深度学习应用于眼科糖尿病视网膜病变预测【原论文】

深度学习

视网膜病变

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Development and Validation of a Deep Learning Algorithm

for Detection of Diabe tic Retinopathy

in Re tinal Fundus Photographs

Varun Gulshan, PhD; Lily Peng, MD, PhD; Marc Coram, PhD; Martin C. Stumpe, PhD; Derek Wu, BS; Arunachalam Narayanaswamy, PhD;

Subhashini Venugopalan, MS; Kasumi Widner, MS; Tom Madams, MEng; Jorge Cuadros, OD, PhD; Ramasamy Kim, OD, DNB;

Rajiv Raman, MS, DNB; Philip C. Nelson, BS; Jessica L. Mega, MD, MPH; Dale R. Webster, PhD

IMPORTANCE

Deep learning is a family of computational methods that allow an algorithm to

program itself by learning from a large set of examples that demonstrate the desired

behavior, removing the need to specify rules explicitly. Application of these methods to

medical imaging requires further assessment and validation.

OBJECTIVE To apply deep learning to create an algorithm for automated detection of diabetic

retinopathy and diabetic macular edema in retinal fundus photographs.

DESIGN AND SETTING A specific type of neural network optimized for image classification

called a deep convolutional neural network was trained using a retrospective development

data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy,

diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists

and ophthalmology senior residents between May and December 2015. The resultant

algorithm was validated in January and February 2016 using 2 separate data sets, both

graded by at least 7 US board-certified ophthalmologists with high intragrader consistency.

EXPOSURE Deep learning–trained algorithm.

MAIN OUTCOMES AND MEASURES The sensitivity and specificity of the algorithm for detecting

referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy,

referable diabetic macular edema, or both, were generated based on the reference standard

of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2

operating points selected from the development set, one selected for high specificity and

another for high sensitivity.

RESULTS The EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4

years; 62.2% women; prevalence of RDR, 683/8878 fully gradable images [7.8%]); the

Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6% women;

prevalence of RDR, 254/1745 fully gradable images [14.6%]). For detecting RDR, the algorithm

had an area under the receiver operating curve of 0.991 (95% CI, 0.988-0.993) for EyePACS-1 and

0.990 (95% CI, 0.986-0.995) for Messidor-2. Using the first operating cut point with high

specificity, for EyePACS-1, the sensitivity was 90.3% (95% CI, 87.5%-92.7%) and the specificity

was 98.1% (95% CI, 97.8%-98.5%). For Messidor-2, the sensitivity was 87.0% (95% CI, 81.1%-

91.0%) and the specificity was 98.5% (95% CI, 97.7%-99.1%). Using a second operating point

with high sensitivity in the development set, for EyePACS-1 the sensitivity was 97.5% and

specificity was 93.4% and for Messidor-2 the sensitivity was 96.1% and specificity was 93.9%.

CONCLUSIONS AND RELEVANCE In this evaluation of retinal fundus photographs from adults

with diabetes, an algorithm based on deep machine learning had high sensitivity and

specificity for detecting referable diabetic retinopathy. Further research is necessary to

determine the feasibility of applying this algorithm in the clinical setting and to determine

whether use of the algorithm could lead to improved care and outcomes compared with

current ophthalmologic assessment.

JAMA. doi:10.1001/jama.2016.17216

Published online November 29, 2016.

Editorial

Supplemental content

Author Affiliations: Google Inc,

Mountain View, California (Gulshan,

Peng, Coram, Stumpe, Wu,

Narayanaswamy, Venugopalan,

Widner, Madams, Nelson, Webster);

Department of Computer Science,

University of Texas, Austin

(Venugopalan); EyePACS LLC,

San Jose, California (Cuadros); School

of Optometry, Vision Science

Graduate Group, University of

California, Berkeley (Cuadros);

Aravind Medical Research

Foundation, Aravind Eye Care

System, Madurai, India (Kim); Shri

Bhagwan Mahavir Vitreoretinal

Services, Sankara Nethralaya,

Chennai, Tamil Nadu, India (Raman);

Verily Life Sciences, Mountain View,

California (Mega); Cardiovascular

Division, Department of Medicine,

Brigham and Women’s Hospital and

Harvard Medical School, Boston,

Massachusetts (Mega).

Corresponding Author: Lily Peng,

MD, PhD, Google Research, 1600

Amphitheatre Way, Mountain View,

CA 94043 (lhpeng@google.com).

Research

JAMA | Original Investigation | INNOVATIONS IN HEALTH CARE DELIVERY

(Reprinted) E1

Downloaded From: http://jamanetwork.com/ on 12/01/2016

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