April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Clinical Impact Of Image Quality Assessment In The Performance Of An Automated Diabetic Retinopathy Screening System
Author Affiliations & Notes
  • Carla Agurto Rios
    Research & Development, VisionQuest Biomedical LLC, Albuquerque, NM
  • E Simon Barriga
    Research & Development, VisionQuest Biomedical LLC, Albuquerque, NM
    Electrical & Computer Engineering, University of New Mexico, Albuquerque, NM
  • Vinayak Joshi
    Research & Development, VisionQuest Biomedical LLC, Albuquerque, NM
  • Jeff Wigdahl
    University of Padua, Padua, Italy
  • Cesar Carranza
    Research & Development, VisionQuest Biomedical LLC, Albuquerque, NM
    Electrical & Computer Engineering, University of New Mexico, Albuquerque, NM
  • Sheila C Nemeth
    Research & Development, VisionQuest Biomedical LLC, Albuquerque, NM
  • Wendall Bauman
    Retina Institute of South Texas, San Antonio, TX
  • Peter Soliz
    Research & Development, VisionQuest Biomedical LLC, Albuquerque, NM
  • Footnotes
    Commercial Relationships Carla Agurto Rios, VisionQuest Biomedical, LLC (E); E Simon Barriga, VisionQuest Biomedical, LLC (E); Vinayak Joshi, VisionQuest Biomedical, LLC (E); Jeff Wigdahl, None; Cesar Carranza, VisionQuest Biomedical, LLC (E); Sheila Nemeth, VisionQuest Biomedical, LLC (E); Wendall Bauman, Retina Institute of South Texas (I); Peter Soliz, VisionQuest Biomedical, LLC (I)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 2285. doi:
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    • Get Citation

      Carla Agurto Rios, E Simon Barriga, Vinayak Joshi, Jeff Wigdahl, Cesar Carranza, Sheila C Nemeth, Wendall Bauman, Peter Soliz; Clinical Impact Of Image Quality Assessment In The Performance Of An Automated Diabetic Retinopathy Screening System. Invest. Ophthalmol. Vis. Sci. 2014;55(13):2285.

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

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Abstract 
 
Purpose
 

To assess the impact on clinical workflow and performance of an automatic image quality (IQ) process as part of a system for diabetic retinopathy (DR) screening in non-mydriatic fundus images.

 
Methods
 

In recent years several software systems for automatic IQ and DR assessment have been developed. This work assesses the clinical impact of image quality in automatic DR screening and proposes optimal IQ rejection rates that optimize performance of the DR screening system. The IQ software detects the presence of shadows and crescents, evaluates the overall image quality, and determines if the images are properly aligned. A case is considered to have sufficient quality for DR evaluation if at least one fovea-centered image from each eye is available. A case that does not comply with the requirement is considered incomplete. Only the images that pass the quality check are processed for DR. We varied threshold on the IQ algorithms to evaluate the impact on the performance of the DR screening system. For each threshold we calculated the number of inadequate cases which would need to be referred for re-imaging and the sensitivity and specificity of the DR screening system.

 
Results
 

We tested the algorithms in 947 images (197 cases). For different IQ thresholds, total case rejection rates vary from 39% to 5%. As this rejection rate decreases, the total number of cases needed referral to retinal examination or re-imaging decreases from 61% to 56%. Based on these numbers the optimal operating point of the system would reject 17% of the cases while achieving a sensitivity of 94% for the detection of DR with a specificity of 67%.

 
Conclusions
 

The impact of IQ indicates a trade-off between the number of cases to be considered inadequate for grading by the algorithm and those incorrectly referred due to poor IQ. The optimal rejection percentage agrees with the 20% of inadequacy rate found in non-mydriatic screenings. Based in our data sample the amount of referrals can be reduced while still achieving high sensitivity for detection of DR. However, the specificity is also reduced.

 
 
Performance of the system at different IQ thresholds
 
Performance of the system at different IQ thresholds
 
Keywords: 499 diabetic retinopathy • 549 image processing • 460 clinical (human) or epidemiologic studies: health care delivery/economics/manpower  
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