Diagnosis of plus disease in retinopathy of prematurity using Retinal Image multiScale Analysis

Slope Chain Code-based scale-independent tortuosity measurement on retinal vessels

Retinal vascular tortuosity presents valuable potential as a clinical biomarker for many relevant vascular and systemic diseases. Our work exhibits twofold: first, the definition of a novel scale-invariant metric to measure retinal blood vessel tortuosity; and second, the generation of a local database, called SCALE-TORT, with the intention of providing a means to test the scale invariance property on real retinal vessels rather than on synthetic data. The proposed scale invariant tortuosity metric is based on the Extended Slope Chain Code which uses variable straight-line segments for describing curves. It is focused on the representation of high-definition curves, the length of the segments is a function of the slope changes of the curve. Scale invariance is an important property when several different retinal image settings or different acquisition sources are used during a particular study or in clinical practice. The database SCALE-TORT, introduced herein, was built semi-automatically from digital images containing the coordinates of blood vessel central lines (curves) taken from images of the same eye obtained by two different imaging methodologies: retinal fundus camera and scanning laser ophthalmoscope. The vessel curves extracted from the same eye are paired for images acquired by the fundus camera and those acquired by the scanning laser ophthalmoscope to evaluate the scale invariance of the metric. Ten different tortuosity metrics were implemented and compared including our proposed metric. Three experiments were conducted to test the metrics and their properties. The first aimed to determine which tortuosity metrics possess the following properties: scale invariance, sensitivity to sudden tortuosity changes when the curve remains constant in size, and how they behave when curves are concatenated. In the second experiment, all reviewed metrics were tested on the publicly available RET-TORT database, to compare the results of the specific metric with the tortuosity classification provided by their experts and in comparison with other authors. Finally, in the third experiment, the behavior of different metrics, including those which are scale-invariant, were tested by utilizing the paired retinal vessel curves from our new SCALE-TORT database. In comparison with other tortuosity metrics, we show that the metric Extended Slope Chain Code proposed in this work optimally complies with scale invariance, in addition to having sufficient sensitivity to detect abrupt changes in tortuosity. Easy implementation being a further plus. Furthermore, we present a new and valuable database for scale property evaluation on images of retinal blood vessels called SCALE-TORT. As far as we are aware, there is no public database with these characteristics.

A software for quantitative measurement of vessel parameters in fundus images

Retinal vessel is a unique structure that allows non-invasive observation of the microcirculatory system. Its pathological features and abnormal structural alterations are associated with cardiovascular and systemic diseases. Especially the abnormalities in caliber features, histology features, and geometric structure of retinal vessels are indicative of these diseases. However, the complex distribution and imperceptible characteristics of vasculature have hindered the measurement of vessel parameters. To this end, we design a new software (Retinal Vessel Parameters Quantitative Measurement Software, RVPQMS) to quantitatively measure the features of retinal vessels. The RVPQMS is designed with the functions of vessel segmentation, landmark localization, vessel tracking, vessel identification and parameter measurement. It enables comprehensive measurement of vessel parameters in both standard zone and whole area. To ensure the accuracy of the software, the algorithms integrated in this software are validated on both private and public datasets, and experimental results demonstrate that it has excellent performance in vessel segmentation, tracking and identification. The RVPQMS software provides thorough and quantitative measurement of retinal vessel parameters, facilitating the study of vessel features for cardiovascular and systemic diseases.

Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population

Purpose

The primary objective of this study was to develop and validate an AI algorithm as a screening tool for the detection of retinopathy of prematurity (ROP).

Participants

Images were collected from infants enrolled in the KIDROP tele-ROP screening program.

Methods

We developed a deep learning (DL) algorithm with 227,326 wide-field images from multiple camera systems obtained from the KIDROP tele-ROP screening program in India over an 11-year period. 37,477 temporal retina images were utilized with the dataset split into train (n = 25,982, 69.33%), validation (n = 4,006, 10.69%), and an independent test set (n = 7,489, 19.98%). The algorithm consists of a binary classifier that distinguishes between the presence of ROP (Stages 1-3) and the absence of ROP. The image labels were retrieved from the daily registers of the tele-ROP program. They consist of per-eye diagnoses provided by trained ROP graders based on all images captured during the screening session. Infants requiring treatment and a proportion of those not requiring urgent referral had an additional confirmatory diagnosis from an ROP specialist.

Results

Of the 7,489 temporal images analyzed in the test set, 2,249 (30.0%) images showed the presence of ROP. The sensitivity and specificity to detect ROP was 91.46% (95% CI: 90.23%-92.59%) and 91.22% (95% CI: 90.42%-91.97%), respectively, while the positive predictive value (PPV) was 81.72% (95% CI: 80.37%-83.00%), negative predictive value (NPV) was 96.14% (95% CI: 95.60%-96.61%) and the AUROC was 0.970.

Conclusion

The novel ROP screening algorithm demonstrated high sensitivity and specificity in detecting the presence of ROP. A prospective clinical validation in a real-world tele-ROP platform is under consideration. It has the potential to lower the number of screening sessions required to be conducted by a specialist for a high-risk preterm infant thus significantly improving workflow efficiency.

Automatic vessel crossing and bifurcation detection based on multi-attention network vessel segmentation and directed graph search

Analysis of the vascular tree is the basic premise to automatically diagnose retinal biomarkers associated with ophthalmic and systemic diseases, among which accurate identification of intersection and bifurcation points is quite challenging but important for disentangling complex vascular network and tracking vessel morphology. In this paper, we present a novel directed graph search-based multi-attentive neural network approach to automatically segment the vascular network and separate intersections and bifurcations from color fundus images. Our approach uses multi-dimensional attention to adaptively integrate local features and their global dependencies while learning to focus on target structures at different scales to generate binary vascular maps. A directed graphical representation of the vascular network is constructed to represent the topology and spatial connectivity of the vascular structures. Using local geometric information including color difference, diameter, and angle, the complex vascular tree is decomposed into multiple sub-trees to finally classify and label vascular feature points. The proposed method has been tested on the DRIVE dataset and the IOSTAR dataset containing 40 images and 30 images, respectively, with 0.863 and 0.764 F¹-score of detection points and average accuracy of 0.914 and 0.854 for classification points. These results demonstrate the superiority of our proposed method outperforming state-of-the-art methods in feature point detection and classification.

Multimodal Imaging, Tele-Education, and Telemedicine in Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is a disease that affects retinal vasculature in premature infants and remains one of the leading causes of blindness in childhood worldwide. ROP screening can encounter some difficulties such as the lack of specialists and services in rural areas. The evolution of technology has helped address these issues and led to the emergence of state-of-the-art multimodal digital imaging devices such fundus cameras with its variable properties, optical coherence tomography (OCT), OCT angiography, and fluorescein angiography which has helped immensely in the process of improving ROP care and understanding the disease pathophysiology. Computer-based imaging analysis and deep learning have recently been demonstrating promising outcomes in regard to ROP diagnosis. Telemedicine is considered an acceptable alternative to clinical examination when optimal circumstances for ROP screening in certain areas are lacking, and the expansion of these programs has been reported. Tele-education programs in ROP have the potential to improve the quality of training to physicians to optimize ROP care.

Retinopathy of prematurity screening: A narrative review of current programs, teleophthalmology, and diagnostic support systems

PURPOSE

Neonatal care in middle-income countries has improved over the last decade, leading to a "third epidemic" of retinopathy of prematurity (ROP). Without concomitant improvements in ROP screening infrastructure, reduction of ROP-associated visual loss remains a challenge worldwide. The emergence of teleophthalmology screening programs and artificial intelligence (AI) technologies represents promising methods to address this growing unmet demand in ROP screening. An improved understanding of current ROP screening programs may inform the adoption of these novel technologies in ROP care.

METHODS

A critical narrative review of the literature was carried out. Publications that were representative of established or emerging ROP screening programs in high-, middle-, and low-income countries were selected for review. Screening programs were reviewed for inclusion criteria, screening frequency and duration, modality, and published sensitivity and specificity.

RESULTS

Screening inclusion criteria, including age and birth weight cutoffs, showed significant heterogeneity globally. Countries of similar income tend to have similar criteria. Three primary screening modalities including binocular indirect ophthalmoscopy (BIO), wide-field digital retinal imaging (WFDRI), and teleophthalmology were identified and reviewed. BIO has documented limitations in reduced interoperator agreement, scalability, and geographical access barriers, which are mitigated in part by WFDRI. Teleophthalmology screening may address limitations in ROP screening workforce distribution and training. Opportunities for AI technologies were identified in the context of these limitations, including interoperator reliability and possibilities for point-of-care diagnosis.

CONCLUSION

Limitations in the current ROP screening include scalability, geographical access, and high screening burden with low treatment yield. These may be addressable through increased adoption of teleophthalmology and AI technologies. As the global incidence of ROP continues to increase, implementation of these novel modalities requires greater consideration.

A novel multiple subdivision-based algorithm for quantitative assessment of retinal vascular tortuosity

Vascular tortuosity as an indicator of retinal vascular morphological changes can be quantitatively analyzed and used as a biomarker for the early diagnosis of relevant disease such as diabetes. While various methods have been proposed to evaluate retinal vascular tortuosity, the main obstacle limiting their clinical application is the poor consistency compared with the experts' evaluation. In this research, we proposed to apply a multiple subdivision-based algorithm for the vessel segment vascular tortuosity analysis combining with a learning curve function of vessel curvature inflection point number, emphasizing the human assessment nature focusing not only global but also on local vascular features. Our algorithm achieved high correlation coefficients of 0.931 for arteries and 0.925 for veins compared with clinical grading of extracted retinal vessels. For the prognostic performance against experts' prediction in retinal fundus images from diabetic patients, the area under the receiver operating characteristic curve reached 0.968, indicating a good consistency with experts' predication in full retinal vascular network evaluation.

Retinal imaging in infants

Digital retinal imaging is at the core of a revolution that is continually improving the screening, diagnosis, documentation, monitoring, and treatment of infant retinal diseases. Historically, imaging the retina of infants had been limited and difficult to obtain. Recent advances in photographic instrumentation have significantly improved the ability to obtain high quality multimodal images of the infant retina. These include color fundus photography with different camera angles, ultrasonography, fundus fluorescein angiography, optical coherence tomography, and optical coherence tomography angiography. We provide a summary of the current literature on retinal imaging in infants and highlight areas where further research is required.

Improving Blood Vessel Tortuosity Measurements via Highly Sampled Numerical Integration of the Frenet-Serret Equations

Measures of vascular tortuosity-how curved and twisted a vessel is-are associated with a variety of vascular diseases. Consequently, measurements of vessel tortuosity that are accurate and comparable across modality, resolution, and size are greatly needed. Yet in practice, precise and consistent measurements are problematic-mismeasurements, inability to calculate, or contradictory and inconsistent measurements occur within and across studies. Here, we present a new method of measuring vessel tortuosity that ensures improved accuracy. Our method relies on numerical integration of the Frenet-Serret equations. By reconstructing the three-dimensional vessel coordinates from tortuosity measurements, we explain how to identify and use a minimally-sufficient sampling rate based on vessel radius while avoiding errors associated with oversampling and overfitting. Our work identifies a key failing in current practices of filtering asymptotic measurements and highlights inconsistencies and redundancies between existing tortuosity metrics. We demonstrate our method by applying it to manually constructed vessel phantoms with known measures of tortuousity, and 9,000 vessels from medical image data spanning human cerebral, coronary, and pulmonary vascular trees, and the carotid, abdominal, renal, and iliac arteries.

Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity

Retinopathy of prematurity is a disease that can affect premature or in similar conditions babies. For diagnosing of retinopathy of prematurity, the infant is examined as soon as possible. Due to the nature of the examination, the images obtained are poor in quality. This article presents an automated method for processing fundus images to improve the visibility of the vascular network. The method includes several processing tasks whose parameters are predicted using an artificial neural network. A set of 88 clinical images were used in this work. The performance of our proposal is efficient, and the average processing time was 42 ms. The method was assessed using both the contrast improvement index and expert opinions. The contrast improvement index average was 2; this means the processed image successfully improved its contrast. Three pediatric ophthalmologists validated the proposed method and agreed that the visual enhancement can help observe clearly the retinal vessels.

Diving Deep into Deep Learning: An Update on Artificial Intelligence in Retina

Purpose of Review

In the present article, we will provide an understanding and review of artificial intelligence in the subspecialty of retina and its potential applications within the specialty.

Recent Findings

Given the significant use of diagnostic imaging within retina, this subspecialty is a fitting area for the incorporation of artificial intelligence. Researchers have aimed at creating models to assist in the diagnosis and management of retinal disease as well as in the prediction of disease course and treatment response. Most of this work thus far has focused on diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity, although other retinal diseases have started to be explored as well.

Summary

Artificial intelligence is well-suited to transform the practice of ophthalmology. A basic understanding of the technology is important for its effective implementation and growth.

Automated diagnosis and quantitative analysis of plus disease in retinopathy of prematurity based on deep convolutional neural networks

BACKGROUND

The purpose of this study was to develop an automated diagnosis and quantitative analysis system for plus disease. The system provides a diagnostic decision but also performs quantitative analysis of the typical pathological features of the disease, which helps the physicians to make the best judgement and communicate the decisions.

METHODS

The deep learning network provided segmentation of the retinal vessels and the optic disc (OD). Based on the vessel segmentation, plus disease was classified and tortuosity, width, fractal dimension and vessel density were evaluated automatically.

RESULTS

The trained network achieved a sensitivity of 95.1% with 97.8% specificity for the diagnosis of plus disease. For detection of preplus or worse, the sensitivity and specificity were 92.4% and 97.4%. The quadratic weighted k was 0.9244. The tortuosities for the normal, preplus and plus groups were 3.61 ± 0.08, 5.95 ± 1.57 and 10.67 ± 0.50 (10⁴  cm^-3 ). The widths of the blood vessels were 63.46 ± 0.39, 67.21 ± 0.70 and 68.89 ± 0.75 μm. The fractal dimensions were 1.18 ± 0.01, 1.22 ± 0.01 and 1.26 ± 0.02. The vessel densities were 1.39 ± 0.03, 1.60 ± 0.01 and 1.64 ± 0.09 (%). All values were statistically different among the groups. After treatment for plus disease with ranibizumab injection, quantitative analysis showed significant changes in the pathological features.

CONCLUSIONS

Our system achieved high accuracy of diagnosis of plus disease in retinopathy of prematurity. It provided a quantitative analysis of the dynamic features of the disease progression. This automated system can assist physicians by providing a classification decision with auxiliary quantitative evaluation of the typical pathological features of the disease.

Plus Disease in Retinopathy of Prematurity: Convolutional Neural Network Performance Using a Combined Neural Network and Feature Extraction Approach

Purpose

Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels, is the most important feature to determine treatment-requiring ROP. We aimed to create a complete, publicly available and feature-extraction-based pipeline, I-ROP ASSIST, that achieves convolutional neural network (CNN)-like performance when diagnosing plus disease from retinal images.

Methods

We developed two datasets containing 100 and 5512 posterior retinal images, respectively. After segmenting retinal vessels, we detected the vessel centerlines. Then, we extracted features relevant to ROP, including tortuosity and dilation measures, and used these features in the classifiers including logistic regression, support vector machine and neural networks to assess a severity score for the input. We tested our system with fivefold cross-validation and calculated the area under the curve (AUC) metric for each classifier and dataset.

Results

For predicting plus versus not-plus categories, we achieved 99% and 94% AUC on the first and second datasets, respectively. For predicting pre-plus or worse versus normal categories, we achieved 99% and 88% AUC on the first and second datasets, respectively. The CNN method achieved 98% and 94% for predicting two categories on the second dataset.

Conclusions

Our system combining automatic retinal vessel segmentation, tracing, feature extraction and classification is able to diagnose plus disease in ROP with CNN-like performance.

Translational Relevance

The high performance of I-ROP ASSIST suggests potential applications in automated and objective diagnosis of plus disease.

Relationship between retinal vessel tortuosity and oxygenation in sickle cell retinopathy

Background

Reduced retinal vascular oxygen (O₂) content causes tissue hypoxia and may lead to development of vision-threatening pathologies. Since increased vessel tortuosity is an early sign for some hypoxia-implicated retinopathies, we investigated a relationship between retinal vascular O₂ content and vessel tortuosity indices.

Methods

Dual wavelength retinal oximetry using a commercially available scanning laser ophthalmoscope was performed in both eyes of 12 healthy (NC) and 12 sickle cell retinopathy (SCR) subjects. Images were analyzed to quantify retinal arterial and venous O₂ content and determine vessel tortuosity index (VTI) and vessel inflection index (VII) in circumpapillary regions. Linear mixed model analysis was used to determine the effect of disease on vascular O₂ content, VTI and VII, and relate vascular O₂ content with VTI and VII. Models accounted for vessel type, fellow eyes, age and mean arterial pressure.

Results

Retinal arterial and venous O₂ content were lower in SCR (O_2A = 11 ± 4 mLO₂/dL, O_2V = 7 ± 2 mLO₂/dL) compared to NC (O_2A = 18 ± 3 mLO₂/dL, O_2V = 13 ± 3 mLO₂/dL) subjects (p < 0.001). As expected, O₂ content was higher in arteries (15 ± 5 mLO₂/dL) than veins (10 ± 4 mLO₂/dL) (p < 0.001), but not different between eyes (OD: 12 ± 5 mLO₂/dL; OS:13 ± 5 mLO₂/dL) (p = 0.3). VTI was not significantly different between SCR (0.18 ± 0.07) and NC (0.15 ± 0.04) subjects, or between arteries (0.18 ± 0.07) and veins (0.16 ± 0.04), or between eyes (OD: 0.18 ± 0.07, OS:0.17 ± 0.05) (p ≥ 0.06). VII was significantly higher in SCR (10 ± 2) compared to NC subjects (8 ± 1) (p = 0.003). VII was also higher in veins (9 ± 2) compared to arteries (8 ± 5) (p = 0.04), but not different between eyes (OD: 9 ± 2; OS: 9 ± 2) (p = 0.2). There was an inverse linear relationship between vascular O₂ (13 ± 5 mLO₂/dL) content and VII (9 ± 2) (β = -0.5; p = 0.02).

Conclusions

The findings augment knowledge of relationship between retinal vascular oxygenation and morphological changes and potentially contribute to identifying biomarkers for assessment of retinal hypoxia due to SCR and other retinopathies.

Deep Learning Algorithm for Automated Diagnosis of Retinopathy of Prematurity Plus Disease

PURPOSE

This study describes the initial development of a deep learning algorithm, ROP.AI, to automatically diagnose retinopathy of prematurity (ROP) plus disease in fundal images.

METHODS

ROP.AI was trained using 6974 fundal images from Australasian image databases. Each image was given a diagnosis as part of real-world routine ROP screening and classified as normal or plus disease. The algorithm was trained using 80% of the images and validated against the remaining 20% within a hold-out test set. Performance in diagnosing plus disease was evaluated against an external set of 90 images. Performance in detecting pre-plus disease was also tested. As a screening tool, the algorithm's operating point was optimized for sensitivity and negative predictive value, and its performance reevaluated.

RESULTS

For plus disease diagnosis within the 20% hold-out test set, the algorithm achieved a 96.6% sensitivity, 98.0% specificity, and 97.3% ± 0.7% accuracy. Area under the receiver operating characteristic curve was 0.993. Within the independent test set, the algorithm achieved a 93.9% sensitivity, 80.7% specificity, and 95.8% negative predictive value. For detection of pre-plus and plus disease, the algorithm achieved 81.4% sensitivity, 80.7% specificity, and 80.7% negative predictive value. Following the identification of an optimized operating point, the algorithm diagnosed plus disease with a 97.0% sensitivity and 97.8% negative predictive value.

CONCLUSIONS

ROP.AI is a deep learning algorithm able to automatically diagnose ROP plus disease with high sensitivity and negative predictive value.

TRANSLATIONAL RELEVANCE

In the context of increasing global disease burden, future development may improve access to ROP diagnosis and care.

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.

Retinal and Choroidal Optical Coherence Tomography Findings of Carotid Cavernous Fistula

PURPOSE

To define the retinal and choroidal imaging findings of carotid cavernous fistula (CCF) including central foveal thickness, subfoveal choroidal thickness, choroidal vascularity index (CVI) parameters, and tortuosity indexes (TIs) as compared to a control group (CG).

DESIGN

Cross-sectional study.

MATERIALS AND METHODS

The spectral domain enhanced-depth imaging optical coherence tomography images of 19 eyes of 19 consecutive patients with angiographically proven CCF and 19 eyes of 19 age- and sex-matched healthy control subjects were included. The patient group was divided according to CCF venous drainage pattern as anterior (A-CCF: draining into ophthalmic veins) and posterior (P-CCF: not draining into ophthalmic veins). The clinically affected eyes of the patient group, ipsilateral to the fistula, were included in the analysis.

RESULTS

There were 15 A-CCFs (78.9%) and 4 P-CCFs (21.1%). The mean SFCT of the A-CCF group (395.21 ± 111.69 μm) was significantly higher than those of the P-CCF (246.84 ± 94.12 μm) and CG groups (280.79 ± 111.36 μm) (P = .039 and P = .006, respectively). The mean CVI of the A-CCF group was significantly higher than that of the CG (68.97 ± 4.81 and 65.66 ± 3.37, respectively, P = .033). The A-CCF group had significantly higher inferior, superior, and total venous TI than the CG group (P = .001, P = .001, and P < .001, respectively).

CONCLUSION

In this first study investigating the CVI and TI in CCF patients, we demonstrated that SFCT, CVI, and TI could potentially be used to aid in the diagnosis of A-CCF.

Automated Detection and Classification of Telemedical Retinopathy of Prematurity Images

Background: Retinopathy of prematurity (ROP) is a retinal disorder of low birth weight infants and it is the leading cause of childhood blindness. The capability of wide field digital imaging systems to capture the clinical features of ROP has greatly helped the physicians to assess the severity of ROP and prevent childhood blindness due to ROP. Currently there is a lack of automated systems to assess the severity of ROP to assist the ROP specialist to make treatment decision. Objective: To present an automated detection and classification approach to assess the severity of ROP using wide field telemedical images. Materials and Methods: A total of 160 telemedical ROP (tele-ROP) images were collected out, of which 36 images were Normal, 79 images were Stage 2, and 45 images were Stage 3. Hessian analysis and support vector machine (SVM) classifier have been used to detect and classify the severity of ROP from tele-ROP images. Results: Classified the Normal, Stage 2, and Stage 3 images using SVM. Achieved accuracy of 91.8%, sensitivity of 90.37%, specificity of 94.65%, false positive rate of 5.35%, and false negative rate of 9.63%. Conclusions: The automated approach of detecting and classifying ROP would support pediatric ophthalmologists for early treatment decisions with optimal care.

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.

Quantitative Analysis of Conjunctival and Retinal Vessels in Fabry Disease

Fabry Disease (FD) is a rare X-linked lysosomal storage disorder characterized by systemic and ocular involvement. It has been described an increasing in retinal and conjunctival vessel tortuosity and this feature represents an important marker for the disease. Currently, there is not an objective method to measure and quantify this parameter. We tested a new semi-automatic software measuring retinal and conjunctival vessel tortuosity from eye fundus and conjunctival digital images in a group of FD patients. We performed an observational case-control study evaluating three mathematical parameters describing tortuosity (sum of angle metric [SOAM], product of angle distance [PAD], triangular index [I2e]) obtained from fundus and conjunctival pictures of 11 FD patients and 11 age and sex-matched controls. Both eyes were considered. Mann-Whitney test was used to compare the FD group versus the control group and, within the FD group, male versus female patients. Linear regression analysis was performed to evaluate the possible association of retinal and conjunctival vessels tortuosity parameters with age and with specific markers of systemic disease's progression. The tortuosity parameters (SOAM, PAD and I2e) were significantly higher in retinal vessels and in conjunctival nasal vessels in FD patients in comparison with the controls (p=0.003, p=0.002, p=0.001 respectively for retina) (p=0.023, p=0.014, p=0.001 respectively for nasal conjunctiva). No significant association was found between retinal and conjunctival tortuosity parameters and increasing age or systemic involvement markers. Vessel tortuosity represents an important clinical manifestation in FD. A computer-assisted analysis of retinal and conjunctival vasculature demonstrated an increased vessels tortuosity in patients affected by Fabry disease. This non-invasive technique might be useful to help the diagnosis in early stages, to establish disease severity and monitor its progression.

Vessel Tortuosity Cutoff Values Using the Modified ROPtool May Predict Need for Treatment in Retinopathy of Prematurity

BACKGROUND AND OBJECTIVE

To quantify vessel tortuosity among infants with retinopathy of prematurity (ROP).

PATIENTS AND METHODS

This was a retrospective study including 61 RetCam images from 33 infants. The laser treatment (LT) group included 17 infants who underwent laser for ROP. The no-treatment (NT) group included 16 infants. The modified ROPtool was used to calculate mean vessel tortuosity (MVT) and highest vessel tortuosity (HVT) for the participants and for the standard plus disease photograph from the Early Treatment for Retinopathy of Prematurity (ETROP) study.

RESULTS

The median MVT was 1.226 versus 1.056 for the LT and NT groups, respectively (P < .001). The median HVT was 1.346 versus 1.088 (P < .001). An MVT of 1.124 was 96.7% sensitive and 100% specific for identifying infants with treatment-requiring ROP. Both MVT and HVT cutoff values correctly captured plus disease in the standard ETROP trial photograph.

CONCLUSION

The modified ROPtool can be used to identify infants who have treatment-requiring ROP with a high level of sensitivity and specificity. [Ophthalmic Surg Lasers Imaging Retina. 2019;50:215-220.].

Intravitreal bevacizumab treatment reduces ocular blood flow in retinopathy of prematurity: a four-case report

PURPOSE

To evaluate the relationship between ocular blood flow, expressed as mean blur rate (MBR) by laser speckle flowgraphy, and intravitreal bevacizumab (IVB) therapy in neonates with retinopathy of prematurity (ROP).

METHODS

This was a case series study of 4 neonates with ROP under sedation before and after IVB and evaluated 8 eyes, in which the circulation could be measured three times consecutively. We performed optic nerve head blood flow measurement and fluorescein angiography (FA) before and 1 week after treatment. Blood flow was analyzed separately for MBR-A (mean of all values), MBR-V (vessel mean), and MBR-T (tissue mean). Comparisons between the MBR (-A, -V, -T), body weight, and other systemic and ocular parameters before and after treatment were performed using a paired t test.

RESULTS

The MBR values after IVB were lower than the pre-treatment values in all cases. All eyes showed leakage at neovascularization on FA before treatment. Although leakage improved 1 week after treatment, the neovascularization did not completely regress.

CONCLUSIONS

IVB improves vein dilation and artery tortuosity, while reducing ocular blood flow in neonates with ROP. We suggest that neovascularization might not be involved in reducing ocular blood flow in the early stage of IVB treatment.

Retinal vascular changes in preterm infants: heart and lung diseases and plus disease

PURPOSE

To report the retinal vascular features of preterm infants with congenital heart disease (CHD), lung disease (pulmonary hypertension [PH] and bronchopulmonary dysplasia [BPD]), and ROP with plus disease to determine whether these disease entities are distinguishable on the basis of retinal vessel morphology.

METHODS

The medical records of preterm infants with CHD, lung disease, and ROP with plus disease were reviewed retrospectively. Qualitative vascular findings were validated using computer-based software to analyze 25 representative images, each corresponding to one infant's eye. The images were organized into five groups, based on clinical information. Vessel diameter (d) and tortuosity index (TI) were measured.

RESULTS

A total of 106 infants (mean gestational age, 30.5 ± 2.22 weeks) were initially included. Ophthalmologic evaluation of preterm infants with CHD and lung diseases showed vascular tortuosity without vasodilation at the posterior pole as well as in the periphery. Quantitative analysis showed that venular diameter was significantly increased in the plus disease group (P = 0.0022) compared to other groups. There was significantly less tortuosity in both arterioles and venules in BPD (P < 0.001, P = 0.0453) compared with plus group.

CONCLUSIONS

The patterns of retinal vascular tortuosity observed in preterm infants may be unique to different systemic congestive conditions and could have therapeutic implications.

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.

Decreased ocular blood flow after photocoagulation therapy in neonatal retinopathy of prematurity

PURPOSE

To evaluate the relationships between optic nerve head blood flow, expressed as mean blur rate (MBR) measured by laser speckle flowgraphy (LSFG), and photocoagulation therapy in neonates with retinopathy of prematurity (ROP).

STUDY DESIGN

Case series study.

METHODS

We studied 5 ROP neonates either during sleep or under sedation both before and after photocoagulation, and evaluated 8 eyes in which the circulation could be measured three times consecutively. Correlations between the MBR-A (mean of all values), MBR-V (vessel mean) and MBR-T (tissue mean) and postmenstrual age were evaluated using Spearman's rank correlation coefficient. In addition, correlations between the relative MBR (-A, -V, -T) value and number of photocoagulation burns and the NV score were evaluated. Differences between post-treatment MBR in ROP subjects and normal neonates' MBR were estimated using analysis of covariance (ANCoVA), with adjustment for postmenstrual age.

RESULTS

The relative MBR (-A, -V, -T) values after photocoagulation were 69.6 ± 16.0%, 66.7 ± 17.0% and 74.3 ± 14.6%, respectively. Postmenstrual age was significantly correlated with post-treatment MBR-A (r = 0.83, p = 0.0101), MBR-V (r = 0.85, p = 0.007) and MBR-T (r = 0.76, p = 0.0282). The relative MBR-T value was significantly correlated with the number of photocoagulation burns (r = -0.75, p = 0.033) and NV score (r = -0.72, p = 0.0437). The ANCoVA results showed no significant difference between post-treatment MBR and normal neonates' MBR.

CONCLUSIONS

Photocoagulation improved the dilation of veins and tortuosity of arteries and reduced ocular blood flow in ROP subjects. Since the post-treatment MBR was not different from a normal neonate's MBR, it is suggested that the pre-treatment MBR was higher in severe ROP cases.

Using an Image Fusion Methodology to Improve Efficiency and Traceability of Posterior Pole Vessel Analysis by ROPtool

Background:

The diagnosis of plus disease in retinopathy of prematurity (ROP) largely determines the need for treatment; however, this diagnosis is subjective. To make the diagnosis of plus disease more objective, semi-automated computer programs (e.g. ROPtool) have been created to quantify vascular dilation and tortuosity. ROPtool can accurately analyze blood vessels only in images with very good quality, but many still images captured by indirect ophthalmoscopy have insufficient image quality for ROPtool analysis.

Purpose:

To evaluate the ability of an image fusion methodology (robust mosaicing) to increase the efficiency and traceability of posterior pole vessel analysis by ROPtool.

Materials and Methodology:

We retrospectively reviewed video indirect ophthalmoscopy images acquired during routine ROP examinations and selected the best unenhanced still image from the video for each infant. Robust mosaicing was used to create an enhanced mosaic image from the same video for each eye. We evaluated the time required for ROPtool analysis as well as ROPtool’s ability to analyze vessels in enhanced vs. unenhanced images.

Results:

We included 39 eyes of 39 infants. ROPtool analysis was faster (125 vs. 152 seconds; p=0.02) in enhanced vs. unenhanced images, respectively. ROPtool was able to trace retinal vessels in more quadrants (143/156, 92% vs 115/156, 74%; p=0.16) in enhanced mosaic vs. unenhanced still images, respectively and in more overall (38/39, 97% vs. 34/39, 87%; p=0.07) enhanced mosaic vs. unenhanced still images, respectively.

Conclusion:

Retinal image enhancement using robust mosaicing advances efforts to automate grading of posterior pole disease in ROP.

Method for quantitative assessment of retinal vessel tortuosity in optical coherence tomography angiography applied to sickle cell retinopathy

Tortuosity is an important geometric vessel parameter and among the first microvascular alterations observed in various retinopathies. In the current study, a quantitative vessel tortuosity index (VTI) based on a combination of local and global centerline features is presented. Performance of VTI and previously established tortuosity indices were compared against human observers' evaluation of tortuosity. An image-processing pipeline was developed for application of VTI in retinal vessels imaged by optical coherence tomography angiography (OCTA) in perifoveal (6 mm × 6 mm) and parafoveal (3 mm × 3 mm) regions centered on the fovea. Forty-one subjects (12 healthy control (NC) and 29 sickle cell retinopathy (SCR)) and 10 subjects (5 NC and 5 SCR) were imaged in the perifoveal and parafoveal regions, respectively. The relationship between VTI and age was examined in the perifoveal regions in NC subjects. VTI was measured from the OCTA images and compared between NC and SCR subjects using generalized least square regression with and without adjusting for age and race. VTI was found to correlate better than the 4 previous indices with performance of human observers. In the perifoveal region, a significant correlation was observed between VTI and age (r = -0.4, P<0.001, N = 12). VTI was higher in SCR than NC subjects in perifoveal and parafoveal regions (P≤0.001). The results demonstrate that the proposed method shows promise for detection of increased tortuosity in vessels due to retinal disorders.

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.

Screening and treatments using telemedicine in retinopathy of prematurity

Several studies have validated the role of telemedicine as a new powerful screening and diagnostic tool for retinal disorders, such as diabetic retinopathy and retinopathy of prematurity. With regard to retinopathy of prematurity, bedside examination with binocular indirect ophthalmoscopy has been the gold standard technique for screening, yet with several limitations. Herein, we review the current evidence that supports the role of telemedicine for the screening of infants with retinopathy of prematurity.

Vessel Delineation in Retinal Images using Leung-Malik filters and Two Levels Hierarchical Learning

Blood vessel segmentation is important for the analysis of ocular fundus images for diseases affecting vessel caliber, occlusion, leakage, inflammation, and proliferation. We introduce a novel supervised method to evaluate performance of Leung-Malik filters in delineating vessels. First, feature vectors are extracted for every pixel with respect to the response of Leung-Malik filters on green channel retinal images in different orientations and scales. A two level hierarchical learning framework is proposed to segment vessels in retinal images with confounding disease abnormalities. In the first level, three expert classifiers are trained to delineate 1) vessels, 2) background, and 3) retinal pathologies including abnormal pathologies such as lesions and anatomical structures such as optic disc. In the second level, a new classifier is trained to detect vessels and non-vessel pixels based on results of the expert classifiers. Qualitative evaluation shows the effectiveness of the proposed expert classifiers in modeling retinal pathologies. Quantitative results on two standard datasets STARE (AUC = 0.971, Acc=0.927) and DRIVE (AUC = 0.955, Acc =0.903) are comparable with other state-of-the-art vessel segmentation methods.

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.

Enhancing Image Characteristics of Retinal Images of Aggressive Posterior Retinopathy of Prematurity Using a Novel Software, (RetiView)

Purpose. To report pilot data from a novel image analysis software "RetiView," to highlight clinically relevant information in RetCam images of infants with aggressive posterior retinopathy of prematurity (APROP). Methods. Twenty-three imaging sessions of consecutive infants of Asian Indian origin with clinically diagnosed APROP underwent three protocols (Grey Enhanced (GE), Color Enhanced (CE), and "Vesselness Measure" (VNM)) of the software. The postprocessed images were compared to baseline data from the archived unprocessed images and clinical exam by the retinopathy of prematurity (ROP) specialist for anterior extent of the vessels, capillary nonperfusion zones (CNP), loops, hemorrhages, and flat neovascularization. Results. There was better visualization of tortuous loops in the GE protocol (56.5%); "bald" zones within the CNP zones (26.1%), hemorrhages (13%), and edge of the disease (34.8%) in the CE images; neovascularization on both GE and CE protocols (13% each); clinically relevant information in cases with poor pupillary dilatation (8.7%); anterior extent of vessels on the VNM protocol (13%) effecting a "reclassification" from zone 1 to zone 2 posterior. Conclusions. RetiView is a noninvasive and inexpensive method of customized image enhancement to detect clinically difficult characteristics in a subset of APROP images with a potential to influence treatment planning.

Analysis of underlying causes of inter-expert disagreement in retinopathy of prematurity diagnosis. Application of machine learning principles

OBJECTIVE

Inter-expert variability in image-based clinical diagnosis has been demonstrated in many diseases including retinopathy of prematurity (ROP), which is a disease affecting low birth weight infants and is a major cause of childhood blindness. In order to better understand the underlying causes of variability among experts, we propose a method to quantify the variability of expert decisions and analyze the relationship between expert diagnoses and features computed from the images. Identification of these features is relevant for development of computer-based decision support systems and educational systems in ROP, and these methods may be applicable to other diseases where inter-expert variability is observed.

METHODS

The experiments were carried out on a dataset of 34 retinal images, each with diagnoses provided independently by 22 experts. Analysis was performed using concepts of Mutual Information (MI) and Kernel Density Estimation. A large set of structural features (a total of 66) were extracted from retinal images. Feature selection was utilized to identify the most important features that correlated to actual clinical decisions by the 22 study experts. The best three features for each observer were selected by an exhaustive search on all possible feature subsets and considering joint MI as a relevance criterion. We also compared our results with the results of Cohen's Kappa [36] as an inter-rater reliability measure.

RESULTS

The results demonstrate that a group of observers (17 among 22) decide consistently with each other. Mean and second central moment of arteriolar tortuosity is among the reasons of disagreement between this group and the rest of the observers, meaning that the group of experts consider amount of tortuosity as well as the variation of tortuosity in the image.

CONCLUSION

Given a set of image-based features, the proposed analysis method can identify critical image-based features that lead to expert agreement and disagreement in diagnosis of ROP. Although tree-based features and various statistics such as central moment are not popular in the literature, our results suggest that they are important for diagnosis.

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

PURPOSE

To examine vascular tortuosity as a function of distance from the optic disk in infants with retinopathy of prematurity.

METHODS

Thirty-four wide-angle retinal images from infants with retinopathy of prematurity were reviewed by 22 experts. A reference standard for each image was defined as the diagnosis (plus vs. not plus) given by the majority of experts. Tortuosity, defined as vessel length divided by straight line distance between vessel end points, was calculated as a function of distance from the disk margin for arteries and veins using computer-based methods developed by the authors.

RESULTS

Mean cumulative tortuosity increased with distance from the disk margin, both in 13 images with plus disease (P = 0.007 for arterial tortuosity [n = 62 arteries], P < 0.001 for venous tortuosity [n = 58 veins] based on slope of best fit line by regression), and in 21 images without plus disease (P < 0.001 for arterial tortuosity [n = 94 arteries], P <0 .001 for venous tortuosity [n = 85 veins]). Images with plus disease had significantly higher vascular tortuosity than images without plus disease (P < 0.05), up to 7.0 disk diameters from the optic disk margin.

CONCLUSION

Vascular tortuosity was higher peripherally than centrally, both in images with and without plus disease, suggesting that peripheral retinal features may be relevant for retinopathy of prematurity diagnosis.

Quantification of vascular tortuosity as an early outcome measure in oxygen induced retinopathy (OIR)

Oxygen-induced retinopathy (OIR) in mice is a popular model system to study pathological angiogenesis in the retinal vasculature. The system is based on vessel depletion by exposure to hyperoxia, which results in acute retinal hypoxia upon return to room air. This hypoxia then triggers neovascularization in the remaining vessels after 5 days. Here we aimed to establish an additional and earlier experimental readout of the vascular response to hypoxia by quantifying the tortuosity of retinal arteries after 2 days. Mouse pups from three different mouse strains were exposed to hyperoxia from postnatal day (P) 7 to P12 and retinas were analysed at P12, P14 and P17. Hypoxia was assessed by staining with the hypoxia marker EF5 and by measuring Vegf mRNA by qPCR. The retinal vasculature was stained in whole mount retinas and tortuosity of radial arterioles was quantified. C57BL/6J mice were used because the vascular response at P17 is well characterised in this strain. We also used C3H/HeJ mice, which contain the retinal degeneration 1 (Rd1) mutation (Pde6b(Rd1)) and have abnormally thin retinas. These thinner, C3H/HeJ retinas do not become ischemic during the OIR model and do not develop neovascularization. They can therefore be used as a control. In addition, we included C3H/HeJ mice that lack the Rd1 mutation (C3H/He(Rd1-)), with normal thickness retinas, to control for strain differences between C57BL/6J and C3H/HeJ. Quantification of vessel tortuosity at P14 showed tortuous arteries in normal thickness retinas (C57BL/6J and C3H/He(Rd1-)) and straight arteries in the thin C3H/HeJ retinas. This correlated with hypoxia, which was severe in normal thickness retinas and mild in the thin C3H/HeJ retinas. Furthermore, at P17 the normal thickness retinas showed strong neovascularisation whereas in the thin C3H/HeJ retinas the retinal vasculature regenerated normally. In conclusion we have demonstrated that arterial tortuosity can act as an early readout for hypoxia in the OIR model before neovascularisation develops.

Plus disease in retinopathy of prematurity: qualitative analysis of diagnostic process by experts

IMPORTANCE

Plus disease is the most important parameter that characterizes severe treatment-requiring retinopathy of prematurity, yet diagnostic agreement among experts is imperfect and the precise factors involved in clinical diagnosis are unclear. This study is designed to address these gaps in knowledge by analyzing cognitive aspects of the plus disease diagnostic process by experts.

OBJECTIVE

To examine the diagnostic reasoning process of experts for plus disease in retinopathy of prematurity using qualitative research techniques.

DESIGN

Cognitive walk-through, with qualitative analysis of videotaped expert responses and quantitative analysis of expert diagnoses.

SETTING

Experimental setting in which experts were videotaped while reviewing study data.

PARTICIPANTS

A panel of international retinopathy of prematurity experts who had the experience of using qualitative retinal features as their primary basis for clinical diagnosis.

INTERVENTION

Six experts were video recorded while independently reviewing 7 wide-angle retinal images from infants with retinopathy of prematurity. Experts were asked to explain their diagnostic process in detail (think-aloud protocol), mark findings relevant to their reasoning, and diagnose each image (plus vs pre-plus vs neither). Subsequently, each expert viewed the images again while being asked to examine arteries and veins in isolation and answer specific questions. Video recordings were transcribed and reviewed. Diagnostic process of experts was analyzed using a published cognitive model.

MAIN OUTCOME AND MEASURES

Interexpert and intraexpert agreement.

RESULTS

Based on the think-aloud protocol, 5 of 6 experts agreed on the same diagnosis in 3 study images and 3 of 6 experts agreed in 3 images. When experts were asked to rank images in order of severity, the mean correlation coefficient between pairs of experts was 0.33 (range, -0.04 to 0.75). All experts considered arterial tortuosity and venous dilation while reviewing each image. Some considered venous tortuosity, arterial dilation, peripheral retinal features, and other factors. When experts were asked to rereview images to diagnose plus disease based strictly on definitions of sufficient arterial tortuosity and venous dilation, all but 1 expert changed their diagnosis compared with the think-aloud protocol.

CONCLUSIONS AND RELEVANCE

Diagnostic consistency in plus disease is imperfect. Experts differ in their reasoning process, retinal features that they focus on, and interpretations of the same features. Understanding these factors may improve diagnosis and education. Future research defining more precise diagnostic criteria may be warranted.

Image-level tortuosity estimation in wide-field retinal images from infants with Retinopathy of Prematurity

Tortuosity and dilation of retinal vessels are considered of primary importance for the diagnosis and follow-up of the Retinopathy of Prematurity (ROP) disease. We developed an algorithm to estimate vessel tortuosity in images acquired with a wide-field fundus camera in ROP subjects, offering clinicians a quantitative, objective, and reproducible diagnostic parameter. Vessels were manually traced in 20 images to provide error-free input data for the tortuosity estimation. At first we investigated different vessel-level measures, some including also caliber information. Then we used them to obtain different imagelevel tortuosity measures, which were eventually combined in a supervised approach to provide a tortuosity index capable to reproduce the clinical experts assessment.To provide manual assessment, the 20 images were independently ordered by increasing tortuosity by three clinical graders and three retinal imaging experts. The proposed tortuosity index obtains a Spearman’s correlation coefficient of 0.95 with ground truth, a performance comparable to the clinical graders’ one and better than the retinal imaging experts’ one.

Alterations of the tunica vasculosa lentis in the rat model of retinopathy of prematurity

PURPOSE

To study the relationship between retinal and tunica vasculosa lentis (TVL) disease in retinopathy of prematurity (ROP). Although the clinical hallmark of ROP is abnormal retinal blood vessels, the vessels of the anterior segment, including the TVL, are also altered.

METHODS

ROP was induced in Long-Evans pigmented and Sprague Dawley albino rats; room-air-reared (RAR) rats served as controls. Then, fluorescein angiographic images of the TVL and retinal vessels were serially obtained with a scanning laser ophthalmoscope near the height of retinal vascular disease, ~20 days of age, and again at 30 and 64 days of age. Additionally, electroretinograms (ERGs) were obtained prior to the first imaging session. The TVL images were analyzed for percent coverage of the posterior lens. The tortuosity of the retinal arterioles was determined using Retinal Image multiScale Analysis (Gelman et al. in Invest Ophthalmol Vis Sci 46:4734-4738, 2005).

RESULTS

In the youngest ROP rats, the TVL was dense, while in RAR rats, it was relatively sparse. By 30 days, the TVL in RAR rats had almost fully regressed, while in ROP rats, it was still pronounced. By the final test age, the TVL had completely regressed in both ROP and RAR rats. In parallel, the tortuous retinal arterioles in ROP rats resolved with increasing age. ERG components indicating postreceptoral dysfunction, the b-wave, and oscillatory potentials were attenuated in ROP rats.

CONCLUSIONS

These findings underscore the retinal vascular abnormalities and, for the first time, show abnormal anterior segment vasculature in the rat model of ROP. There is delayed regression of the TVL in the rat model of ROP. This demonstrates that ROP is a disease of the whole eye.