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Original Investigation
Journal Club
November 2016

Influence of Computer-Generated Mosaic Photographs on Retinopathy of Prematurity Diagnosis and Management

Journal Club PowerPoint Slide Download
Author Affiliations
  • 1Department of Ophthalmology, Weill Cornell Medical College, New York, New York
  • 2Stein Eye Institute, University of California, Los Angeles
  • 3University at Buffalo, State University of New York, Buffalo
  • 4Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago
  • 5Casey Eye Institute, Oregon Health & Science University, Portland
  • 6Bascom Palmer Eye Institute, University of Miami, Miami, Florida
  • 7Associated Retinal Consultants, Oakland University, Royal Oak, Michigan
  • 8Asociación para Evitar la Ceguera en México, Mexico City, Mexico
  • 9Long Island Vitreoretinal Consultants, Long Island, New York
  • 10Department of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
  • 11Department of Ophthalmology, Chang Gung University, College of Medicine, Taoyuan, Taiwan
  • 12Center for Global Health, University of Illinois at Chicago, Chicago
JAMA Ophthalmol. 2016;134(11):1283-1289. doi:10.1001/jamaophthalmol.2016.3625
Key Points

Question  How do computer-generated mosaic photographs affect the diagnosis and management of retinopathy of prematurity (ROP)?

Findings  In a cohort study of 9 ROP experts examining 40 sets of images, use of mosaic photographs compared with multiple individual photographs was associated with improved diagnosis of stage 2 disease or worse, plus disease, and treatment-requiring ROP. The ROP experts also improved their intergrader agreement for the diagnosis of plus disease or not, stage 3 disease or worse or not, and type 2 ROP or not.

Meaning  Mosaic photography may improve the accuracy and reliability of image-based diagnosis of clinically significant ROP.


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.