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Image analysis for retinopathy of prematurity diagnosis.

ABSTRACT: To review findings from the authors' published studies involving telemedicine and image analysis for retinopathy of prematurity (ROP) diagnosis.Twenty-two ROP experts interpreted a set of 34 wide-angle retinal images for presence of plus disease. For each image, a reference standard diagnosis was defined from expert consensus. A computer-based system was used to measure individual and linear combinations of image parameters for arteries and veins: integrated curvature (IC), diameter, and tortuosity index (TI). Sensitivity, specificity, and receiver operating characteristic areas under the curve (AUC) for plus disease diagnosis were determined for each expert. Sensitivity and specificity curves were calculated for the computer-based system by varying the diagnostic cutoffs for arterial IC and venous diameter. Individual vessels from the original 34 images were identified with particular diagnostic cutoffs, and combined into composite wide-angle images using graphics editing software.For plus disease diagnosis, expert sensitivity ranged from 0.308-1.000, specificity from 0.571-1.000, and AUC from 0.784 to 1.000. Among computer system parameters, one linear combination had AUC 0.967, which was greater than that of 18 of 22 (81.8%) experts. Composite computer-generated images were produced using the arterial IC and venous diameter values associated with 75% under-diagnosis of plus disease (ie, 25% sensitivity cutoff), 50% under-diagnosis of plus disease (ie, 50% sensitivity cutoff), and 25% under-diagnosis of plus disease (ie, 75% sensitivity cutoff).Computer-based image analysis has the potential to diagnose severe ROP with comparable or better accuracy than experts, and could provide added value to telemedicine systems. Future quantitative definitions of plus disease might improve diagnostic objectivity.

SUBMITTER: Chiang MF

PROVIDER: S-EPMC2765401 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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Image analysis for retinopathy of prematurity diagnosis.

Chiang Michael F MF Gelman Rony R Martinez-Perez M Elena ME Du Yunling E YE Casper Daniel S DS Currie Leanne M LM Shah Payal D PD Starren Justin J Flynn John T JT

Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus 20091001 5

<h4>Purpose</h4>To review findings from the authors' published studies involving telemedicine and image analysis for retinopathy of prematurity (ROP) diagnosis.<h4>Methods</h4>Twenty-two ROP experts interpreted a set of 34 wide-angle retinal images for presence of plus disease. For each image, a reference standard diagnosis was defined from expert consensus. A computer-based system was used to measure individual and linear combinations of image parameters for arteries and veins: integrated curva ...[more]

PMID: 19840720

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Project description:ImportanceTelemedicine in retinopathy of prematurity (ROP) has the potential for delivering timely care to premature infants at risk for serious ROP.ObjectiveTo describe the characteristics of eyes at risk for ROP to provide insights into what types of ROP are most easily detected early by image grading.Design, setting, and participantsSecondary analysis of eyes with referral-warranted (RW) ROP (stage 3 ROP, zone I ROP, plus disease) on diagnostic examination from the Telemedicine Approaches to Evaluating Acute-Phase Retinopathy of Prematurity (e-ROP) study was conducted from May 1, 2011, to October 31, 2013, in 1257 premature infants with birth weights less than 1251 g in 13 neonatal units in North America. Data analysis was performed between February 1, 2016, and June 5, 2017.InterventionsSerial imaging sessions with concurrent diagnostic examinations for ROP.Main outcomes and measuresTime of detecting RW-ROP on image evaluation compared with clinical examination.ResultsIn the e-ROP study, 246 infants (492 eyes) were included in the analysis; 138 (56.1%) were male. A total of 447 eyes had RW-ROP on diagnostic examination. Image grading in 123 infants (mean [SD] gestational age, 24.8 [1.4] weeks) detected RW-ROP earlier than diagnostic examination (early) in 191 (42.7%) eyes by about 15 days and detected RW-ROP in 123 infants (mean [SD] gestational age, 24.6 [1.5] weeks) at the same time (same) in 200 (44.7%) eyes. Most of the early eyes (153 [80.1%]) interpreted as being RW-ROP positive on imaging evaluation agreed with examination findings when the examination subsequently documented RW-ROP. At the sessions in which RW-ROP was first found by examination, stage 3 or more in 123 infants (mean [SD] gestational age, 24.8 [1.4] weeks) ROP was noted earlier on image evaluation in 151 of 191 early eyes (79.1%) and in 172 of 200 of same eyes (86.0%) (P = .08); the presence of zone I ROP was detected in 57 of 191 (29.8%) early eyes vs 64 of 200 (32.0%) same eyes (P = .90); and plus disease was noted in 30 of 191 (15.7%) early eyes and 45 of 200 (22.5%) same eyes (P = .08).Conclusions and relevanceIn both early and same eyes, zone I and/or stage 3 ROP determined a significant proportion of RW-ROP; plus disease played a relatively minor role. In most early RW-ROP eyes, the findings were consistent with clinical examination and/or image grading at the next session. Because ROP telemedicine is used more widely, development of standard approaches and protocols is essential.

Project description:ImportanceThe current guidelines for retinopathy of prematurity (ROP) detection programs in the United States include a range of birth weights (BWs) and gestational ages and likely require examinations of many premature infants who are at low risk for developing serious retinopathy.ObjectiveTo determine the incidence, onset, and early course of ROP in what to our knowledge is the largest cohort to date that is representative of infants who are undergoing ROP screening.Design, setting, and participantsThis secondary analysis of data from the Postnatal Growth and Retinopathy of Prematurity (G-ROP) retrospective cohort study was conducted in 29 hospitals in the United States and Canada between January 2006 and December 2011 and included 7483 infants who underwent serial ROP examinations.Main outcomes and measuresMost severe ROP in either eye, classified as no ROP, mild ROP, type 2 ROP, or type 1 ROP (per Early Treatment for ROP Study criteria). Onset at postmenstrual age for zone I disease and stage of ROP, plus disease, and treatment.ResultsThis study included 7483 infants with a mean (SD) BW of 1099 (259) g and a mean (SD) gestational age of 28 (3) weeks who underwent ROP examinations. Of these, 3224 infants (43.1%) developed ROP, 459 (6.1%) developed type 1 and 472 (6.3%) type 2 ROP, 514 (6.9%) underwent treatment in 1 or both eyes, and 147 (2%) had zone I disease. Additionally, 98.1% of type 1 or 2 ROP cases occurred in infants with a BW of less than 1251 g. Only about half of the eyes (49.4%) were vascularized into zone III by 37 weeks postmenstrual age.Conclusions and relevanceThese findings add to our knowledge of ROP screening as they include all eligible infants, not just high-risk infants as in previous studies. More than 40% of at-risk premature infants develop some stage of ROP, and most retinopathy regresses without treatment. However, approximately 12.5% develop severe ROP, which occurs almost exclusively among infants with a BW of less than 1251 g.

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.

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Image analysis for retinopathy of prematurity diagnosis.

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Image analysis for retinopathy of prematurity diagnosis.

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