Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity

Redd, Travis K and Campbell, John Peter and Brown, James M and Kim, Sang Jin and Ostmo, Susan and Chan, Robison Vernon Paul and Dy, Jennifer and Erdogmus, Deniz and Ioannidis, Stratis and Kalpathy-Cramer, Jayashree and Chiang, Michael F (2018) Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. British Journal of Ophthalmology . ISSN 0007-1161

Full content URL: https://doi.org/10.1136/bjophthalmol-2018-313156

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Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity
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Abstract

Background Prior work has demonstrated the near-perfect accuracy of a deep learning retinal image analysis system for diagnosing plus disease in retinopathy of prematurity (ROP). Here we assess the screening potential of this scoring system by determining its ability to detect all components of ROP diagnosis.

Methods Clinical examination and fundus photography were performed at seven participating centres. A deep learning system was trained to detect plus disease, generating a quantitative assessment of retinal vascular abnormality (the i-ROP plus score) on a 1–9 scale. Overall ROP disease category was established using a consensus reference standard diagnosis combining clinical and image-based diagnosis. Experts then ranked ordered a second data set of 100 posterior images according to overall ROP severity.

Results 4861 examinations from 870 infants were analysed. 155 examinations (3%) had a reference standard diagnosis of type 1 ROP. The i-ROP deep learning (DL) vascular severity score had an area under the receiver operating curve of 0.960 for detecting type 1 ROP. Establishing a threshold i-ROP DL score of 3 conferred 94% sensitivity, 79% specificity, 13% positive predictive value and 99.7% negative predictive value for type 1 ROP. There was strong correlation between expert rank ordering of overall ROP severity and the i-ROP DL vascular severity score (Spearman correlation coefficient=0.93; p<0.0001).

Conclusion The i-ROP DL system accurately identifies diagnostic categories and overall disease severity in an automated fashion, after being trained only on posterior pole vascular morphology. These data provide proof of concept that a deep learning screening platform could improve objectivity of ROP diagnosis and accessibility of screening.

Keywords:deep learning, retinopathy, computer-aided detection (CAD)
Subjects:B Subjects allied to Medicine > B800 Medical Technology
G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
ID Code:35644
Deposited On:15 Apr 2019 08:55

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