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Multicenter Study
. 2021 Dec 1;148(6):e2021051772.
doi: 10.1542/peds.2021-051772.

Single-Examination Risk Prediction of Severe Retinopathy of Prematurity

Aaron S Coyner  1   2 , Jimmy S Chen  1 , Praveer Singh  3   4 , Robert L Schelonka  5 , Brian K Jordan  5 , Cindy T McEvoy  5 , Jamie E Anderson  1 , R V Paul Chan  6 , Kemal Sonmez  2 , Deniz Erdogmus  7 , Michael F Chiang  8 , Jayashree Kalpathy-Cramer  3   4 , J Peter Campbell  1
Affiliations
Multicenter Study

Single-Examination Risk Prediction of Severe Retinopathy of Prematurity

Aaron S Coyner et al. Pediatrics. .

Abstract

Background and objectives: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Screening and treatment reduces this risk, but requires multiple examinations of infants, most of whom will not develop severe disease. Previous work has suggested that artificial intelligence may be able to detect incident severe disease (treatment-requiring retinopathy of prematurity [TR-ROP]) before clinical diagnosis. We aimed to build a risk model that combined artificial intelligence with clinical demographics to reduce the number of examinations without missing cases of TR-ROP.

Methods: Infants undergoing routine ROP screening examinations (1579 total eyes, 190 with TR-ROP) were recruited from 8 North American study centers. A vascular severity score (VSS) was derived from retinal fundus images obtained at 32 to 33 weeks' postmenstrual age. Seven ElasticNet logistic regression models were trained on all combinations of birth weight, gestational age, and VSS. The area under the precision-recall curve was used to identify the highest-performing model.

Results: The gestational age + VSS model had the highest performance (mean ± SD area under the precision-recall curve: 0.35 ± 0.11). On 2 different test data sets (n = 444 and n = 132), sensitivity was 100% (positive predictive value: 28.1% and 22.6%) and specificity was 48.9% and 80.8% (negative predictive value: 100.0%).

Conclusions: Using a single examination, this model identified all infants who developed TR-ROP, on average, >1 month before diagnosis with moderate to high specificity. This approach could lead to earlier identification of incident severe ROP, reducing late diagnosis and treatment while simultaneously reducing the number of ROP examinations and unnecessary physiologic stress for low-risk infants.

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Conflict of interest statement

POTENTIAL CONFLICT OF INTEREST: Dr Chan is on the Scientific Advisory Board for Phoenix Technology Group (Pleasanton, CA) and is a consultant for Novartis (Basel, Switzerland) and Alcon (Ft Worth, TX). Dr Chiang was previously a consultant for Novartis (Basel, Switzerland) and is an equity owner of Inteleretina (Honolulu, HI). Drs Chiang, Campbell, Chan, and Kalpathy-Cramer receive research support from Genentech. Dr Chan receives research support from Regeneron. The Imaging and Informatics in Retinopathy of Prematurity Deep Learning system has been licensed to Boston Artificial Intelligence laboratories by Oregon Health & Science University, Massachusetts General Hospital, Northeastern University, and the University of Illinois Chicago, which may result in royalties to Drs Chan, Campbell, and Kalpathy-Cramer in the future. Dr Campbell is an unpaid advisor to Boston Artificial Intelligence; the other authors have indicated they have no financial relationships relevant to this article to disclose.

Figures

Figure 1:
Figure 1:. Areas under the precision-recall (PR) and receiver operating characteristics (ROC) curves for the gestational age + vascular severity score model.
The mean ± standard deviation of the (A) AUPR and (B) AUROC, respectively, were 0.35 ± 0.11 and 0.82 ± 0.07 on five-fold cross-validation.
Figure 2:
Figure 2:. Change in maximum inter-eye vascular severity score over time among patients that screened positive, by treatment group.
Among patients that screened positive by the optimal model, patients that developed TR-ROP had higher maximum inter-eye vascular severity scores at every subsequent follow up (p ≤ 0.05).
See this image and copyright information in PMC

Comment in

References

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