Elsevier

Ophthalmology

Volume 124, Issue 7, July 2017, Pages 962-969
Ophthalmology

Original article
Automated Identification of Diabetic Retinopathy Using Deep Learning

https://doi.org/10.1016/j.ophtha.2017.02.008Get rights and content

Purpose

Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The objective of this study was to develop robust diagnostic technology to automate DR screening. Referral of eyes with DR to an ophthalmologist for further evaluation and treatment would aid in reducing the rate of vision loss, enabling timely and accurate diagnoses.

Design

We developed and evaluated a data-driven deep learning algorithm as a novel diagnostic tool for automated DR detection. The algorithm processed color fundus images and classified them as healthy (no retinopathy) or having DR, identifying relevant cases for medical referral.

Methods

A total of 75 137 publicly available fundus images from diabetic patients were used to train and test an artificial intelligence model to differentiate healthy fundi from those with DR. A panel of retinal specialists determined the ground truth for our data set before experimentation. We also tested our model using the public MESSIDOR 2 and E-Ophtha databases for external validation. Information learned in our automated method was visualized readily through an automatically generated abnormality heatmap, highlighting subregions within each input fundus image for further clinical review.

Main Outcome Measures

We used area under the receiver operating characteristic curve (AUC) as a metric to measure the precision–recall trade-off of our algorithm, reporting associated sensitivity and specificity metrics on the receiver operating characteristic curve.

Results

Our model achieved a 0.97 AUC with a 94% and 98% sensitivity and specificity, respectively, on 5-fold cross-validation using our local data set. Testing against the independent MESSIDOR 2 and E-Ophtha databases achieved a 0.94 and 0.95 AUC score, respectively.

Conclusions

A fully data-driven artificial intelligence–based grading algorithm can be used to screen fundus photographs obtained from diabetic patients and to identify, with high reliability, which cases should be referred to an ophthalmologist for further evaluation and treatment. The implementation of such an algorithm on a global basis could reduce drastically the rate of vision loss attributed to DR.

Section snippets

Methods

Figure 1A represents an abstraction of our algorithmic pipeline. We compiled and preprocessed fundus images across various sources into a large-scale data set. Our deep learning network learned data-driven features from this data set, characterizing DR based on an expert-labelled ground truth. These deep features were propagated (along with relevant metadata) into a tree-based classification model that output a final, actionable diagnosis.

Results

We tested the model using 5-fold stratified cross-validation on our local data set of 75 137 images, preserving the percentage of samples of each class per fold. This testing procedure trained 5 separate models, each holding out a distinct validation bucket of approximately 15 000 images. Average metrics were derived from 5 test runs on respective held-out data by comparing the model's predictions with the gold standard determined by the panel of specialists. A final, complete model was trained

Discussion

This study proposed a novel automated-feature learning approach to DR detection using deep learning methods. It provides a robust solution for DR detection within a large-scale data set, and the results attained indicate the high efficacy of our computer-aided model in providing efficient, low-cost, and objective DR diagnostics without depending on clinicians to examine and grade images manually. Our method also does not require any specialized, inaccessible, or costly computer equipment to

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    Supplemental material available at www.aaojournal.org.

    Financial Disclosure(s): The author(s) have made the following disclosure(s): R.G.: Patent - (Patent Application Number: 62383333); Patent Filing date: September 2, 2016.

    Author Contributions:

    Conception and design: Gargeya, Leng

    Analysis and interpretation: Gargeya, Leng

    Data collection: Gargeya, Leng

    Obtained funding: none

    Overall responsibility: Gargeya, Leng

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