Telemedicine Follow-Up for Intravitreal Bevacizumab Injection in the Stanford University Network for Diagnosis of Retinopathy of Prematurity (SUNDROP) Cohort

Variability in Plus Disease Diagnosis using Single and Serial Images

PURPOSE

To assess changes in retinopathy of prematurity (ROP) diagnosis in single and serial retinal images.

DESIGN

Cohort study.

PARTICIPANTS

Cases of ROP recruited from the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) consortium evaluated by 7 graders.

METHODS

Seven ophthalmologists reviewed both single and 3 consecutive serial retinal images from 15 cases with ROP, and severity was assigned as plus, preplus, or none. Imaging data were acquired during routine ROP screening from 2011 to 2015, and a reference standard diagnosis was established for each image. A secondary analysis was performed using the i-ROP deep learning system to assign a vascular severity score (VSS) to each image, ranging from 1 to 9, with 9 being the most severe disease. This score has been previously demonstrated to correlate with the International Classification of ROP. Mean plus disease severity was calculated by averaging 14 labels per image in serial and single images to decrease noise.

MAIN OUTCOME MEASURES

Grading severity of ROP as defined by plus, preplus, or no ROP.

RESULTS

Assessment of serial retinal images changed the grading severity for > 50% of the graders, although there was wide variability. Cohen's kappa ranged from 0.29 to 1.0, which showed a wide range of agreement from slight to perfect by each grader. Changes in the grading of serial retinal images were noted more commonly in cases of preplus disease. The mean severity in cases with a diagnosis of plus disease and no disease did not change between single and serial images. The ROP VSS demonstrated good correlation with the range of expert classifications of plus disease and overall agreement with the mode class (P = 0.001). The VSS correlated with mean plus disease severity by expert diagnosis (correlation coefficient, 0.89). The more aggressive graders tended to be influenced by serial images to increase the severity of their grading. The VSS also demonstrated agreement with disease progression across serial images, which progressed to preplus and plus disease.

CONCLUSIONS

Clinicians demonstrated variability in ROP diagnosis when presented with both single and serial images. The use of deep learning as a quantitative assessment of plus disease has the potential to standardize ROP diagnosis and treatment.

Effective field of view of wide-field fundus photography in the Stanford University Network for Diagnosis of Retinopathy of Prematurity (SUNDROP)

Five-field 130° wide-angle imaging is the standard of care for retinopathy of prematurity (ROP) screening with an ideal hypothetical composite field-of-view (FOV) of 180°. We hypothesized that in many real-world scenarios the effective composite FOV is considerably less than ideal. This observational retrospective study analyzed the effective FOV of fundus photos of patients screened for ROP as part of the Stanford University Network for Diagnosis of Retinopathy of Prematurity (SUNDROP) initiative. Five fundus photos were selected from each eye per image session. Effective FOV was defined as the largest circular area centered on the optic disc that encompassed retina in each of the four cardinal views. Seventy-three subjects were analyzed, 35 without ROP and 34 with ROP. Mean effective FOV was 144.55 ± 6.62° ranging from 130.00 to 153.71°. Effective FOV was not correlated with the presence or absence of ROP, gestational age, birth weight, or postmenstrual age. Mean effective FOV was wider in males compared to females. Standard five-field 130° fundus photos yielded an average effective FOV of 144.54° in the SUNDROP cohort. This implies that an imaging FOV during ROP screening considerably less than the hypothetical ideal of 180° is sufficient for detecting treatment warranted ROP.

Use of Telemedicine for subspecialty support in the NICU setting

The regionalization of neonatal care was implemented with an overarching goal to improve neonatal outcomes.¹ This led to centralized neonatal care in urban settings that jeopardized the sustainability of the community level 2 and level 3 Neonatal Intensive Care Units (NICU) in medically underserved areas.² Coupled with pediatric subspecialist and allied health professional workforce shortages, regionalization resulted in disparate and limited access to subspecialty care.^3-6 Innovative telemedicine technologies may offer an alternative and powerful care model for infants in geographically isolated and underserved areas. This chapter describes how telemedicine offerings of remote pediatric subspecialty and specialized programs may bridge gaps of access to specialized care and maintain the clinical services in community NICUs.