Abstract
IMPORTANCE: Telemedicine is becoming an increasingly important component of clinical care for retinopathy of prematurity (ROP), but little information exists regarding the role of mosaic photography for ROP telemedicine diagnosis. OBJECTIVE: To examine the potential effect of computer-generated mosaic photographs on the diagnosis and management of ROP. DESIGN, SETTING, AND PARTICIPANTS: In this prospective cohort study performed from July 12, 2011, through September 21, 2015, images were acquired from ROP screening at 8 academic institutions, and ROP experts interpreted 40 sets (20 sets with individual fundus photographs with ≥3 fields and 20 computer-generated mosaic photographs) of wide-angle retinal images from infants with ROP. All experts independently reviewed the 40 sets and provided a diagnosis and management plan for each set presented. MAIN OUTCOMES AND MEASURES: The primary outcome measure was the sensitivity and specificity of the ROP diagnosis by experts that was calculated using a consensus reference standard diagnosis, determined from the diagnosis of fundus photographs by 3 experienced readers in combination with the clinical diagnosis based on ophthalmoscopic examination. Mean unweighted κ statistics were used to analyze the mean intergrader agreement among experts for diagnosis of zone, stage, plus disease, and category. RESULTS: Nine ROP experts (4 women and 5 men) who have been practicing ophthalmology for a mean of 10.8 years (range, 3-24 years) consented to participate. Diagnosis by the mosaic photographs compared with diagnosis by multiple individual photographs resulted in improvements in sensitivity for diagnosis of stage 2 disease or worse (95.9% vs 88.9%; difference, 7.0; 95% CI, 3.5 to 10.5; P = .02), plus disease (85.7% vs 63.5%; difference, 22.2; 95% CI, 7.6 to 36.9; P = .02), and treatment-requiring ROP (84.4% vs 68.5%; difference, 15.9; 95% CI, 0.8 to 31.7; P = .047). With use of the κ statistic, mosaic photographs, compared with multiple individual photographs, resulted in improvements in intergrader agreement for diagnosis of plus disease or not (0.54 vs 0.40; mean κ difference, 0.14; 95% CI, 0.07 to 0.21; P = .004), stage 3 disease or worse or not (0.60 vs 0.52; mean κ difference, 0.06; 95% CI, -0.06 to 0.18; P = .04), and type 2 ROP or not (0.58 vs 0.51; mean κ difference, 0.07; 95% CI, 0.03 to 0.11; P = .04). After viewing the mosaic photographs, experts altered their choice of management in 42 of 180 responses (23.3%; 95% CI, 17.1%-29.5%). CONCLUSIONS AND RELEVANCE: Compared with multiple individual photographs, computer-generated mosaic photographs were associated with improved accuracy of image-based diagnosis for certain categories (eg, plus disease, stage 2 disease or worse, and treatment-requiring ROP) of ROP by experts. It is unclear, however, whether these findings are generalizable, and the results of this study may not be relevant to mosaic grading of other retinal vascular conditions.