Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia

Abstract

Purpose: To evaluate the performance of a deep learning (DL) algorithm for retinopathy of prematurity (ROP) screening in Nepal and Mongolia.

Design: Retrospective analysis of prospectively collected clinical data.

Participants: Clinical information and fundus images were obtained from infants in 2 ROP screening programs in Nepal and Mongolia.

Methods: Fundus images were obtained using the Forus 3nethra neo (Forus Health) in Nepal and the RetCam Portable (Natus Medical, Inc.) in Mongolia. The overall severity of ROP was determined from the medical record using the International Classification of ROP (ICROP). The presence of plus disease was determined independently in each image using a reference standard diagnosis. The Imaging and Informatics for ROP (i-ROP) DL algorithm was trained on images from the RetCam to classify plus disease and to assign a vascular severity score (VSS) from 1 through 9.

Main outcome measures: Area under the receiver operating characteristic curve and area under the precision-recall curve for the presence of plus disease or type 1 ROP and association between VSS and ICROP disease category.

Results: The prevalence of type 1 ROP was found to be higher in Mongolia (14.0%) than in Nepal (2.2%; P < 0.001) in these data sets. In Mongolia (RetCam images), the area under the receiver operating characteristic curve for examination-level plus disease detection was 0.968, and the area under the precision-recall curve was 0.823. In Nepal (Forus images), these values were 0.999 and 0.993, respectively. The ROP VSS was associated with ICROP classification in both datasets (P < 0.001). At the population level, the median VSS was found to be higher in Mongolia (2.7; interquartile range [IQR], 1.3-5.4]) as compared with Nepal (1.9; IQR, 1.2-3.4; P < 0.001).

Conclusions: These data provide preliminary evidence of the effectiveness of the i-ROP DL algorithm for ROP screening in neonatal populations in Nepal and Mongolia using multiple camera systems and are useful for consideration in future clinical implementation of artificial intelligence-based ROP screening in low- and middle-income countries.

Keywords: Artificial intelligence; BW, birth weight; DL, deep learning; Deep learning; GA, gestational age; ICROP, International Classification of Retinopathy of Prematurity; IQR, interquartile range; LMIC, low- and middle-income country; Mongolia; Nepal; ROP, retinopathy of prematurity; RSD, reference standard diagnosis; Retinopathy of prematurity; TR, treatment-requiring; VSS, vascular severity score; i-ROP, Imaging and Informatics for Retinopathy of Prematurity.

Figure 1

Diagnostic accuracy of the Imaging and Informatics for Retinopathy of Prematurity DL algorithm in Nepal and Mongolia. Receiver operating characteristic (ROC) and precision-recall curves in Nepal (n = 391 examinations) and Mongolia (n = 467 examinations) for plus disease and treatment-requiring (TR) retinopathy of prematurity (ROP) diagnosis. AUC-PR = area under the precision-recall curve; AUC-ROC = area under the receiver operating characteristic curve; VSS = vascular severity score.

Figure 2

Box-and-whisker plots showing retinopathy of prematurity (ROP) vascular severity score (VSS) score in Nepal and Mongolia by disease category. The ROP VSS score was associated with disease category (P < 0.001) in both countries and was higher in Mongolia compared with Nepal when associated with disease category (P < 0.001). TR = treatment-requiring.