Plus disease diagnosis in retinopathy of prematurity: vascular tortuosity as a function of distance from optic disk

Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics

The condition known as Plus disease is distinguished by atypical alterations in the retinal vasculature of neonates born prematurely. It has been demonstrated that the diagnosis of Plus disease is subjective and qualitative in nature. The utilization of quantitative methods and computer-based image analysis to enhance the objectivity of Plus disease diagnosis has been extensively established in the literature. This study presents the development of a computer-based image analysis method aimed at automatically distinguishing Plus images from non-Plus images. The proposed methodology conducts a quantitative analysis of the vascular characteristics linked to Plus disease, thereby aiding physicians in making informed judgments. A collection of 76 posterior retinal images from a diverse group of infants who underwent screening for Retinopathy of Prematurity (ROP) was obtained. A reference standard diagnosis was established as the majority of the labeling performed by three experts in ROP during two separate sessions. The process of segmenting retinal vessels was carried out using a semi-automatic methodology. Computer algorithms were developed to compute the tortuosity, dilation, and density of vessels in various retinal regions as potential discriminative characteristics. A classifier was provided with a set of selected features in order to distinguish between Plus images and non-Plus images. This study included 76 infants (49 [64.5%] boys) with mean birth weight of 1305 ± 427 g and mean gestational age of 29.3 ± 3 weeks. The average level of agreement among experts for the diagnosis of plus disease was found to be 79% with a standard deviation of 5.3%. In terms of intra-expert agreement, the average was 85% with a standard deviation of 3%. Furthermore, the average tortuosity of the five most tortuous vessels was significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The curvature values based on points were found to be significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The maximum diameter of vessels within a region extending 5-disc diameters away from the border of the optic disc (referred to as 5DD) exhibited a statistically significant increase in Plus images compared to non-Plus images (p ≤ 0.0001). The density of vessels in Plus images was found to be significantly higher compared to non-Plus images (p ≤ 0.0001). The classifier's accuracy in distinguishing between Plus and non-Plus images, as determined through tenfold cross-validation, was found to be 0.86 ± 0.01. This accuracy was observed to be higher than the diagnostic accuracy of one out of three experts when compared to the reference standard. The implemented algorithm in the current study demonstrated a commendable level of accuracy in detecting Plus disease in cases of retinopathy of prematurity, exhibiting comparable performance to that of expert diagnoses. By engaging in an objective analysis of the characteristics of vessels, there exists the possibility of conducting a quantitative assessment of the disease progression's features. The utilization of this automated system has the potential to enhance physicians' ability to diagnose Plus disease, thereby offering valuable contributions to the management of ROP through the integration of traditional ophthalmoscopy and image-based telemedicine methodologies.

Current Application of Digital Diagnosing Systems for Retinopathy of Prematurity

BACKGROUND AND OBJECTIVE

Retinopathy of prematurity (ROP), a proliferative vascular eye disease, is one of the leading causes of blindness in childhood and prevails in premature infants with low-birth-weight. The recent progress in digital image analysis offers novel strategies for ROP diagnosis. This paper provides a comprehensive review on the development of digital diagnosing systems for ROP to software researchers. It may also be adopted as a guide to ophthalmologists for selecting the most suitable diagnostic software in the clinical setting, particularly for the remote ophthalmic support.

METHODS

We review the latest literatures concerning the application of digital diagnosing systems for ROP. The diagnosing systems are analyzed and categorized. Articles published between 1998 and 2020 were screened with the two searching engines Pubmed and Google Scholar.

RESULTS

Telemedicine is a method of remote image interpretation that can provide medical service to remote regions, and yet requires training to local operators. On the basis of image collection in telemedicine, computer-based image analytical systems for ROP were later developed. So far, the aforementioned systems have been mainly developed by virtue of classic machine learning, deep learning (DL) and multiple machine learning. During the past two decades, various computer-aided systems for ROP based on classic machine learning (e.g. RISA, ROPtool, CAIER) became available and have achieved satisfactory performance. Further, automated systems for ROP diagnosis based on DL are developed for clinical applications and exhibit high accuracy. Moreover, multiple instance learning is another method to establish an automated system for ROP detection besides DL, which, however, warrants further investigation in future.

CONCLUSION

At present, the incorporation of computer-based image analysis with telemedicine potentially enables the detection, supervision and in-time treatment of ROP for the preterm babies.

Accuracy and Reliability of Eye-Based vs Quadrant-Based Diagnosis of Plus Disease in Retinopathy of Prematurity

Importance

Presence of plus disease in retinopathy of prematurity is the most critical element in identifying treatment-requiring disease. However, there is significant variability in plus disease diagnosis. In particular, plus disease has been defined as 2 or more quadrants of vascular abnormality, and it is not clear whether it is more reliably and accurately diagnosed by eye-based assessment of overall retinal appearance or by quadrant-based assessment combining grades of 4 individual quadrants.

Objective

To compare eye-based vs quadrant-based diagnosis of plus disease and to provide insight for ophthalmologists about the diagnostic process.

Design, Setting, and Participants

In this multicenter cohort study, we developed a database of 197 wide-angle retinal images from 141 preterm infants from neonatal intensive care units at 9 academic institutions (enrolled from July 2011 to December 2016). Each image was assigned a reference standard diagnosis based on consensus image-based and clinical diagnosis. Data analysis was performed from February 2017 to September 2017.

Interventions

Six graders independently diagnosed each of the 4 quadrants (cropped images) of the 197 eyes (quadrant-based diagnosis) as well as the entire image (eye-based diagnosis). Images were displayed individually, in random order. Quadrant-based diagnosis of plus disease was made when 2 or more quadrants were diagnosed as indicating plus disease by combining grades of individual quadrants post hoc.

Main Outcomes and Measures

Intragrader and intergrader reliability (absolute agreement and κ statistic) and accuracy compared with the reference standard diagnosis.

Results

Of the 141 included preterm infants, 65 (46.1%) were female and 116 (82.3%) white, and the mean (SD) gestational age was 27.0 (2.6) weeks. There was variable agreement between eye-based and quadrant-based diagnosis among the 6 graders (Cohen κ range, 0.32-0.75). Four graders showed underdiagnosis of plus disease with quadrant-based diagnosis compared with eye-based diagnosis (by McNemar test). Intergrader agreement of quadrant-based diagnosis was lower than that of eye-based diagnosis (Fleiss κ, 0.75 [95% CI, 0.71-0.78] vs 0.55 [95% CI, 0.51-0.59]). The accuracy of eye-based diagnosis compared with the reference standard diagnosis was substantial to near-perfect, whereas that of quadrant-based plus disease diagnosis was only moderate to substantial for each grader.

Conclusions and Relevance

Graders had lower reliability and accuracy using quadrant-based diagnosis combining grades of individual quadrants than with eye-based diagnosis, suggesting that eye-based diagnosis has advantages over quadrant-based diagnosis. This has implications for more precise definitions of plus disease regarding the criterion of 2 or more quadrants, clinical care, computer-based image analysis, and education for all ophthalmologists who manage retinopathy of prematurity.

A computer-aided diagnosis system for plus disease in retinopathy of prematurity with structure adaptive segmentation and vessel based features

Retinopathy of Prematurity (ROP) is a blinding disease affecting the retina of low birth-weight preterm infants. Accurate diagnosis of ROP is essential to identify treatment-requiring ROP, which would help to prevent childhood blindness. Plus disease, which characterizes abnormal twisting, widening and branching of the blood vessels, is a significant symptom of treatment requiring ROP. In this paper, we have developed and evaluated a computer-based analysis system for objective assessment of plus disease in ROP, which best mimics the clinical method of disease diagnosis by identifying unique vessel based features. The proposed system consists of an initial segmentation stage, which will efficiently extract blood vessels of varying width and length by utilizing structure adaptive filtering, connectivity analysis and image fusion. The paper proposes the usage of additional retinal features namely leaf node count and vessel density, to portray the abnormal growth and branching of the blood vessels and to complement the commonly used features namely tortuosity and width. The test results show a better classification of plus disease in terms of sensitivity (95%) and specificity (93%), emphasizing the superiority of the proposed segmentation algorithm and vessel-based features. An additional advantage of the proposed system is that the process of selection of relevant vessels for feature extraction is fully automated, which makes the system highly useful to the non-physician graders, owing to the unavailability of a sufficient number of ROP specialists.

Plus Disease in Retinopathy of Prematurity: More Than Meets the ICROP?

Retinopathy of prematurity (ROP), a vasoproliferative retinal disease affecting premature infants, is a leading cause of childhood blindness throughout the world. Plus disease, defined as venous dilatation and arteriolar tortuosity within the posterior retinal vessels greater than or equal to that of a standard published photograph, is the most critical finding in identifying treatment-requiring ROP. Despite an internationally accepted definition of plus disease, there is significant variability in diagnostic process and outcome, producing variable levels of reported intra- and interexpert agreement. Several potential explanations for poor agreement have been proposed, including attention to undefined vascular features such as venous tortuosity, focus on narrower or wider field of view, unfamiliarity with digital images, the magnification and apparent severity of the standard photograph, and cut-off point differences among experts as to the level of tortuosity and dilation sufficient for "plus disease" along a continuum. Moreover, differences in diagnostic consistency among groups of experts separated both geographically and chronologically have been reported. These findings have implications for clinical care, research, and education, and highlight the need for a more precise definition of plus disease and objective diagnostic methods for ROP.

Posterior Pole Vascular Changes Before Treatment of Retinopathy of Prematurity

Importance

Objective

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusions and Relevance

Retinopathy of Prematurity-assist: Novel Software for Detecting Plus Disease

Purpose

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.

Conclusions

The new automated algorithm used in this pilot scheme for diagnosis and screening of patients with plus ROP has acceptable accuracy. With more improvements, it may become particularly useful, especially in centers without a skilled person in the ROP field.

Expert Diagnosis of Plus Disease in Retinopathy of Prematurity From Computer-Based Image Analysis

IMPORTANCE

Published definitions of plus disease in retinopathy of prematurity (ROP) reference arterial tortuosity and venous dilation within the posterior pole based on a standard published photograph. One possible explanation for limited interexpert reliability for a diagnosis of plus disease is that experts deviate from the published definitions.

OBJECTIVE

To identify vascular features used by experts for diagnosis of plus disease through quantitative image analysis.

DESIGN, SETTING, AND PARTICIPANTS

A computer-based image analysis system (Imaging and Informatics in ROP [i-ROP]) was developed using a set of 77 digital fundus images, and the system was designed to classify images compared with a reference standard diagnosis (RSD). System performance was analyzed as a function of the field of view (circular crops with a radius of 1-6 disc diameters) and vessel subtype (arteries only, veins only, or all vessels). Routine ROP screening was conducted from June 29, 2011, to October 14, 2014, in neonatal intensive care units at 8 academic institutions, with a subset of 73 images independently classified by 11 ROP experts for validation. The RSD was compared with the majority diagnosis of experts.

MAIN OUTCOMES AND MEASURES

The primary outcome measure was the percentage of accuracy of the i-ROP system classification of plus disease, with the RSD as a function of the field of view and vessel type. Secondary outcome measures included the accuracy of the 11 experts compared with the RSD.

RESULTS

Accuracy of plus disease diagnosis by the i-ROP computer-based system was highest (95%; 95% CI, 94%-95%) when it incorporated vascular tortuosity from both arteries and veins and with the widest field of view (6-disc diameter radius). Accuracy was 90% or less when using only arterial tortuosity and 85% or less using a 2- to 3-disc diameter view similar to the standard published photograph. Diagnostic accuracy of the i-ROP system (95%) was comparable to that of 11 expert physicians (mean 87%, range 79%-99%).

CONCLUSIONS AND RELEVANCE

Experts in ROP appear to consider findings from beyond the posterior retina when diagnosing plus disease and consider tortuosity of both arteries and veins, in contrast with published definitions. It is feasible for a computer-based image analysis system to perform comparably with ROP experts, using manually segmented images.

Plus Disease in Retinopathy of Prematurity: Diagnostic Trends in 2016 Versus 2007

PURPOSE

To identify any temporal trends in the diagnosis of plus disease in retinopathy of prematurity (ROP) by experts.

DESIGN

Reliability analysis.

METHODS

ROP experts were recruited in 2007 and 2016 to classify 34 wide-field fundus images of ROP as plus, pre-plus, or normal, coded as "3," "2," and "1," respectively, in the database. The main outcome was the average calculated score for each image in each cohort. Secondary outcomes included correlation on the relative ordering of the images in 2016 vs 2007, interexpert agreement, and intraexpert agreement.

RESULTS

The average score for each image was higher for 30 of 34 (88%) images in 2016 compared with 2007, influenced by fewer images classified as normal (P < .01), a similar number of pre-plus (P = .52), and more classified as plus (P < .01). The mean weighted kappa values in 2006 were 0.36 (range 0.21-0.60), compared with 0.22 (range 0-0.40) in 2016. There was good correlation between rankings of disease severity between the 2 cohorts (Spearman rank correlation ρ = 0.94), indicating near-perfect agreement on relative disease severity.

CONCLUSIONS

Despite good agreement between cohorts on relative disease severity ranking, the higher average score and classifications for each image demonstrate that experts are diagnosing pre-plus and plus disease at earlier stages of disease severity in 2016, compared with 2007. This has implications for patient care, research, and teaching, and additional studies are needed to better understand this temporal trend in image-based plus disease diagnosis.

Comprehensive Retinal Image Analysis for Aggressive Posterior Retinopathy of Prematurity

Computer aided analysis plays a nontrivial role in assisting the diagnosis of various eye pathologies. In this paper, we propose a framework to help diagnose the presence of Aggressive Posterior Retinopathy Of Prematurity (APROP), a pathology that is characterised by rapid onset and increased tortuosity of blood vessels close to the optic disc (OD). We quantify vessel characteristics that are of clinical relevance to APROP such as tortuosity and the extent of branching i.e., vessel segment count in the defined diagnostic region. We have adapted three vessel segmentation techniques: matched filter response, scale space theory and morphology with local entropy based thresholding. The proposed feature set equips us to build a linear discriminant classifier to discriminate APROP images from clinically healthy images. We have studied 36 images from 21 APROP subjects against a control group of 15 clinically healthy age matched infants. All subjects are age matched ranging from 33−40 weeks of post menstrual age. Experimental results show that we attain 100% recall and 95.45% precision, when the vessel network obtained from morphology is used for feature extraction.

Computer-aided diagnosis of retinopathy in retinal fundus images of preterm infants via quantification of vascular tortuosity

Retinopathy of prematurity (ROP), a disorder of the retina occurring in preterm infants, is the leading cause of preventable childhood blindness. An active phase of ROP that requires treatment is associated with the presence of plus disease, which is diagnosed clinically in a qualitative manner by visual assessment of the existence of a certain level of increase in the thickness and tortuosity of retinal vessels. The present study performs computer-aided diagnosis (CAD) of plus disease via quantitative measurement of tortuosity in retinal fundus images of preterm infants. Digital image processing techniques were developed for the detection of retinal vessels and measurement of their tortuosity. The total lengths of abnormally tortuous vessels in each quadrant and the entire image were then computed. A minimum-length diagnostic-decision-making criterion was developed to assess the diagnostic sensitivity and specificity of the values obtained. The area ([Formula: see text]) under the receiver operating characteristic curve was used to assess the overall diagnostic accuracy of the methods. Using a set of 19 retinal fundus images of preterm infants with plus disease and 91 without plus disease, the proposed methods provided an overall diagnostic accuracy of [Formula: see text]. Using the total length of all abnormally tortuous vessel segments in an image, our techniques are capable of CAD of plus disease with high accuracy without the need for manual selection of vessels to analyze. The proposed methods may be used in a clinical or teleophthalmological setting.

The Novel Evidenced Assessment of Tortuosity system: interobserver reliability and agreement with clinical assessment

PURPOSE

Computer-assisted assessment of vessel tortuosity is clinically useful in retinopathy of prematurity (ROP). However, poor image quality is often prohibitive for accurate segmentation by fully automated systems and semi-automated systems are prone to unreliability. In the present work, we describe a method of retinal vessel tortuosity measurement by means of purpose-built image analysis software that does not require high image quality yet is also reliable.

METHODS

Images were obtained from neonates at risk of ROP with Retcam Shuttle(®) . Individual vessels were assessed with the semi-automated Novel Evidenced Assessment of Tortuosity (NEAT) system by two masked experimenters. Scores were compared to assess reliability. They were also compared against clinical scoring of individual vessels by two ROP screeners to assess relationship with clinical assessment. In a second image cohort, the mean of the most tortuous vessel in each of four quadrants in each eye (NEAT-O) was compared against the documented gold standard clinical grading of plus disease.

RESULTS

Reliability of the NEAT system for 50 individual vessels using Bland-Altman plots was excellent. NEAT tortuosity scores for 50 individual vessels compared to clinical scoring showed strong correlation (0.706). Correlation between the NEAT-O score for average tortuosity and gold standard for 167 eyes was modest (0.578).

CONCLUSIONS

The NEAT system is intuitive, user-friendly and robust enough to be clinically useful in poor-quality images. It allows for a rapid, valid and reliable assessment of tortuosity of individual vessels and produces a tortuosity score that correlates well with severity of plus disease.

Plus Disease in Retinopathy of Prematurity: A Continuous Spectrum of Vascular Abnormality as a Basis of Diagnostic Variability

PURPOSE

To identify patterns of interexpert discrepancy in plus disease diagnosis in retinopathy of prematurity (ROP).

DESIGN

We developed 2 datasets of clinical 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 examination results. We recruited 8 expert ROP clinicians to classify these images and compared the distribution of classifications between experts and the RSD.

PARTICIPANTS

Eight participating experts with more than 10 years of clinical ROP experience and more than 5 peer-reviewed ROP publications who analyzed images obtained during routine ROP screening in neonatal intensive care units.

METHODS

Expert classification of images of plus disease in ROP.

MAIN OUTCOME MEASURES

Interexpert agreement (weighted κ statistic) and agreement and bias on ordinal classification between experts (analysis of variance [ANOVA]) and the RSD (percent agreement).

RESULTS

There was variable interexpert agreement on diagnostic classifications between the 8 experts and the RSD (weighted κ, 0-0.75; mean, 0.30). The RSD agreement ranged from 80% to 94% for the dataset of 100 images and from 29% to 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 preplus disease. The 2-way ANOVA model suggested a highly significant effect of both image and user on the average score (dataset A: P < 0.05 and adjusted R² = 0.82; and dataset B: P < 0.05 and adjusted R² = 0.6615).

CONCLUSIONS

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 preplus disease. This has important implications for research, teaching, and patient care for ROP and suggests that a continuous ROP plus disease severity score may reflect more accurately the behavior of expert ROP clinicians and may better standardize classification in the future.

Plus Disease in Retinopathy of Prematurity: Improving Diagnosis by Ranking Disease Severity and Using Quantitative Image Analysis

PURPOSE

To determine expert agreement on relative retinopathy of prematurity (ROP) disease severity and whether computer-based image analysis can model relative disease severity, and to propose consideration of a more continuous severity score for ROP.

DESIGN

We developed 2 databases of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP (i-ROP) cohort study and recruited expert physician, nonexpert physician, and nonphysician graders to classify and perform pairwise comparisons on both databases.

PARTICIPANTS

Six participating expert ROP clinician-scientists, each with a minimum of 10 years of clinical ROP experience and 5 ROP publications, and 5 image graders (3 physicians and 2 nonphysician graders) who analyzed images that were obtained during routine ROP screening in neonatal intensive care units.

METHODS

Images in both databases were ranked by average disease classification (classification ranking), by pairwise comparison using the Elo rating method (comparison ranking), and by correlation with the i-ROP computer-based image analysis system.

MAIN OUTCOME MEASURES

Interexpert agreement (weighted κ statistic) compared with the correlation coefficient (CC) between experts on pairwise comparisons and correlation between expert rankings and computer-based image analysis modeling.

RESULTS

There was variable interexpert agreement on diagnostic classification of disease (plus, preplus, or normal) among the 6 experts (mean weighted κ, 0.27; range, 0.06-0.63), but good correlation between experts on comparison ranking of disease severity (mean CC, 0.84; range, 0.74-0.93) on the set of 34 images. Comparison ranking provided a severity ranking that was in good agreement with ranking obtained by classification ranking (CC, 0.92). Comparison ranking on the larger dataset by both expert and nonexpert graders demonstrated good correlation (mean CC, 0.97; range, 0.95-0.98). The i-ROP system was able to model this continuous severity with good correlation (CC, 0.86).

CONCLUSIONS

Experts diagnose plus disease on a continuum, with poor absolute agreement on classification but good relative agreement on disease severity. These results suggest that the use of pairwise rankings and a continuous severity score, such as that provided by the i-ROP system, may improve agreement on disease severity in the future.

Diagnostic Discrepancies in Retinopathy of Prematurity Classification

PURPOSE

To identify the most common areas for discrepancy in retinopathy of prematurity (ROP) classification between experts.

DESIGN

Prospective cohort study.

PARTICIPANTS

A total of 281 infants were identified as part of a multicenter, prospective, ROP cohort study from 7 participating centers. Each site had participating ophthalmologists who provided the clinical classification after routine examination using binocular indirect ophthalmoscopy (BIO) and obtained wide-angle retinal images, which were independently classified by 2 study experts.

METHODS

Wide-angle retinal images (RetCam; Clarity Medical Systems, Pleasanton, CA) were obtained from study subjects, and 2 experts evaluated each image using a secure web-based module. Image-based classifications for zone, stage, plus disease, and overall disease category (no ROP, mild ROP, type II or pre-plus, and type I) were compared between the 2 experts and with the clinical classification obtained by BIO.

MAIN OUTCOME MEASURES

Inter-expert image-based agreement and image-based versus ophthalmoscopic diagnostic agreement using absolute agreement and weighted kappa statistic.

RESULTS

A total of 1553 study eye examinations from 281 infants were included in the study. Experts disagreed on the stage classification in 620 of 1553 comparisons (40%), plus disease classification (including pre-plus) in 287 of 1553 comparisons (18%), zone in 117 of 1553 comparisons (8%), and overall ROP category in 618 of 1553 comparisons (40%). However, agreement for presence versus absence of type 1 disease was >95%. There were no differences between image-based and clinical classification except for zone III disease.

CONCLUSIONS

The most common area of discrepancy in ROP classification is stage, although inter-expert agreement for clinically significant disease, such as presence versus absence of type 1 and type 2 disease, is high. There were no differences between image-based grading and clinical examination in the ability to detect clinically significant disease. This study provides additional evidence that image-based classification of ROP reliably detects clinically significant levels of ROP with high accuracy compared with the clinical examination.

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis

PURPOSE

We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis.

METHODS

A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the "i-ROP" system.

RESULTS

Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%).

CONCLUSIONS

This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination.

TRANSLATIONAL RELEVANCE

Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists.

Computer-aided diagnosis of plus disease in retinal fundus images of preterm infants via measurement of vessel tortuosity

An increase in retinal vessel tortuosity can be indicative of the presence of various diseases including retinopathy of prematurity (ROP). Accurate detection and measurement of such changes could help in computer-aided diagnosis of plus disease, which warrants treatment of ROP. We present image processing methods for detection and segmentation of retinal vessels, quantification of vessel tortuosity, and diagnostic-decision-making criteria that incorporate the clinical definition of plus-diagnosis. The obtained results using 110 retinal fundus images of preterm infants (91 without plus and 19 with plus) provide high sensitivity = 0.89 (17/19) and excellent specificity = 0.95 (86/91) in the diagnosis of plus disease.

Significant Correlation between Retinal Venous Tortuosity and Aqueous Vascular Endothelial Growth Factor Concentration in Eyes with Central Retinal Vein Occlusion

PURPOSE

To determine whether the degree of venous tortuosity is significantly correlated with the aqueous vascular endothelial growth factor (VEGF) concentration in eyes with a central retinal vein occlusion (CRVO).

METHODS

We reviewed the medical records of 32 eyes of 32 patients who had macular edema due to a CRVO. All of the patients were examined at the Nagoya University Hospital and were scheduled to receive an intravitreal injection of bevacizumab (IVB) or ranibizumab (IVR) within 12 weeks of the onset of the CRVO to treat the macular edema. Aqueous humor was collected just before the IVB or IVR, and the VEGF concentration was determined by enzyme-linked immunosorbent assay (ELISA). The venous tortuosity index was calculated by dividing the length of the retinal veins by the chord length of the same segment. The correlation between the mean tortuosity index of the inferotemporal and supratemporal branches of the retinal vein and the aqueous VEGF concentration was determined.

RESULTS

The mean aqueous VEGF concentration was 384 ± 312 pg/ml with a range of 90 to 1077 pg/ml. The degree of venous tortuosity was significantly correlated with the VEGF concentration in the aqueous. (r = 0.49, P = 0.004), with the foveal thickness (r = 0.40, P = 0.02), and with the best-corrected visual acuity (r = 0.38, P = 0.03).

CONCLUSIONS

The significant correlation between the aqueous VEGF concentration and the venous tortuosity indicates that the degree of retinal venous tortuosity can be used to identify eyes that are at a high risk of developing neovascularization.

Assessment of vessel tortuosity in retinal images of preterm infants

Diagnosis of plus disease is crucial for timely treatment and management of retinopathy of prematurity. An indicator of the presence of plus disease is an increase in the tortuosity of blood vessels in the retina. In this work, we propose a new angle-variation-based measure for quantification of tortuosity in retinal fundus images of preterm infants. The methods include the use of Gabor filters to detect vessels as well as to obtain their orientation at each pixel. Morphological image processing methods are used to obtain a skeleton image of the vessels for measurement of tortuosity. Out of 11 vessel segments, marked by an expert ophthalmologist as showing high levels of tortuosity due to plus disease, all were correctly identified using the proposed methods.