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

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: child health (paediatrics); public health; retina; telemedicine.

Figure 1

Distributions of Imaging & Informatics retinopathy of prematurity deep learning (i-ROP DL) vascular severity score in eye examinations with different reference standard diagnoses. Data are shown for 4861 eye examinations. In this data set, a hypothetical referral cut-off score of ‘3’ would effectively exclude 89% of examinations with no or mild ROP, while capturing 94% of examinations with type 1 ROP.

Figure 2

Association between Imaging & Informatics retinopathy of prematurity deep learning (i-ROP DL) vascular severity score and ordered ranking of overall ROP disease severity of 100 images by five experts. In this data set, a hypothetical referral cut-off score of ‘3’ would effectively exclude 94% of cases of no or mild ROP, while capturing 100% of cases of type 1 ROP.