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Though screening programs may be limited, maintaining ongoing dialogue and relationships with partners in ROP screening will help ensure that as soon as resources are available, ROP screening and treatment programs can resume. In a recent study, most parents reported being educated about ROP at time of discharge through verbal explanation by providers or written materials, while less than 15% reported learning about ROP from online resources or videos.[35] This method of education requires providers to be educated and frequently updated about ROP--a well described area of improvement for ROP screening in many countries. In a population of parents of ROP patients in Massachusetts and South Carolina, limited understanding of ROP, feeling overwhelmed, and lack of resources to address social stressors significantly limited parents' ability to manage their child's healthcare.[56] Suggestions to increase parent adherence with screening and follow-up include thorough ROP parent education, optimizing insurance coverage, reducing transportation costs, and absences from work excused with appropriate documentation. The 10 Essential Public Health Services Model Challenges to Retinopathy of Prematurity (ROP) Screening and the Need for a Model to Evaluate... ROP is one the leading causes of preventable childhood blindness in the world.[1] In 2010, an estimate of 184,700 preterm infants worldwide developed some degree of ROP, and of these, 53,800 developed visually impairing severe disease (Blencowe et al 2010). [Extracted from the article]

To validate a vascular severity score as an appropriate output for artificial intelligence (AI) Software as a Medical Device (SaMD) for retinopathy of prematurity (ROP) through comparison with ordinal disease severity labels for stage and plus disease assigned by the International Classification of Retinopathy of Prematurity, Third Edition (ICROP3), committee. Validation study of an AI-based ROP vascular severity score. A total of 34 ROP experts from the ICROP3 committee. Two separate datasets of 30 fundus photographs each for stage (0–5) and plus disease (plus, preplus, neither) were labeled by members of the ICROP3 committee using an open-source platform. Averaging these results produced a continuous label for plus (1–9) and stage (1–3) for each image. Experts were also asked to compare each image to each other in terms of relative severity for plus disease. Each image was also labeled with a vascular severity score from the Imaging and Informatics in ROP deep learning system, which was compared with each grader's diagnostic labels for correlation, as well as the ophthalmoscopic diagnosis of stage. Weighted kappa and Pearson correlation coefficients (CCs) were calculated between each pair of grader classification labels for stage and plus disease. The Elo algorithm was also used to convert pairwise comparisons for each expert into an ordered set of images from least to most severe. The mean weighted kappa and CC for all interobserver pairs for plus disease image comparison were 0.67 and 0.88, respectively. The vascular severity score was found to be highly correlated with both the average plus disease classification (CC = 0.90, P < 0.001) and the ophthalmoscopic diagnosis of stage (P < 0.001 by analysis of variance) among all experts. The ROP vascular severity score correlates well with the International Classification of Retinopathy of Prematurity committee member's labels for plus disease and stage, which had significant intergrader variability. Generation of a consensus for a validated scoring system for ROP SaMD can facilitate global innovation and regulatory authorization of these technologies. [ABSTRACT FROM AUTHOR]

Retinopathy of prematurity (ROP) is a retinal vasoproliferative disease that affects premature infants. Despite improvements in neonatal care and management guidelines, ROP remains a leading cause of childhood blindness worldwide. Current screening guidelines are primarily based on two risk factors: birth weight and gestational age; however, many investigators have suggested other risk factors, including maternal factors, prenatal and perinatal factors, demographics, medical interventions, comorbidities of prematurity, nutrition, and genetic factors. We review the existing literature addressing various possible ROP risk factors. Although there have been contradictory reports, and the risk may vary between different populations, understanding ROP risk factors is essential to develop predictive models, to gain insights into pathophysiology of retinal vascular diseases and diseases of prematurity, and to determine future directions in management of and research in ROP. [ABSTRACT FROM AUTHOR]

Purpose: To measure accuracy and speed for detection of vascular progression in retinopathy of prematurity (ROP) from serial images. Two strategies are compared: static side-by-side presentation and dynamic flickering of superimposed image pairs. Design: Prospective comparative study. Methods: Fifteen de-identified, wide-angle retinal image pairs were taken from infants who eventually developed plus disease. Image pairs representing vascular disease progression were taken ≥1 week apart, and control images without progression were taken on the same day. Dynamic flickering pairs were created by digital image registration. Ten experts independently reviewed each image pair on a secure website using both strategies, and were asked to identify progression or state that images were identical. Accuracy and speed were measured, using examination date and ophthalmoscopic findings as a reference standard. Results: Using static images, experts were accurate in a mean (%) ± standard deviation (SD) of 11.4 of 15 (76%) ± 1.7 image pairs. Using dynamic flickering images, experts were accurate in a mean (%) ± SD of 11.3 of 15 (75%) ± 1.7 image pairs. There was no significant difference in accuracy between these strategies (P = .420). Diagnostic speed was faster using dynamic flickering (24.7 ± 8.3 seconds) vs static side-by-side images (40.3 ± 18.3 seconds) (P = .002). Experts reported higher confidence when interpreting dynamic flickering images (P = .001). Conclusions: Retinal imaging provides objective documentation of vascular appearance, with potentially improved ability to recognize ROP progression compared to standard ophthalmoscopy. Speed of identifying vascular progression was faster by review of dynamic flickering image pairs than by static side-by-side images, although there was no difference in accuracy. [Copyright &y& Elsevier]