Expert Diagnosis of Plus Disease in Retinopathy of Prematurity From Computer-Based Image Analysis (original) (raw)
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Computer-automated quantification of plus disease in retinopathy of prematurity
Journal of American Association for Pediatric Ophthalmology and Strabismus, 2003
Background: In some cases of retinopathy of prematurity (ROP), it difficult to determine with certainty whether plus disease is present or absent. We have developed a computer program that captures digital images from a video-indirect ophthalmoscope, identifies and traces the major posterior pole blood vessels, measures the dilation and tortuosity of each vessel, and calculates whether or not an eye has plus disease. Our purpose was to determine the accuracy of the computer program in comparison with two masked examiners. Methods: A representative sample of posterior pole images from 20 premature infants, 10 normal and 10 representing various degrees of dilation and tortuosity, was extracted from our video database and analyzed by the computer program as well as by two masked examiners experienced in the diagnosis of ROP. The standard photograph from the Cryotherapy for ROP study, representing the minimum degree of dilation and tortuosity required for plus disease, was also digitized, analyzed, and used as a numeric comparison for the automated determination of plus disease. Results: Of the five images determined to have plus disease by both examiners, four were calculated to have plus disease by the computer program (80% sensitivity). Of the 11 images without plus disease, 10 were calculated not to have plus disease by the computer program (91% specificity). Conclusions: Our computer program has very good sensitivity and specificity compared with masked examiners' determination of the presence or absence of plus disease. Automated analysis of dilation and tortuosity of posterior pole blood vessels has the potential to remove subjectivity from the determination of plus disease.
Retinopathy of Prematurity-assist: Novel Software for Detecting Plus Disease
Korean Journal of Ophthalmology
To design software with a novel algorithm, which analyzes the tortuosity and vascular dilatation in fundal images of retinopathy of prematurity (ROP) patients with an acceptable accuracy for detecting plus disease. Methods: Eighty-seven well-focused fundal images taken with RetCam were classified to three groups of plus, non-plus, and pre-plus by agreement between three ROP experts. Automated algorithms in this study were designed based on two methods: the curvature measure and distance transform for assessment of tortuosity and vascular dilatation, respectively as two major parameters of plus disease detection. Results: Thirty-eight plus, 12 pre-plus, and 37 non-plus images, which were classified by three experts, were tested by an automated algorithm and software evaluated the correct grouping of images in comparison to expert voting with three different classifiers, k-nearest neighbor, support vector machine and multilayer perceptron network. The plus, pre-plus, and non-plus images were analyzed with 72.3%, 83.7%, and 84.4% accuracy, respectively.
Investigative Opthalmology & Visual Science, 2008
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-Thirty-four 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% underdiagnosis of true plus disease (i.e., 25% sensitivity cutoff) were AIC 0.061 and VD 4.272, the values associated with 50% underdiagnosis of true plus disease (i.e., a 50% sensitivity cutoff) were AIC 0.049 and VD 4.088, and the values associated with 25% underdiagnosis 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 method may be useful for educational purposes, and for development of future disease definitions based on objective, quantitative principles. Retinopathy of prematurity (ROP) is a vasoproliferative disease affecting low-birth-weight infants and is a leading cause of childhood blindness throughout the world. 1-3 The International Classification for ROP (ICROP) provides a universal system for describing the
Plus disease in retinopathy of prematurity: an analysis of diagnostic performance
Transactions of the American Ophthalmological Society, 2007
To measure agreement and accuracy of plus disease diagnosis among retinopathy of prematurity (ROP) experts; and to compare expert performance to that of a computer-based analysis system, Retinal Image multiScale Analysis. Twenty-two recognized ROP experts independently interpreted a set of 34 wide-angle retinal photographs for presence of plus disease. Diagnostic agreement was analyzed. A reference standard was defined based on majority vote of experts. Images were analyzed using individual and linear combinations of computer-based system parameters for arterioles and venules: integrated curvature (IC), diameter, and tortuosity index (TI). Sensitivity, specificity, and receiver operating characteristic areas under the curve (AUC) for plus disease diagnosis were determined for each expert and for the computer-based system. Mean kappa statistic for each expert compared to all others was between 0 and 0.20 (slight agreement) in 1 expert (4.5%), 0.21 and 0.40 (fair agreement) in 3 exper...
Automatic evaluation of Plus Disease in Retinopathy of Prematurity
Applied Mechanics and Materials, Vol. 530-531, 2014
Abnormal dilation and tortuosity of retinal blood vessels are the primary signs of plus disease in retinopathy of prematurity. Timely prognosis could help reduce the delay in treatment and the risk of retinal detachment. Our objectives is to determine whether tortuosity and dilation sufficient for plus disease could be assessed most accurately by considering only arterioles, venules, or both. Tortuosity estimation and width measurement is done using previously proposed methods. Image preprocessing is applied before the two features namely, tortuosity and width of blood vessels are estimated to supply as input parameters for classification using K-means clustering technique. The results are validated by comparing with expert ophthalmologists' ground truths. Performance is evaluated based on measures as sensitivity, specificity, predictive values and accuracy. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy values obtained when considering both the arteriolar tortuosity and venous dilation are 85.86%, 90.74%, 88.76%, 88.28% and 88.50% respectively.
003: Image analysis for retinopathy of prematurity diagnosis
Journal of American Association for Pediatric Ophthalmology and Strabismus, 2009
Purpose-To review findings from the authors' published studies involving telemedicine and image analysis for retinopathy of prematurity (ROP) diagnosis. Methods-Twenty-two ROP experts interpreted a set of 34 wide-angle retinal images for presence of plus disease. For each image, a reference standard diagnosis was defined from expert consensus. A computer-based system was used to measure individual and linear combinations of image parameters for arteries and veins: integrated curvature (IC), diameter, and tortuosity index (TI). Sensitivity, specificity, and receiver operating characteristic areas under the curve (AUC) for plus disease diagnosis were determined for each expert. Sensitivity and specificity curves were calculated for the computer-based system by varying the diagnostic cutoffs for arterial IC and venous diameter. Individual vessels from the original 34 images were identified with particular diagnostic cutoffs, and combined into composite wide-angle images using graphics editing software. Results-Expert sensitivity ranged from 0.308-1.000, specificity from 0.571-1.000, and AUC from 0.784 to 1.000. Among computer system parameters, one linear combination had AUC 0.967, which was greater than that of 18 of 22 (81.8%) experts. Composite computer-generated images were produced using the arterial IC and venous diameter values associated with 75% under-diagnosis of plus disease (ie, 25% sensitivity cutoff), 50% under-diagnosis of plus disease (ie, 50% sensitivity cutoff), and 25% under-diagnosis of plus disease (ie, 75% sensitivity cutoff).
Ophthalmic Surgery, Lasers and Imaging Retina, 2014
BACKGROUND AND OBJECTIVE: Plus disease is the primary indication for retinopathy of prematurity (ROP) treatment, but in borderline cases ophthalmologists may struggle to judge whether it is present. ROPtool is a semi-automated computer program that objectively assesses plus disease by measuring retinal vascular tortuosity and width. This study determined ROPtool’s bedside diagnostic accuracy concurrent with ROP screening. PATIENTS AND METHODS: ROP screening examinations were recorded using Keeler video indirect ophthalmoscopy. A masked operator traced images in ROPtool at the bedside, comparing ROPtool’s plus diagnosis to the examiner’s clinical judgment. RESULTS: Four hundred sixty-four examinations (129 eyes of 65 infants) were performed. ROPtool’s sensitivity, specificity, and area under the receiver operating characteristic curve for plus diagnosis was 71% (CI: 38%–100%), 93% (CI: 89%–98%) and 0.87, and for pre-plus or worse was 68% (CI: 51%–85%), 82% (CI: 77%–86%) and 0.81, res...
Plus Disease in Retinopathy of Prematurity: Quantitative Analysis of Vascular Change
American Journal of Ophthalmology, 2010
Purpose-To examine the relationship between rate of vascular change and plus disease diagnosis. Design-Retrospective observational case-control study. Methods-Wide-angle images were taken bilaterally from 37 infants at 31-33 and 35-37 weeks post-menstrual age (PMA). The semi-automated Retinal Image multiScale Analysis system was used to measure parameters for all arteries and veins: integrated curvature, diameter, and tortuosity index. A reference standard diagnosis (plus vs. not plus) was defined for each eye by consensus of five
Plus Disease in Retinopathy of Prematurity
Ophthalmology, 2016
Objective-To identify patterns of inter-expert discrepancy in plus disease diagnosis in retinopathy of prematurity (ROP). Design-We developed two datasets of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP study, and determined a consensus reference standard diagnosis (RSD) for each image, based on 3 independent image graders and the clinical exam. We recruited 8 expert ROP clinicians to classify these images and compared the distribution of classifications between experts and the RSD. Subjects, Participants, and/or Controls-Images obtained during routine ROP screening in neonatal intensive care units. 8 participating experts with >10 years of clinical ROP experience and >5 peer-reviewed ROP publications. Methods, Intervention, or Testing-Expert classification of images of plus disease in ROP. Main Outcome Measures-Inter-expert agreement (weighted kappa statistic), and agreement and bias on ordinal classification between experts (ANOVA) and the RSD (percent agreement). Results-There was variable inter-expert agreement on diagnostic classifications between the 8 experts and the RSD (weighted kappa 0-0.75, mean 0.30). RSD agreement ranged from 80-94% agreement for the dataset of 100 images, and 29-79% for the dataset of 34 images. However, when images were ranked in order of disease severity (by average expert classification), the pattern of expert classification revealed a consistent systematic bias for each expert consistent with unique cut points for the diagnosis of plus disease and pre-plus disease. The two-way ANOVA model suggested a highly significant effect of both image and user on the average score (P<0.05, adjusted R 2 =0.82 for dataset A, and P< 0.05 and adjusted R 2 =0.6615 for dataset B). Conclusions and Relevance-There is wide variability in the classification of plus disease by ROP experts, which occurs because experts have different "cut-points" for the amounts of vascular abnormality required for presence of plus and pre-plus disease. This has important implications for research, teaching and patient care for ROP, and suggests that a continuous ROP plus disease severity score may more accurately reflect the behavior of expert ROP clinicians, and may better standardize classification in the future.