Publications by authors named "Susan Ostmo"

45 Publications

105° field of view non-contact handheld swept-source optical coherence tomography.

Opt Lett 2021 Dec;46(23):5878-5881

We demonstrate a handheld swept-source optical coherence tomography (OCT) system with a 400 kHz vertical-cavity surface-emitting laser (VCSEL) light source, a non-contact approach, and an unprecedented single shot 105° field of view (FOV). We also implemented a spiral scanning pattern allowing real-time visualization with improved scanning efficiency. To the best of our knowledge, this is the widest FOV achieved in a portable non-contact OCT retinal imaging system to date. Improvements to the FOV may aid the evaluation of retinal diseases such as retinopathy of prematurity, where important vitreoretinal changes often occur in the peripheral retina.
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http://dx.doi.org/10.1364/OL.443672DOI Listing
December 2021

International Classification of Retinopathy of Prematurity, Third Edition.

Ophthalmology 2021 10 8;128(10):e51-e68. Epub 2021 Jul 8.

Department of Pediatric Retina, Narayana Nethralaya Eye Institute, Bangalore, Karnataka, India.

Purpose: The International Classification of Retinopathy of Prematurity is a consensus statement that creates a standard nomenclature for classification of retinopathy of prematurity (ROP). It was initially published in 1984, expanded in 1987, and revisited in 2005. This article presents a third revision, the International Classification of Retinopathy of Prematurity, Third Edition (ICROP3), which is now required because of challenges such as: (1) concerns about subjectivity in critical elements of disease classification; (2) innovations in ophthalmic imaging; (3) novel pharmacologic therapies (e.g., anti-vascular endothelial growth factor agents) with unique regression and reactivation features after treatment compared with ablative therapies; and (4) recognition that patterns of ROP in some regions of the world do not fit neatly into the current classification system.

Design: Review of evidence-based literature, along with expert consensus opinion.

Participants: International ROP expert committee assembled in March 2019 representing 17 countries and comprising 14 pediatric ophthalmologists and 20 retinal specialists, as well as 12 women and 22 men.

Methods: The committee was initially divided into 3 subcommittees-acute phase, regression or reactivation, and imaging-each of which used iterative videoconferences and an online message board to identify key challenges and approaches. Subsequently, the entire committee used iterative videoconferences, 2 in-person multiday meetings, and an online message board to develop consensus on classification.

Main Outcome Measures: Consensus statement.

Results: The ICROP3 retains current definitions such as zone (location of disease), stage (appearance of disease at the avascular-vascular junction), and circumferential extent of disease. Major updates in the ICROP3 include refined classification metrics (e.g., posterior zone II, notch, subcategorization of stage 5, and recognition that a continuous spectrum of vascular abnormality exists from normal to plus disease). Updates also include the definition of aggressive ROP to replace aggressive-posterior ROP because of increasing recognition that aggressive disease may occur in larger preterm infants and beyond the posterior retina, particularly in regions of the world with limited resources. ROP regression and reactivation are described in detail, with additional description of long-term sequelae.

Conclusions: These principles may improve the quality and standardization of ROP care worldwide and may provide a foundation to improve research and clinical care.
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http://dx.doi.org/10.1016/j.ophtha.2021.05.031DOI Listing
October 2021

High-speed and widefield handheld swept-source OCT angiography with a VCSEL light source.

Biomed Opt Express 2021 Jun 20;12(6):3553-3570. Epub 2021 May 20.

Casey Eye Institute, Oregon Health and Science University, Portland, OR 97239, USA.

Optical coherence tomography (OCT) and OCT angiography (OCTA) enable noninvasive structural and angiographic imaging of the eye. Portable handheld OCT/OCTA systems are required for imaging patients in the supine position. Examples include infants in the neonatal intensive care unit (NICU) and operating room (OR). The speed of image acquisition plays a pivotal role in acquiring high-quality OCT/OCTA images, particularly with the handheld system, since both the operator hand tremor and subject motion can cause significant motion artifacts. In addition, having a large field of view and the ability of real-time data visualization are critical elements in rapid disease screening, reducing imaging time, and detecting peripheral retinal pathologies. The arrangement of optical components is less flexible in the handheld system due to the limitation of size and weight. In this paper, we introduce a 400-kHz, 55-degree field of view handheld OCT/OCTA system that has overcome many technical challenges as a portable OCT system as well as a high-speed OCTA system. We demonstrate imaging premature infants with retinopathy of prematurity (ROP) in the NICU, a patient with incontinentia pigmenti (IP), and a patient with X-linked retinoschisis (XLRS) in the OR using our handheld OCT system. Our design may have the potential for improving the diagnosis of retinal diseases and help provide a practical guideline for designing a flexible and portable OCT system.
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http://dx.doi.org/10.1364/BOE.425411DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8221946PMC
June 2021

Diagnosability of Synthetic Retinal Fundus Images for Plus Disease Detection in Retinopathy of Prematurity.

AMIA Annu Symp Proc 2020 25;2020:329-337. Epub 2021 Jan 25.

Medical Informatics & Clinical Epidemiology.

Advances in generative adversarial networks have allowed for engineering of highly-realistic images. Many studies have applied these techniques to medical images. However, evaluation of generated medical images often relies upon image quality and reconstruction metrics, and subjective evaluation by laypersons. This is acceptable for generation of images depicting everyday objects, but not for medical images, where there may be subtle features experts rely upon for diagnosis. We implemented the pix2pix generative adversarial network for retinal fundus image generation, and evaluated the ability of experts to identify generated images as such and to form accurate diagnoses of plus disease in retinopathy of prematurity. We found that, while experts could discern between real and generated images, the diagnoses between image sets were similar. By directly evaluating and confirming physicians' abilities to diagnose generated retinal fundus images, this work supports conclusions that generated images may be viable for dataset augmentation and physician training.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075515PMC
June 2021

Neurodevelopmental outcomes in preterm infants with retinopathy of prematurity.

Surv Ophthalmol 2021 Sep-Oct;66(5):877-891. Epub 2021 Mar 2.

Casey Eye Institute, Department of Ophthalmology, Oregon Health and Science University, Portland, OR, USA. Electronic address:

Over the past decade there has been a paradigm shift in the treatment of retinopathy of prematurity (ROP) with the introduction of antivascular endothelial growth factor (anti-VEGF) treatments. Anti-VEGF agents have the advantages of being easier to administer, requiring less anesthesia, having the potential for improved peripheral vision, and producing less refractive error than laser treatment. On the other hand, it is known that intravitreal administration of anti-VEGF agents lowers VEGF levels in the blood and raises the theoretical concern of intraocular anti-VEGF causing deleterious effects in other organ systems, including the brain. As a result, there has been increased attention recently on neurodevelopmental outcomes in infants treated with anti-VEGF agents. These studies should be put into context with what is known about systemic comorbidities, socioeconomic influences, and the effects of extreme prematurity itself on neurodevelopmental outcomes. We summarize what is known about neurodevelopmental outcomes in extremely preterm infants with ROP, discuss the implications for determining the neurodevelopmental status using neurodevelopmental testing as well as other indicators, and review the existing literature relating to neurodevelopmental outcomes in babies treated for ROP.
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http://dx.doi.org/10.1016/j.survophthal.2021.02.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351023PMC
March 2021

Identification of candidate genes and pathways in retinopathy of prematurity by whole exome sequencing of preterm infants enriched in phenotypic extremes.

Sci Rep 2021 03 2;11(1):4966. Epub 2021 Mar 2.

Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, 3375 SW Terwilliger Boulevard, Portland, OR, 97239, USA.

Retinopathy of prematurity (ROP) is a vasoproliferative retinal disease affecting premature infants. In addition to prematurity itself and oxygen treatment, genetic factors have been suggested to predispose to ROP. We aimed to identify potentially pathogenic genes and biological pathways associated with ROP by analyzing variants from whole exome sequencing (WES) data of premature infants. As part of a multicenter ROP cohort study, 100 non-Hispanic Caucasian preterm infants enriched in phenotypic extremes were subjected to WES. Gene-based testing was done on coding nonsynonymous variants. Genes showing enrichment of qualifying variants in severe ROP compared to mild or no ROP from gene-based tests with adjustment for gestational age and birth weight were selected for gene set enrichment analysis (GSEA). Mean BW of included infants with pre-plus, type-1 or type 2 ROP including aggressive posterior ROP (n = 58) and mild or no ROP (n = 42) were 744 g and 995 g, respectively. No single genes reached genome-wide significance that could account for a severe phenotype. GSEA identified two significantly associated pathways (smooth endoplasmic reticulum and vitamin C metabolism) after correction for multiple tests. WES of premature infants revealed potential pathways that may be important in the pathogenesis of ROP and in further genetic studies.
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http://dx.doi.org/10.1038/s41598-021-83552-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925531PMC
March 2021

Applications of Artificial Intelligence for Retinopathy of Prematurity Screening.

Pediatrics 2021 03;147(3)

Athinoula A. Martinos Center for Biomedical Imaging and Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts.

Objectives: Childhood blindness from retinopathy of prematurity (ROP) is increasing as a result of improvements in neonatal care worldwide. We evaluate the effectiveness of artificial intelligence (AI)-based screening in an Indian ROP telemedicine program and whether differences in ROP severity between neonatal care units (NCUs) identified by using AI are related to differences in oxygen-titrating capability.

Methods: External validation study of an existing AI-based quantitative severity scale for ROP on a data set of images from the Retinopathy of Prematurity Eradication Save Our Sight ROP telemedicine program in India. All images were assigned an ROP severity score (1-9) by using the Imaging and Informatics in Retinopathy of Prematurity Deep Learning system. We calculated the area under the receiver operating characteristic curve and sensitivity and specificity for treatment-requiring retinopathy of prematurity. Using multivariable linear regression, we evaluated the mean and median ROP severity in each NCU as a function of mean birth weight, gestational age, and the presence of oxygen blenders and pulse oxygenation monitors.

Results: The area under the receiver operating characteristic curve for detection of treatment-requiring retinopathy of prematurity was 0.98, with 100% sensitivity and 78% specificity. We found higher median (interquartile range) ROP severity in NCUs without oxygen blenders and pulse oxygenation monitors, most apparent in bigger infants (>1500 g and 31 weeks' gestation: 2.7 [2.5-3.0] vs 3.1 [2.4-3.8]; = .007, with adjustment for birth weight and gestational age).

Conclusions: Integration of AI into ROP screening programs may lead to improved access to care for secondary prevention of ROP and may facilitate assessment of disease epidemiology and NCU resources.
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http://dx.doi.org/10.1542/peds.2020-016618DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924138PMC
March 2021

Deep Learning for the Diagnosis of Stage in Retinopathy of Prematurity: Accuracy and Generalizability across Populations and Cameras.

Ophthalmol Retina 2021 10 6;5(10):1027-1035. Epub 2021 Feb 6.

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon. Electronic address:

Purpose: Stage is an important feature to identify in retinal images of infants at risk of retinopathy of prematurity (ROP). The purpose of this study was to implement a convolutional neural network (CNN) for binary detection of stages 1, 2, and 3 in ROP and to evaluate its generalizability across different populations and camera systems.

Design: Diagnostic validation study of CNN for stage detection.

Participants: Retinal fundus images obtained from preterm infants during routine ROP screenings.

Methods: Two datasets were used: 5943 fundus images obtained by RetCam camera (Natus Medical, Pleasanton, CA) from 9 North American institutions and 5049 images obtained by 3nethra camera (Forus Health Incorporated, Bengaluru, India) from 4 hospitals in Nepal. Images were labeled based on the presence of stage by 1 to 3 expert graders. Three CNN models were trained using 5-fold cross-validation on datasets from North America alone, Nepal alone, and a combined dataset and were evaluated on 2 held-out test sets consisting of 708 and 247 images from the Nepali and North American datasets, respectively.

Main Outcome Measures: Convolutional neural network performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, and specificity.

Results: Both the North American- and Nepali-trained models demonstrated high performance on a test set from the same population: AUROC, 0.99; AUPRC, 0.98; sensitivity, 94%; and AUROC, 0.97; AUPRC, 0.91; and sensitivity, 73%; respectively. However, the performance of each model decreased to AUROC of 0.96 and AUPRC of 0.88 (sensitivity, 52%) and AUROC of 0.62 and AUPRC of 0.36 (sensitivity, 44%) when evaluated on a test set from the other population. Compared with the models trained on individual datasets, the model trained on a combined dataset achieved improved performance on each respective test set: sensitivity improved from 94% to 98% on the North American test set and from 73% to 82% on the Nepali test set.

Conclusions: A CNN can identify accurately the presence of ROP stage in retinal images, but performance depends on the similarity between training and testing populations. We demonstrated that internal and external performance can be improved by increasing the heterogeneity of the training dataset features of the training dataset, in this case by combining images from different populations and cameras.
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http://dx.doi.org/10.1016/j.oret.2020.12.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364291PMC
October 2021

Evaluation of a Deep Learning-Derived Quantitative Retinopathy of Prematurity Severity Scale.

Ophthalmology 2021 07 27;128(7):1070-1076. Epub 2020 Oct 27.

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon; Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon. Electronic address:

Purpose: To evaluate the clinical usefulness of a quantitative deep learning-derived vascular severity score for retinopathy of prematurity (ROP) by assessing its correlation with clinical ROP diagnosis and by measuring clinician agreement in applying a novel scale.

Design: Analysis of existing database of posterior pole fundus images and corresponding ophthalmoscopic examinations using 2 methods of assigning a quantitative scale to vascular severity.

Participants: Images were from clinical examinations of patients in the Imaging and Informatics in ROP Consortium. Four ophthalmologists and 1 study coordinator evaluated vascular severity on a scale from 1 to 9.

Methods: A quantitative vascular severity score (1-9) was applied to each image using a deep learning algorithm. A database of 499 images was developed for assessment of interobserver agreement.

Main Outcome Measures: Distribution of deep learning-derived vascular severity scores with the clinical assessment of zone (I, II, or III), stage (0, 1, 2, or 3), and extent (<3 clock hours, 3-6 clock hours, and >6 clock hours) of stage 3 evaluated using multivariate linear regression and weighted κ values and Pearson correlation coefficients for interobserver agreement on a 1-to-9 vascular severity scale.

Results: For deep learning analysis, a total of 6344 clinical examinations were analyzed. A higher deep learning-derived vascular severity score was associated with more posterior disease, higher disease stage, and higher extent of stage 3 disease (P < 0.001 for all). For a given ROP stage, the vascular severity score was higher in zone I than zones II or III (P < 0.001). Multivariate regression found zone, stage, and extent all were associated independently with the severity score (P < 0.001 for all). For interobserver agreement, the mean ± standard deviation weighted κ value was 0.67 ± 0.06, and the Pearson correlation coefficient ± standard deviation was 0.88 ± 0.04 on the use of a 1-to-9 vascular severity scale.

Conclusions: A vascular severity scale for ROP seems feasible for clinical adoption; corresponds with zone, stage, extent of stage 3, and plus disease; and facilitates the use of objective technology such as deep learning to improve the consistency of ROP diagnosis.
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http://dx.doi.org/10.1016/j.ophtha.2020.10.025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076329PMC
July 2021

Development of Screening Criteria for Retinopathy of Prematurity in Ulaanbaatar, Mongolia, Using a Web-based Data Management System.

J Pediatr Ophthalmol Strabismus 2020 Sep;57(5):333-339

Purpose: To describe a process for identifying birth weight (BW) and gestational age (GA) screening guidelines in Mongolia.

Methods: This was a prospective cohort study in a tertiary care hospital in Ulaanbataar, Mongolia, of 193 premature infants with GA of 36 weeks or younger and/or BW of 2,000 g or less) with regression analysis to determine associations between BW and GA and the development of retinopathy of prematurity (ROP).

Results: As BW and GA decreased, the relative risk of developing ROP increased. The relative risk of developing any stage of ROP in infants born at 29 weeks or younger was 2.91 (95% CI: 1.55 to 5.44; P < .001] compared to older infants. The relative risk of developing any type of ROP in infants with BW of less than 1,200 g was 2.41 (95% CI: 1.35 to 4.29; P = .003] and developing type 2 or worse ROP was 2.05 (95% CI: 0.99 to 4.25; P = .05).

Conclusions: Infants in Mongolia with heavier BW and older GA who fall outside of current United States screening guidelines of GA of 30 weeks or younger and/or BW of 1,500 g or less developed clinically relevant ROP. [J Pediatr Ophthalmol Strabismus. 2020;57(5):333-339.].
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http://dx.doi.org/10.3928/01913913-20200804-01DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880618PMC
September 2020

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

Transl Vis Sci Technol 2020 02 14;9(2):10. Epub 2020 Feb 14.

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA.

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.
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http://dx.doi.org/10.1167/tvst.9.2.10DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346878PMC
February 2020

Variability in Plus Disease Identified Using a Deep Learning-Based Retinopathy of Prematurity Severity Scale.

Ophthalmol Retina 2020 10 4;4(10):1016-1021. Epub 2020 May 4.

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon. Electronic address:

Purpose: Retinopathy of prematurity is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective, which leads to treatment differences. Our goal was to determine objective differences in the diagnosis of plus disease between clinicians using an automated retinopathy of prematurity (ROP) vascular severity score.

Design: This retrospective cohort study used data from the Imaging and Informatics in ROP Consortium, which comprises 8 tertiary care centers in North America. Fundus photographs of all infants undergoing ROP screening examinations between July 1, 2011, and December 31, 2016, were obtained.

Participants: Infants meeting ROP screening criteria who were diagnosed with plus disease and treatment initiated by an examining physician based on ophthalmoscopic examination results.

Methods: An ROP severity score (1-9) was generated for each image using a deep learning (DL) algorithm.

Main Outcome Measures: The mean, median, and range of ROP vascular severity scores overall and for each examiner when the diagnosis of plus disease was made.

Results: A total of 5255 clinical examinations in 871 babies were analyzed. Of these, 168 eyes were diagnosed with plus disease by 11 different examiners and were included in the study. The mean ± standard deviation vascular severity score for patients diagnosed with plus disease was 7.4 ± 1.9, median was 8.5 (interquartile range, 5.8-8.9), and range was 1.1 to 9.0. Within some examiners, variability in the level of vascular severity diagnosed as plus disease was present, and 1 examiner routinely diagnosed plus disease in patients with less severe disease than the other examiners (P < 0.01).

Conclusions: We observed variability both between and within examiners in the diagnosis of plus disease using DL. Prospective evaluation of clinical trial data using an objective measurement of vascular severity may help to define better the minimum necessary level of vascular severity for the diagnosis of plus disease or how other clinical features such as zone, stage, and extent of peripheral disease ought to be incorporated in treatment decisions.
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http://dx.doi.org/10.1016/j.oret.2020.04.022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867469PMC
October 2020

Evaluation of artificial intelligence-based telemedicine screening for retinopathy of prematurity.

J AAPOS 2020 06 11;24(3):160-162. Epub 2020 Apr 11.

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland. Electronic address:

Retrospective evaluation of a deep learning-derived retinopathy of prematurity (ROP) vascular severity score in an operational ROP screening program demonstrated high diagnostic performance for detection of type 2 or worse ROP. To our knowledge, this is the first report in the literature that evaluated the use of artificial intelligence for ROP screening and represents a proof of concept. With further prospective validation, this technology might improve the accuracy, efficiency, and objectivity of diagnosis and facilitate earlier detection of disease progression in patients with potentially blinding ROP.
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http://dx.doi.org/10.1016/j.jaapos.2020.01.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508795PMC
June 2020

Aggressive Posterior Retinopathy of Prematurity: Clinical and Quantitative Imaging Features in a Large North American Cohort.

Ophthalmology 2020 08 7;127(8):1105-1112. Epub 2020 Feb 7.

Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon. Electronic address:

Purpose: Aggressive posterior retinopathy of prematurity (AP-ROP) is a vision-threatening disease with a significant rate of progression to retinal detachment. The purpose of this study was to characterize AP-ROP quantitatively by demographics, rate of disease progression, and a deep learning-based vascular severity score.

Design: Retrospective analysis.

Participants: The Imaging and Informatics in ROP cohort from 8 North American centers, consisting of 947 patients and 5945 clinical eye examinations with fundus images, was used. Pretreatment eyes were categorized by disease severity: none, mild, type 2 or pre-plus, treatment-requiring (TR) without AP-ROP, TR with AP-ROP. Analyses compared TR with AP-ROP and TR without AP-ROP to investigate differences between AP-ROP and other TR disease.

Methods: A reference standard diagnosis was generated for each eye examination using previously published methods combining 3 independent image-based gradings and 1 ophthalmoscopic grading. All fundus images were analyzed using a previously published deep learning system and were assigned a score from 1 through 9.

Main Outcome Measures: Birth weight, gestational age, postmenstrual age, and vascular severity score.

Results: Infants who demonstrated AP-ROP were more premature by birth weight (617 g vs. 679 g; P = 0.01) and gestational age (24.3 weeks vs. 25.0 weeks; P < 0.01) and reached peak severity at an earlier postmenstrual age (34.7 weeks vs. 36.9 weeks; P < 0.001) compared with infants with TR without AP-ROP. The mean vascular severity score was greatest in TR with AP-ROP infants compared with TR without AP-ROP infants (8.79 vs. 7.19; P < 0.001). Analyzing the severity score over time, the rate of progression was fastest in infants with AP-ROP (P < 0.002 at 30-32 weeks).

Conclusions: Premature infants in North America with AP-ROP are born younger and demonstrate disease earlier than infants with less severe ROP. Disease severity is quantifiable with a deep learning-based score, which correlates with clinically identified categories of disease, including AP-ROP. The rate of progression to peak disease is greatest in eyes that demonstrate AP-ROP compared with other treatment-requiring eyes. Analysis of quantitative characteristics of AP-ROP may help improve diagnosis and treatment of an aggressive, vision-threatening form of ROP.
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http://dx.doi.org/10.1016/j.ophtha.2020.01.052DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384953PMC
August 2020

Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning.

JAMA Ophthalmol 2019 Jul 3. Epub 2019 Jul 3.

Department of Ophthalmology, Casey Eye institute, Oregon Health & Science University, Portland.

Importance: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective and qualitative.

Objective: To describe a quantitative ROP severity score derived using a deep learning algorithm designed to evaluate plus disease and to assess its utility for objectively monitoring ROP progression.

Design, Setting, And Participants: This retrospective cohort study included images from 5255 clinical examinations of 871 premature infants who met the ROP screening criteria of the Imaging and Informatics in ROP (i-ROP) Consortium, which comprises 9 tertiary care centers in North America, from July 1, 2011, to December 31, 2016. Data analysis was performed from July 2017 to May 2018.

Exposure: A deep learning algorithm was used to assign a continuous ROP vascular severity score from 1 (most normal) to 9 (most severe) at each examination based on a single posterior photograph compared with a reference standard diagnosis (RSD) simplified into 4 categories: no ROP, mild ROP, type 2 ROP or pre-plus disease, or type 1 ROP. Disease course was assessed longitudinally across multiple examinations for all patients.

Main Outcomes And Measures: Mean ROP vascular severity score progression over time compared with the RSD.

Results: A total of 5255 clinical examinations from 871 infants (mean [SD] gestational age, 27.0 [2.0] weeks; 493 [56.6%] male; mean [SD] birth weight, 949 [271] g) were analyzed. The median severity scores for each category were as follows: 1.1 (interquartile range [IQR], 1.0-1.5) (no ROP), 1.5 (IQR, 1.1-3.4) (mild ROP), 4.6 (IQR, 2.4-5.3) (type 2 and pre-plus), and 7.5 (IQR, 5.0-8.7) (treatment-requiring ROP) (P < .001). When the long-term differences in the median severity scores across time between the eyes progressing to treatment and those who did not eventually require treatment were compared, the median score was higher in the treatment group by 0.06 at 30 to 32 weeks, 0.75 at 32 to 34 weeks, 3.56 at 34 to 36 weeks, 3.71 at 36 to 38 weeks, and 3.24 at 38 to 40 weeks postmenstrual age (P < .001 for all comparisons).

Conclusions And Relevance: The findings suggest that the proposed ROP vascular severity score is associated with category of disease at a given point in time and clinical progression of ROP in premature infants. Automated image analysis may be used to quantify clinical disease progression and identify infants at high risk for eventually developing treatment-requiring ROP. This finding has implications for quality and delivery of ROP care and for future approaches to disease classification.
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http://dx.doi.org/10.1001/jamaophthalmol.2019.2433DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6613341PMC
July 2019

A Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning to Monitor Disease Regression After Treatment.

JAMA Ophthalmol 2019 Jul 3. Epub 2019 Jul 3.

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland.

Importance: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide, but treatment failure and disease recurrence are important causes of adverse outcomes in patients with treatment-requiring ROP (TR-ROP).

Objectives: To apply an automated ROP vascular severity score obtained using a deep learning algorithm and to assess its utility for objectively monitoring ROP regression after treatment.

Design, Setting, And Participants: This retrospective cohort study used data from the Imaging and Informatics in ROP consortium, which comprises 9 tertiary referral centers in North America that screen high volumes of at-risk infants for ROP. Images of 5255 clinical eye examinations from 871 infants performed between July 2011 and December 2016 were assessed for eligibility in the present study. The disease course was assessed with time across the numerous examinations for patients with TR-ROP. Infants born prematurely meeting screening criteria for ROP who developed TR-ROP and who had images captured within 4 weeks before and after treatment as well as at the time of treatment were included.

Main Outcomes And Measures: The primary outcome was mean (SD) ROP vascular severity score before, at time of, and after treatment. A deep learning classifier was used to assign a continuous ROP vascular severity score, which ranged from 1 (normal) to 9 (most severe), at each examination. A secondary outcome was the difference in ROP vascular severity score among eyes treated with laser or the vascular endothelial growth factor antagonist bevacizumab. Differences between groups for both outcomes were assessed using unpaired 2-tailed t tests with Bonferroni correction.

Results: Of 5255 examined eyes, 91 developed TR-ROP, of which 46 eyes met the inclusion criteria based on the available images. The mean (SD) birth weight of those patients was 653 (185) g, with a mean (SD) gestational age of 24.9 (1.3) weeks. The mean (SD) ROP vascular severity scores significantly increased 2 weeks prior to treatment (4.19 [1.75]), peaked at treatment (7.43 [1.89]), and decreased for at least 2 weeks after treatment (4.00 [1.88]) (all P < .001). Eyes requiring retreatment with laser had higher ROP vascular severity scores at the time of initial treatment compared with eyes receiving a single treatment (P < .001).

Conclusions And Relevance: This quantitative ROP vascular severity score appears to consistently reflect clinical disease progression and posttreatment regression in eyes with TR-ROP. These study results may have implications for the monitoring of patients with ROP for treatment failure and disease recurrence and for determining the appropriate level of disease severity for primary treatment in eyes with aggressive disease.
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http://dx.doi.org/10.1001/jamaophthalmol.2019.2442DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6613298PMC
July 2019

Classification and comparison via neural networks.

Neural Netw 2019 Oct 19;118:65-80. Epub 2019 Jun 19.

Department of Electrical and Computer Engineering, Northeastern University, 360 Huntington Avenue, 409 Dana, Boston, MA 02115, USA.

We consider learning from comparison labels generated as follows: given two samples in a dataset, a labeler produces a label indicating their relative order. Such comparison labels scale quadratically with the dataset size; most importantly, in practice, they often exhibit lower variance compared to class labels. We propose a new neural network architecture based on siamese networks to incorporate both class and comparison labels in the same training pipeline, using Bradley-Terry and Thurstone loss functions. Our architecture leads to a significant improvement in predicting both class and comparison labels, increasing classification AUC by as much as 35% and comparison AUC by as much as 6% on several real-life datasets. We further show that, by incorporating comparisons, training from few samples becomes possible: a deep neural network of 5.9 million parameters trained on 80 images attains a 0.92 AUC when incorporating comparisons.
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http://dx.doi.org/10.1016/j.neunet.2019.06.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6718310PMC
October 2019

Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.

Ophthalmol Retina 2019 05 31;3(5):444-450. Epub 2019 Jan 31.

Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon; Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon. Electronic address:

Purpose: Accurate image-based ophthalmic diagnosis relies on fundus image clarity. This has important implications for the quality of ophthalmic diagnoses and for emerging methods such as telemedicine and computer-based image analysis. The purpose of this study was to implement a deep convolutional neural network (CNN) for automated assessment of fundus image quality in retinopathy of prematurity (ROP).

Design: Experimental study.

Participants: Retinal fundus images were collected from preterm infants during routine ROP screenings.

Methods: Six thousand one hundred thirty-nine retinal fundus images were collected from 9 academic institutions. Each image was graded for quality (acceptable quality [AQ], possibly acceptable quality [PAQ], or not acceptable quality [NAQ]) by 3 independent experts. Quality was defined as the ability to assess an image confidently for the presence of ROP. Of the 6139 images, NAQ, PAQ, and AQ images represented 5.6%, 43.6%, and 50.8% of the image set, respectively. Because of low representation of NAQ images in the data set, images labeled NAQ were grouped into the PAQ category, and a binary CNN classifier was trained using 5-fold cross-validation on 4000 images. A test set of 2109 images was held out for final model evaluation. Additionally, 30 images were ranked from worst to best quality by 6 experts via pairwise comparisons, and the CNN's ability to rank quality, regardless of quality classification, was assessed.

Main Outcome Measures: The CNN performance was evaluated using area under the receiver operating characteristic curve (AUC). A Spearman's rank correlation was calculated to evaluate the overall ability of the CNN to rank images from worst to best quality as compared with experts.

Results: The mean AUC for 5-fold cross-validation was 0.958 (standard deviation, 0.005) for the diagnosis of AQ versus PAQ images. The AUC was 0.965 for the test set. The Spearman's rank correlation coefficient on the set of 30 images was 0.90 as compared with the overall expert consensus ranking.

Conclusions: This model accurately assessed retinal fundus image quality in a comparable manner with that of experts. This fully automated model has potential for application in clinical settings, telemedicine, and computer-based image analysis in ROP and for generalizability to other ophthalmic diseases.
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http://dx.doi.org/10.1016/j.oret.2019.01.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6501831PMC
May 2019

Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity.

AMIA Annu Symp Proc 2018 5;2018:1224-1232. Epub 2018 Dec 5.

Medical Informatics & Clinical Epidemiology, and.

Accurate image-based medical diagnosis relies upon adequate image quality and clarity. This has important implications for clinical diagnosis, and for emerging methods such as telemedicine and computer-based image analysis. In this study, we trained a convolutional neural network (CNN) to automatically assess the quality of retinal fundus images in a representative ophthalmic disease, retinopathy of prematurity (ROP). 6,043 wide-angle fundus images were collected from preterm infants during routine ROP screening examinations. Images were assessed by clinical experts for quality regarding ability to diagnose ROP accurately, and were labeled "acceptable" or "not acceptable." The CNN training, validation and test sets consisted of 2,770 images, 200 images, and 3,073 images, respectively. Test set accuracy was 89.1%, with area under the receiver operating curve equal to 0.964, and area under the precision-recall curve equal to 0.966. Taken together, our CNN shows promise as a useful prescreening method for telemedicine and computer-based image analysis applications. We feel this methodology is generalizable to all clinical domains involving image-based diagnosis.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371336PMC
December 2019

Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity.

Br J Ophthalmol 2018 Nov 23. Epub 2018 Nov 23.

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, USA

Background: Prior work has demonstrated the near-perfect accuracy of a deep learning retinal image analysis system for diagnosing plus disease in retinopathy of prematurity (ROP). Here we assess the screening potential of this scoring system by determining its ability to detect all components of ROP diagnosis.

Methods: Clinical examination and fundus photography were performed at seven participating centres. A deep learning system was trained to detect plus disease, generating a quantitative assessment of retinal vascular abnormality (the i-ROP plus score) on a 1-9 scale. Overall ROP disease category was established using a consensus reference standard diagnosis combining clinical and image-based diagnosis. Experts then ranked ordered a second data set of 100 posterior images according to overall ROP severity.

Results: 4861 examinations from 870 infants were analysed. 155 examinations (3%) had a reference standard diagnosis of type 1 ROP. The i-ROP deep learning (DL) vascular severity score had an area under the receiver operating curve of 0.960 for detecting type 1 ROP. Establishing a threshold i-ROP DL score of 3 conferred 94% sensitivity, 79% specificity, 13% positive predictive value and 99.7% negative predictive value for type 1 ROP. There was strong correlation between expert rank ordering of overall ROP severity and the i-ROP DL vascular severity score (Spearman correlation coefficient=0.93; p<0.0001).

Conclusion: The i-ROP DL system accurately identifies diagnostic categories and overall disease severity in an automated fashion, after being trained only on posterior pole vascular morphology. These data provide proof of concept that a deep learning screening platform could improve objectivity of ROP diagnosis and accessibility of screening.
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http://dx.doi.org/10.1136/bjophthalmol-2018-313156DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880608PMC
November 2018

Retinal Telemedicine.

Curr Ophthalmol Rep 2018 Mar 29;6(1):36-45. Epub 2018 Jan 29.

Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago.

Purpose Of Review: An update and overview of the literature on current telemedicine applications in retina.

Recent Findings: The application of telemedicine to the field of Ophthalmology and Retina has been growing with advancing technologies in ophthalmic imaging. Retinal telemedicine has been most commonly applied to diabetic retinopathy and retinopathy of prematurity in adult and pediatric patients respectively. Telemedicine has the potential to alleviate the growing demand for clinical evaluation of retinal diseases. Subsequently, automated image analysis and deep learning systems may facilitate efficient processing of large, increasing numbers of images generated in telemedicine systems. Telemedicine may additionally improve access to education and standardized training through tele-education systems.

Summary: Telemedicine has the potential to be utilized as a useful adjunct but not a complete replacement for physical clinical examinations. Retinal telemedicine programs should be carefully and appropriately integrated into current clinical systems.
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http://dx.doi.org/10.1007/s40135-018-0161-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6101043PMC
March 2018

Anti-Vascular Endothelial Growth Factor and the Evolving Management Paradigm for Retinopathy of Prematurity.

Asia Pac J Ophthalmol (Phila) 2018 May-Jun;7(3):136-144. Epub 2018 May 29.

Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois.

Diagnosis and management of pediatric retinal conditions such as retinopathy of prematurity (ROP) have been evolving significantly with the availability of new technology and treatments. New imaging systems, telemedicine, tele-education, and anti‒vascular endothelial growth factor (VEGF) intravitreal pharmacotherapy are all changing the way we diagnose and deliver care to children with pediatric retinal disease. Fluorescein angiography and optical coherence tomography have the potential to improve our diagnosis and management of disease, and with improvements in retinal imaging, telemedicine is becoming more feasible. Telemedicine, tele-education, and computer-based image analysis may overcome many of the challenges we face in providing adequate care and access for children with pediatric retinal disease. Treatment options have also expanded with the use of anti-VEGF therapy. Although the use of intravitreal anti-VEGF for ROP has been documented in the literature for more than a decade, many questions still remain about its safety in the pediatric patient population. Several ongoing prospective studies are exploring the utility of anti-VEGF agents for ROP, with attention to the optimal dose of drug, systemic safety, and our understanding of recurrence of disease. This review aims to provide an update on current diagnostic and therapeutic modalities, focusing predominantly on the role of anti-VEGF therapy, for the management of ROP and other pediatric retinal vascular diseases.
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http://dx.doi.org/10.22608/APO.201850DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880609PMC
June 2018

Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.

JAMA Ophthalmol 2018 07;136(7):803-810

Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland.

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 κ 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 κ 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.
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http://dx.doi.org/10.1001/jamaophthalmol.2018.1934DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6136045PMC
July 2018

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

Asia Pac J Ophthalmol (Phila) 2018 May-Jun;7(3):152-155. Epub 2018 May 24.

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.

Retinopathy of prematurity (ROP), a vasoproliferative retinal disease affecting premature infants, is a leading cause of childhood blindness throughout the world. Plus disease, defined as venous dilatation and arteriolar tortuosity within the posterior retinal vessels greater than or equal to that of a standard published photograph, is the most critical finding in identifying treatment-requiring ROP. Despite an internationally accepted definition of plus disease, there is significant variability in diagnostic process and outcome, producing variable levels of reported intra- and interexpert agreement. Several potential explanations for poor agreement have been proposed, including attention to undefined vascular features such as venous tortuosity, focus on narrower or wider field of view, unfamiliarity with digital images, the magnification and apparent severity of the standard photograph, and cut-off point differences among experts as to the level of tortuosity and dilation sufficient for "plus disease" along a continuum. Moreover, differences in diagnostic consistency among groups of experts separated both geographically and chronologically have been reported. These findings have implications for clinical care, research, and education, and highlight the need for a more precise definition of plus disease and objective diagnostic methods for ROP.
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http://dx.doi.org/10.22608/APO.201863DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880619PMC
June 2018

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

JAMA Ophthalmol 2018 06;136(6):648-655

Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland.

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.
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http://dx.doi.org/10.1001/jamaophthalmol.2018.1195DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6145779PMC
June 2018

Diagnostic Accuracy of Ophthalmoscopy vs Telemedicine in Examinations for Retinopathy of Prematurity.

JAMA Ophthalmol 2018 05;136(5):498-504

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland.

Importance: Examinations for retinopathy of prematurity (ROP) are typically performed using binocular indirect ophthalmoscopy. Telemedicine studies have traditionally assessed the accuracy of telemedicine compared with ophthalmoscopy as a criterion standard. However, it is not known whether ophthalmoscopy is truly more accurate than telemedicine.

Objective: To directly compare the accuracy and sensitivity of ophthalmoscopy vs telemedicine in diagnosing ROP using a consensus reference standard.

Design, Setting, And Participants: This multicenter prospective study conducted between July 1, 2011, and November 30, 2014, at 7 neonatal intensive care units and academic ophthalmology departments in the United States and Mexico included 281 premature infants who met the screening criteria for ROP.

Exposures: Each examination consisted of 1 eye undergoing binocular indirect ophthalmoscopy by an experienced clinician followed by remote image review of wide-angle fundus photographs by 3 independent telemedicine graders.

Main Outcomes And Measures: Results of both examination methods were combined into a consensus reference standard diagnosis. The agreement of both ophthalmoscopy and telemedicine was compared with this standard, using percentage agreement and weighted κ statistics.

Results: Among the 281 infants in the study (127 girls and 154 boys; mean [SD] gestational age, 27.1 [2.4] weeks), a total of 1553 eye examinations were classified using both ophthalmoscopy and telemedicine. Ophthalmoscopy and telemedicine each had similar sensitivity for zone I disease (78% [95% CI, 71%-84%] vs 78% [95% CI, 73%-83%]; P > .99 [n = 165]), plus disease (74% [95% CI, 61%-87%] vs 79% [95% CI, 72%-86%]; P = .41 [n = 50]), and type 2 ROP (stage 3, zone I, or plus disease: 86% [95% CI, 80%-92%] vs 79% [95% CI, 75%-83%]; P = .10 [n = 251]), but ophthalmoscopy was slightly more sensitive in identifying stage 3 disease (85% [95% CI, 79%-91%] vs 73% [95% CI, 67%-78%]; P = .004 [n = 136]).

Conclusions And Relevance: No difference was found in overall accuracy between ophthalmoscopy and telemedicine for the detection of clinically significant ROP, although, on average, ophthalmoscopy had slightly higher accuracy for the diagnosis of zone III and stage 3 ROP. With the caveat that there was variable accuracy between examiners using both modalities, these results support the use of telemedicine for the diagnosis of clinically significant ROP.
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http://dx.doi.org/10.1001/jamaophthalmol.2018.0649DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6036899PMC
May 2018

Telemedical Diagnosis of Stage 4 and Stage 5 Retinopathy of Prematurity.

Ophthalmol Retina 2018 01 9;2(1):59-64. Epub 2017 Jun 9.

Department of Ophthalmology, Weill Cornell Medical College, New York, New York; Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois; Center for Global Health, University of Illinois at Chicago College of Medicine, Chicago, Illinois. Electronic address:

Purpose: To determine the accuracy of image-based diagnosis for stage 4 or worse retinopathy of prematurity (ROP) disease.

Design: Prospective cohort study.

Participants: We prospectively obtained data, from 8 major ROP centers, for 1220 eye examinations from 230 infants.

Methods: An ophthalmologist at each center provided a clinical diagnosis using indirect ophthalmoscopy. Wide-angle retinal images (RetCam; Clarity Medical Systems, Pleasanton, CA) were then obtained, and these were independently read by 2 ROP experts using a web-based system for an image-based diagnosis.

Main Outcome Measures: Sensitivity and specificity of image-based diagnosis from the ROP experts were calculated using the clinical diagnosis as the reference standard.

Results: Of 1220 examinations, 28 (2%) had a clinical diagnosis of stage 4 or worse. Sensitivity and specificity for stage 4 or worse disease were 75% and 99% for expert 1, and 86% and 99% for expert 2. Sensitivity and specificity for the detection of stage 5 disease were 69% and 99% for both experts.

Conclusions: There are inconsistencies in the accuracy of image-based diagnosis of stage 4 and stage 5 ROP when compared with the clinical diagnosis.
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http://dx.doi.org/10.1016/j.oret.2017.04.001DOI Listing
January 2018

Changes in Relative Position of Choroidal Versus Retinal Vessels in Preterm Infants.

Invest Ophthalmol Vis Sci 2017 12;58(14):6334-6341

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States.

Purpose: The purpose of this study was to characterize a novel finding that relative positions of choroidal and retinal vessels change over time in preterm infants and to identify factors associated with this finding using quantitative analysis.

Methods: Fundus images were obtained prospectively through a retinopathy of prematurity (ROP) cohort study. Images were excluded if choroidal vessels could not be identified. Changes in relative position of characteristic choroidal landmarks with respect to retinal vessels between two time points 5 to 7 weeks apart were measured. Univariate and multivariate regression analyses were performed to identify associated factors with the amount of change.

Results: The discovery and replication cohorts included 45 and 58 patients, respectively. Ninety-two of them (89%) were non-Hispanic Caucasians. Changes in relative position of choroidal versus retinal vessels were detected in all eyes of the discovery and replication cohorts (mean amount = 0.42 ± 0.12 and 0.35 ± 0.12 mm, respectively). On combined multiple regression analysis of the two cohorts, type 1 ROP, higher postmenstral age at the first time point, and shorter distance from optic disc to choroidal landmark were significantly associated with less change in relative position.

Conclusions: Choroidal vessels grow anteriorly with respect to retinal vessels at posterior pole in preterm infants, suggesting relatively faster peripheral growth of choroidal versus retinal vessels. Eyes with severe ROP showed less difference in growth, which might represent alterations in choroidal development due to advanced ROP. These findings may contribute to better understanding about the physiology of choroidal development and involvement in ROP.
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http://dx.doi.org/10.1167/iovs.17-22687DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5742993PMC
December 2017

Assessment of a Tele-education System to Enhance Retinopathy of Prematurity Training by International Ophthalmologists-in-Training in Mexico.

Ophthalmology 2017 07 3;124(7):953-961. Epub 2017 Apr 3.

Department of Ophthalmology, Weill Cornell Medical College, New York, New York; Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois; Center for Global Health, College of Medicine, University of Illinois at Chicago, Chicago, Illinois. Electronic address:

Purpose: To evaluate a tele-education system developed to improve diagnostic competency in retinopathy of prematurity (ROP) by ophthalmologists-in-training in Mexico.

Design: Prospective, randomized cohort study.

Participants: Fifty-eight ophthalmology residents and fellows from a training program in Mexico consented to participate. Twenty-nine of 58 trainees (50%) were randomized to the educational intervention (pretest, ROP tutorial, ROP educational chapters, and posttest), and 29 of 58 trainees (50%) were randomized to a control group (pretest and posttest only).

Methods: A secure web-based educational system was created using clinical cases (20 pretest, 20 posttest, and 25 training chapter-based) developed from a repository of over 2500 unique image sets of ROP. For each image set used, a reference standard ROP diagnosis was established by combining the clinical diagnosis by indirect ophthalmoscope examination and image-based diagnosis by multiple experts. Trainees were presented with image-based clinical cases of ROP during a pretest, posttest, and training chapters.

Main Outcome Measures: The accuracy of ROP diagnosis (e.g., plus disease, zone, stage, category) was determined using sensitivity and specificity calculations from the pretest and posttest results of the educational intervention group versus control group. The unweighted kappa statistic was used to analyze the intragrader agreement for ROP diagnosis by the ophthalmologists-in-training during the pretest and posttest for both groups.

Results: Trainees completing the tele-education system had statistically significant improvements (P < 0.01) in the accuracy of ROP diagnosis for plus disease, zone, stage, category, and aggressive posterior ROP (AP-ROP). Compared with the control group, trainees who completed the ROP tele-education system performed better on the posttest for accurately diagnosing plus disease (67% vs. 48%; P = 0.04) and the presence of ROP (96% vs. 91%; P < 0.01). The specificity for diagnosing AP-ROP (94% vs. 78%; P < 0.01), type 2 ROP or worse (92% vs. 84%; P = 0.04), and ROP requiring treatment (89% vs. 79%; P < 0.01) was better for the trainees completing the tele-education system compared with the control group. Intragrader agreement improved for identification of plus disease, zone, stage, and category of ROP after completion of the educational intervention.

Conclusions: A tele-education system for ROP education was effective in improving the diagnostic accuracy of ROP by ophthalmologists-in-training in Mexico. This system has the potential to increase competency in ROP diagnosis and management for ophthalmologists-in-training from middle-income nations.
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http://dx.doi.org/10.1016/j.ophtha.2017.02.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895299PMC
July 2017

Toward a severity index for ROP: An unsupervised approach.

Annu Int Conf IEEE Eng Med Biol Soc 2016 Aug;2016:1312-1315

Retinopathy of prematurity (ROP) is a disease affecting low birth-weight infants and is the major cause of childhood blindness. Although accurate diagnosis is important, there is a high variability among expert decisions mostly due to subjective thresholds. Existing work focused on automated diagnosis of ROP. In this study, we construct a continuous severity index as an alternative to discrete classification. We follow an unsupervised approach by performing nonlinear dimensionality reduction. Instead of extracting several statistics of image features, each image is represented by the probability distribution of its features. The distance between distributions are then used in manifold learning methods as the distance between samples. The experiments are carried out on a dataset of 104 wide-angle retinal images. The results are promising and they reflect the challenges of the discrete classification.
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http://dx.doi.org/10.1109/EMBC.2016.7590948DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5482004PMC
August 2016
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