (improving upon the current state of the art and matching human graders' performance), good generalizability (good cross dataset testing results), explicit invariance to imaging conditions (via illumination normalization), and flexible packaging (web-based API and linkable libraries for telemedicine integration).

 
 
EyeArt diagnostic efficacy compares well to humans and generalizes across datasets.
 
EyeArt diagnostic efficacy compares well to humans and generalizes across datasets.
   
Keywords: 550 imaging/image analysis: clinical • 499 diabetic retinopathy • 549 image processing  
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