Plus Disease in Retinopathy of Prematurity: Development of Composite Images by Quantification of Expert Opinion
Michael F. Chiang 1*,
Rony Gelman 2,
Steven L. Williams 2,
Joo-Yeon Lee 3,
Daniel S. Casper 2,
M. Elena Martinez-Perez 4,
and
John T. Flynn 5
1 Ophthalmology and Biomedical Informatics, Columbia University College of Physicians and Surgeons, 635 West 165th Street, Box 92, New York, New York, 10032, United States; Biomedical Informatics, Columbia Univeresity College of Physicians and Surgeons, New York, New York, United States
2 Ophthalmology, Columbia University College of Physicians and Surgeons, New York, New York, United States
3 Ophthalmology, Columbia University College of Physicians and Surgeons, New York, New York, United States; Ophthalmology, Hallym University College of Medicine, Seoul, Korea, Republic of
4 Computer Science, National Autonomous University of Mexico, Mexico City, Mexico
5 Columbia University/Harkness Eye Institute, New York, New York, United States
* To whom correspondence should be addressed. E-mail: chiang{at}dbmi.columbia.edu.
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Abstract |
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Purpose: To demonstrate a methodology for generating composite wide-angle images of plus disease in retinopathy of prematurity (ROP), using quantitative analysis of expert opinions.
Methods: 34 wide-angle retinal images were independently interpreted by 22 ROP experts as "plus" or "not plus." All images were processed by the computer-based Retinal Image multiScale Analysis (RISA) system to calculate two parameters: arterial integrated curvature (AIC) and venous diameter (VD). Using a reference standard defined by expert consensus, sensitivity and specificity curves were calculated by varying the diagnostic cutoffs for AIC and VD. From these curves, individual vessels from multiple images were identified with particular diagnostic cutoffs, and were combined into composite wide-angle images using graphics editing software.
Results: The values associated with 75% under-diagnosis of true plus disease (i.e. 25% sensitivity cutoff) were AIC 0.061 and VD 4.272, the values associated with 50% under-diagnosis of true plus disease (i.e. 50% sensitivity cutoff) were AIC 0.049 and VD 4.088, and the values associated with 25% under-diagnosis of true plus disease (i.e. 75% sensitivity cutoff) were AIC 0.042 and VD 3.795. Composite wide-angle images were generated by identifying and combining individual vessels with these characteristics.
Conclusions: Computer-based image analysis permits quantification of retinal vascular features, and a spectrum of abnormalities is seen in ROP. Selection of appropriate vessels from multiple images can produce composite plus disease images corresponding to expert opinions. This may be useful for educational purposes, and for development of future disease definitions based on quantitative, objective principles.
Key Words:
retinopathy of prematurity, image analysis, children-s vision, retinal vasculature
