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Towards implementation of AI in New Zealand national screening program: Cloud-based, Robust, and Bespoke

Li Xie, Song Yang, David Squirrell, View ORCID ProfileEhsan Vaghefi
doi: https://doi.org/10.1101/823260
Li Xie
1School of Optometry and Vision Sciences, The University of Auckland
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Song Yang
1School of Optometry and Vision Sciences, The University of Auckland
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David Squirrell
2Department of Ophthalmology, The University of Auckland
3Auckland District Health Board, New Zealand
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Ehsan Vaghefi
1School of Optometry and Vision Sciences, The University of Auckland
4Auckland Bioengineering Institute, The University of Auckland
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  • ORCID record for Ehsan Vaghefi
  • For correspondence: e.vaghefi@auckland.ac.nz
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Abstract

Convolutional Neural Networks (CNN)s have become a prominent method of AI implementation in medical classification tasks. Grading Diabetic Retinopathy (DR) has been at the forefront of the development of AI for ophthalmology. However, major obstacles remain in the generalization of these CNN’s onto real-world DR screening programs. We believe these difficulties are due to use of 1) small training datasets (<5,000 images), 2) private and ‘curated’ repositories, 3) offline CNN implementation methods, while 4) relying on accuracy measured as area under the curve (AUC) as the sole measure of CNN performance.

To address these issues, the public EyePACS Kaggle Diabetic Retinopathy dataset was uploaded onto Microsoft Azure™ cloud platform. Two CNNs were trained as a “Quality Assurance”, and a “Classifier”. The “Classifier” CNN performance was then tested both on ‘un-curated’ as well as the ‘curated’ test set created by the “Quality Assessment” CNN. Finally, the sensitivity of the “Classifier” CNNs was boosted post-training using two post-training techniques.

Our “Classifier” CNN proved to be robust, as its performance was similar on ‘curated’ and ‘uncurated’ sets. The implementation of ‘cascading thresholds’ and ‘max margin’ techniques led to significant improvements in the “Classifier” CNN’s sensitivity, while also enhancing the specificity of other grades.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 29, 2019.
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Towards implementation of AI in New Zealand national screening program: Cloud-based, Robust, and Bespoke
Li Xie, Song Yang, David Squirrell, Ehsan Vaghefi
bioRxiv 823260; doi: https://doi.org/10.1101/823260
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Towards implementation of AI in New Zealand national screening program: Cloud-based, Robust, and Bespoke
Li Xie, Song Yang, David Squirrell, Ehsan Vaghefi
bioRxiv 823260; doi: https://doi.org/10.1101/823260

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