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

ABSTRACT: 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.

SUBMITTER: Kim SJ

PROVIDER: S-EPMC6145779 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Importance:Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The decision to treat is primarily based on the presence of plus disease, defined as dilation and tortuosity of retinal vessels. However, clinical diagnosis of plus disease is highly subjective and variable. Objective:To implement and validate an algorithm based on deep learning to automatically diagnose plus disease from retinal photographs. Design, Setting, and Participants:A deep convolutional neural network was trained using a data set of 5511 retinal photographs. Each image was previously assigned a reference standard diagnosis (RSD) based on consensus of image grading by 3 experts and clinical diagnosis by 1 expert (ie, normal, pre-plus disease, or plus disease). The algorithm was evaluated by 5-fold cross-validation and tested on an independent set of 100 images. Images were collected from 8 academic institutions participating in the Imaging and Informatics in ROP (i-ROP) cohort study. The deep learning algorithm was tested against 8 ROP experts, each of whom had more than 10 years of clinical experience and more than 5 peer-reviewed publications about ROP. Data were collected from July 2011 to December 2016. Data were analyzed from December 2016 to September 2017. Exposures:A deep learning algorithm trained on retinal photographs. Main Outcomes and Measures:Receiver operating characteristic analysis was performed to evaluate performance of the algorithm against the RSD. Quadratic-weighted ? coefficients were calculated for ternary classification (ie, normal, pre-plus disease, and plus disease) to measure agreement with the RSD and 8 independent experts. Results:Of the 5511 included retinal photographs, 4535 (82.3%) were graded as normal, 805 (14.6%) as pre-plus disease, and 172 (3.1%) as plus disease, based on the RSD. Mean (SD) area under the receiver operating characteristic curve statistics were 0.94 (0.01) for the diagnosis of normal (vs pre-plus disease or plus disease) and 0.98 (0.01) for the diagnosis of plus disease (vs normal or pre-plus disease). For diagnosis of plus disease in an independent test set of 100 retinal images, the algorithm achieved a sensitivity of 93% with 94% specificity. For detection of pre-plus disease or worse, the sensitivity and specificity were 100% and 94%, respectively. On the same test set, the algorithm achieved a quadratic-weighted ? coefficient of 0.92 compared with the RSD, outperforming 6 of 8 ROP experts. Conclusions and Relevance:This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts. This has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.

Project description: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.

Project description:Background: Oral propranolol reduces retinopathy of prematurity (ROP) progression, although not safely. Propranolol 0.1% eye micro-drops administered to newborns with stage 2 ROP are well-tolerated, but not sufficiently effective. Methods: A multi-center open-label trial was conducted to assess the safety and efficacy of propranolol 0.2% eye micro-drops in newborns with stage 1 ROP. The progression of the disease was evaluated with serial ophthalmologic examinations. Hemodynamic, respiratory, biochemical parameters, and propranolol plasma levels were monitored. Demographic and perinatal characteristics, co-morbidities and co-intervention incidences, together with ROP progression, were compared with a historical control group in the same centers participating in the trial. Results: Ninety-eight newborns were enrolled and compared with the historical control group. Populations were not perfectly homogeneous (as demonstrated by the differences in the Apgar score and the different incidence rate in surfactant administration and oxygen exposure). The progression to ROP stage 2 or 3 plus was significantly lower than the incidence expected on the basis of historical data (Risk Ratio 0.521, 95% CI 0.297- 0.916). No adverse effects related to propranolol were observed and the mean propranolol plasma level was significantly lower than the safety cut-off of 20 ng/mL. Unexpectedly, three newborns treated with oral propranolol before the appearance of ROP, showed a ROP that was unresponsive to propranolol eye micro-drops and required laser photocoagulation treatment. Conclusion: Propranolol 0.2% eye micro-drops were well-tolerated and appeared to reduce the ROP progression expected on the basis of a comparison with a historical control group. Propranolol administered too early appears to favor a more aggressive ROP, suggesting that a β-adrenoreceptor blockade is only useful during the proliferative phase. Further randomized placebo-controlled trials are required to confirm the current results. Clinical Trial Registration The trial was registered at ClinicalTrials.gov with Identifier NCT02504944 and with EudraCT Number 2014-005472-29.

Project description:ImportanceThe Surfactant, Positive Pressure, and Pulse Oximetry Randomized Trial (SUPPORT) demonstrated that static low oxygen saturation decreased retinopathy of prematurity (ROP) but increased mortality compared with static high oxygen saturation cohorts.ObjectiveTo compare outcomes of a biphasic oxygen protocol with static targets recommended by SUPPORT.Design, setting, and participantsRetrospective cohort study comparing biphasic vs static standards 41 months prior to and 42 months after a change from biphasic to static SUPPORT standards at a level III neonatal intensive care unit (Fairview Hospital, Cleveland, Ohio). The study included infants born at a corrected gestational age (CGA) of 31 weeks or younger or birth weight 1500 g or less. Data were analyzed between August 2010 and July 2017.InterventionsThe pre-SUPPORT group underwent biphasic protocol target saturations of 85% to 92% at younger than 34 weeks' CGA and greater than 95% at 34 weeks' CGA or older. The post-SUPPORT group underwent a constant 91% to 95% target.Main outcomes and measuresPrimary outcome was incidence of type 1 ROP. Secondary outcomes were incidence of any ROP, time to full vascularization, and mortality.ResultsOf 596 eligible infants, 562 were included in ophthalmic analysis. Three hundred three patients were boys (54%); 399 were white (71%), 87 were black (15%), and 76 were of other or unknown race/ethnicity (14%). Mean (SD) CGA and birth weight were 29 (2) weeks and 1151 (346) g, respectively. Any ROP overall increased (53 [20%] pre-SUPPORT vs n = 86 [28%] post-SUPPORT; absolute difference, 8%; 95% CI, 1%-15%; odds ratio, 1.6; 95% CI, 1.05-2.3; P = .03). Type 1 ROP increased in the post-SUPPORT era (n = 6 [2%] pre-SUPPORT vs n = 18 [6%] post-SUPPORT; absolute difference, 4%; 95% CI, 0.4%-7%; odds ratio, 2.7; 95% CI, 1.05-6.9; P = .03). There was a delay in vascularization in the post-SUPPORT group (n = 6 [2%] pre-SUPPORT vs n = 18 [6%] post-SUPPORT; absolute difference, 4%; 95% CI, 0.4%-7%; P = .03).Conclusions and relevanceCompared with static oxygen standards, biphasic oxygen targets are associated with decreased incidence and severity of ROP without increasing mortality.

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

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