June 2013
Volume 54, Issue 15
ARVO Annual Meeting Abstract  |   June 2013
Remote Observer Rapid Cataract Screening Based on Fundus Photo
Author Affiliations & Notes
  • Ann Choi
    University of Illinois Urbana Champaign, Urbana, IL
    The EYE Center, Champaign, IL
  • David Hjelmstad
    The EYE Center, Champaign, IL
    Arizona State University, Metro Phoenix, AZ
  • Jessica Taibl
    University of Illinois Urbana Champaign, Urbana, IL
    The EYE Center, Champaign, IL
  • Kelsey Martin
    The EYE Center, Champaign, IL
  • Samir Sayegh
    The EYE Center, Champaign, IL
  • Footnotes
    Commercial Relationships Ann Choi, None; David Hjelmstad, None; Jessica Taibl, None; Kelsey Martin, None; Samir Sayegh, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 2306. doi:
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    • Get Citation

      Ann Choi, David Hjelmstad, Jessica Taibl, Kelsey Martin, Samir Sayegh; Remote Observer Rapid Cataract Screening Based on Fundus Photo. Invest. Ophthalmol. Vis. Sci. 2013;54(15):2306.

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

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Purpose: Detecting the presence of cataracts can help restore a patient’s vision but also hint at a patient’s diabetic state or other underlying pathologies. Given the huge backlog of cataracts worldwide, rapid determination of the patients in most need for surgery is an important public health task. Consequently, we present a quick and simple method to determine the presence of a cataract based on automated imaging of the retina.

Methods: The method is based on the comparison of a photo of the fundus obtained using a standardized setting of a digital non-mydriatic fundus camera, to that of a similar photo of a non-cataractous eye. The inexperienced observer who has no previous knowledge of cataract pathology or grading systems completes a brief training session. Right to left, preoperative and postoperative eye and eyes from different patients comparisons are included in the training database as these discrimination tasks are all relevant to the goal of referral and allocating resources. Following the training session the observer is tested with a different set of images to determine the effectiveness and efficiency of this method.

Results: The recognition rate of minimally trained technicians was generally quite high (above 95%) with all observers classifying 100 hundred pairs within less than 5 minutes.

Conclusions: Our methodology demonstrates that cataracts can be identified by minimally trained technicians based on the automated acquisition of a fundus photo. This has implications for public health efforts and telemedicine programs that can contribute to eradicate a reversible cause of blindness worldwide. One novel aspect in our methodology is the minimal need for training and the provision of a computerized learning environment where rapid recognition of cataractous states can be learned by an untrained remote observer in less than half a day.

Keywords: 445 cataract • 550 imaging/image analysis: clinical  

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