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Multicenter Study
. 2020 Oct;4(10):1016-1021.
doi: 10.1016/j.oret.2020.04.022. Epub 2020 May 4.

Variability in Plus Disease Identified Using a Deep Learning-Based Retinopathy of Prematurity Severity Scale

Collaborators, Affiliations
Multicenter Study

Variability in Plus Disease Identified Using a Deep Learning-Based Retinopathy of Prematurity Severity Scale

Rene Y Choi et al. Ophthalmol Retina. 2020 Oct.

Abstract

Purpose: Retinopathy of prematurity is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective, which leads to treatment differences. Our goal was to determine objective differences in the diagnosis of plus disease between clinicians using an automated retinopathy of prematurity (ROP) vascular severity score.

Design: This retrospective cohort study used data from the Imaging and Informatics in ROP Consortium, which comprises 8 tertiary care centers in North America. Fundus photographs of all infants undergoing ROP screening examinations between July 1, 2011, and December 31, 2016, were obtained.

Participants: Infants meeting ROP screening criteria who were diagnosed with plus disease and treatment initiated by an examining physician based on ophthalmoscopic examination results.

Methods: An ROP severity score (1-9) was generated for each image using a deep learning (DL) algorithm.

Main outcome measures: The mean, median, and range of ROP vascular severity scores overall and for each examiner when the diagnosis of plus disease was made.

Results: A total of 5255 clinical examinations in 871 babies were analyzed. Of these, 168 eyes were diagnosed with plus disease by 11 different examiners and were included in the study. The mean ± standard deviation vascular severity score for patients diagnosed with plus disease was 7.4 ± 1.9, median was 8.5 (interquartile range, 5.8-8.9), and range was 1.1 to 9.0. Within some examiners, variability in the level of vascular severity diagnosed as plus disease was present, and 1 examiner routinely diagnosed plus disease in patients with less severe disease than the other examiners (P < 0.01).

Conclusions: We observed variability both between and within examiners in the diagnosis of plus disease using DL. Prospective evaluation of clinical trial data using an objective measurement of vascular severity may help to define better the minimum necessary level of vascular severity for the diagnosis of plus disease or how other clinical features such as zone, stage, and extent of peripheral disease ought to be incorporated in treatment decisions.

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Conflict of interest statement

Conflict of Interest:

Michael F. Chiang is an unpaid member of the Scientific Advisory Board for Clarity Medical Systems, a Consultant for Novartis, and has equity in Inteleretina. R. V. Paul Chan is on the Scientific Advisory Board for Phoenix Technology Group, and a Consultant for Novartis and Alcon. Drs. Chiang, Kalpathy-Cramer, and Campbell receive research funding from Genentech.

Figures

Figure 1.
Figure 1.
Boxplot representing the overall distribution of quantitative vascular severity scores (1–9 scale) of patients diagnosed with plus disease by 11 clinical examiners. Thick black line represents the median. Black dots represent outliers.
Figure 2.
Figure 2.
A, B, Representative examples of wide-angle retinal images and corresponding quantitative vascular severity scores (1–9 scale) from deep learning. Both images correspond to infants diagnosed with plus disease and treated at the time of image acquisition. The image in (A) seems to correspond to less severe disease, which is reflected in the vascular severity scores (upper right), suggesting that the vascular severity score may be a useful quantitative metric for standardizing the diagnosis of plus disease and treatment-requiring retinopathy of prematurity.

Comment in

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