July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
THE USE OF ARTIFICIAL INTELLIGENCE FOR THE DETECTION OF STARGARDT’S DISEASE FROM FUNDUS IMAGES
Author Affiliations & Notes
  • Saad Y Al-Kadhi
    Department of Ophthalmology , Columbia University College of Physicians and Surgeons, New York, New York, United States
  • Loai Skouti
    Department of Ophthalmology , Columbia University College of Physicians and Surgeons, New York, New York, United States
  • April Ellis
    Department of Ophthalmology , Columbia University College of Physicians and Surgeons, New York, New York, United States
  • Qun Zeng
    Department of Ophthalmology , Columbia University College of Physicians and Surgeons, New York, New York, United States
  • Tarun Sharma
    Department of Ophthalmology , Columbia University College of Physicians and Surgeons, New York, New York, United States
  • Stephen H Tsang
    Department of Ophthalmology , Columbia University College of Physicians and Surgeons, New York, New York, United States
  • Tongalp H Tezel
    Department of Ophthalmology , Columbia University College of Physicians and Surgeons, New York, New York, United States
  • Footnotes
    Commercial Relationships   Saad Al-Kadhi, None; Loai Skouti, None; April Ellis, None; Qun Zeng, None; Tarun Sharma, None; Stephen Tsang, None; Tongalp Tezel, None
  • Footnotes
    Support  Supported by an unrestricted grant from Research to Prevent Blindness, Inc, New York, NY, Slomo and Cindy Silvian Foundation, New York, NY. New York State
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1501. doi:https://doi.org/
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      Saad Y Al-Kadhi, Loai Skouti, April Ellis, Qun Zeng, Tarun Sharma, Stephen H Tsang, Tongalp H Tezel; THE USE OF ARTIFICIAL INTELLIGENCE FOR THE DETECTION OF STARGARDT’S DISEASE FROM FUNDUS IMAGES. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1501. doi: https://doi.org/.

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

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Abstract

Purpose : Developing an artificial intelligence model to diagnose Stargardt’s disease from color fundus photos.

Methods : Color fundus photos of eyes with established diagnosis of Stargardt’s disease (n=25) and normal fundi (n=25) were used to train a Convolutional Neural Network (CNN) containing three convolutional layers followed by two fully connected layers ending with a sigmoid activation function (TensorFlow, https://www.tensorflow.org/). The numeric output the networks’ diagnostic ability was expressed as a percentage value with the smallest corresponding to 0% and the highest to 100%. Values >50% were considered diagnostic for Stargardt’s disease. Sensitivity and specificity of the network’s ability to correctly diagnose Stargardt’s disease was tested against an experienced retina specialist’s clinical assessment.

Results : The model was tested with another set of fundus photos that was not used for the training purposes. The model was able to correctly diagnose fundus photos with Stargardt’s disease (n=5), (values ranging 60.5-93.9%) vs. normal fundi (n=10), (values ranging 15.7-35.3%), (100% specificity and 100% sensitivity).

Conclusions : The deep learning model was able to diagnose correctly fundus images of Stargardt’s disease with 100% specificity and 100% sensitivity.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

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