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1.
Surv Ophthalmol ; 2021 Mar 02.
Artigo em Inglês | MEDLINE | ID: mdl-33667496

RESUMO

Over the past decade there has been a paradigm shift in the treatment of retinopathy of prematurity (ROP) with the introduction of anti-vascular 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 concerns 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 the effects of 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.

2.
Sci Rep ; 11(1): 4966, 2021 Mar 02.
Artigo em Inglês | MEDLINE | ID: mdl-33654115

RESUMO

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.

3.
Ophthalmol Retina ; 2021 Feb 06.
Artigo em Inglês | MEDLINE | ID: mdl-33561545

RESUMO

PURPOSE: The presence of stage is an important feature to identify in retinal images of infants at risk for retinopathy of prematurity (ROP). The purpose of this study was to implement a convolutional neural network (CNN) for binary detection of stage 1-3 in ROP and evaluate its generalizability across different populations and camera systems. DESIGN: Diagnostic validation study of CNN for stage detection. SUBJECTS, PARTICIPANTS, AND/OR CONTROLS: Retinal fundus images obtained from preterm infants during routine ROP screenings. METHODS: Two datasets were used: 6247 fundus images taken by a RetCam camera from nine North American institutions, and 4647 images taken by a Forus 3nethra camera from four hospitals in Nepal. Images were labeled based on the presence of stage by 1-3 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 evaluated on two held-out test sets consisting of 708 and 247 images from the Nepali and North American datasets respectively. MAIN OUTCOME MEASURES: CNN performance was evaluated using area under the receiver operating curve (AUROC) and 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/AUPRC) 0.99/0.98 with sensitivity of 94%, and 0.97/0.91 with sensitivity of 73%, respectively. However, the performance of each model decreased to 0.96/0.88 (sensitivity 52%) and 0.62/0.36 (sensitivity 44%) when evaluated on a test set from the other population. Compared to 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 accurately identify the presence of ROP stage in retinal images, but performance depends on the similarity between training and testing populations. We demonstrate 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.

4.
Pediatrics ; 147(3)2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33637645

RESUMO

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]; P = .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.

6.
Ophthalmol Glaucoma ; 3(4): 253-261, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33008558

RESUMO

PURPOSE: To compare the average intraocular pressure (IOP) among smokers, past smokers, and never smokers using the American Academy of Ophthalmology Intelligent Research in Sight (IRIS®) Registry. DESIGN: Retrospective database study of the IRIS® Registry data. PARTICIPANTS: Intelligent Research in Sight Registry patients who were seen by an eye care provider during 2017. METHODS: Patients were divided into current smoker, past smoker, and never smoker categories. The IOP was based on an average measurement, and separate analyses were performed in patients with and without a glaucoma diagnosis based on International Classification of Diseases (Ninth Edition and Tenth Edition) codes. Stratified, descriptive statistics by glaucoma status were determined, and the relationship between smoking and IOP was assessed with a multivariate linear regression model. MAIN OUTCOME MEASURES: Mean IOP. RESULTS: A total of 12 535 013 patients were included. Compared with never smokers, current and past smokers showed a statistically significantly higher IOP by 0.92 mmHg (95% confidence interval [CI], 0.88-0.95 mmHg) and 0.77 mmHg (95% CI, 0.75-0.79 mmHg), respectively, after adjusting for age, gender, glaucoma, age-related macular degeneration, diabetic retinopathy, cataract, glaucoma surgery, cataract surgery, and first-order interactions. In addition, the difference in IOP between current and never smokers was the highest in the fourth decade, regardless of the glaucoma status (glaucoma group, 1.14 mmHg [95% CI, 1.00-1.29 mmHg]; without glaucoma group, 0.68 mmHg [95% CI, 0.65-0.71 mmHg]). CONCLUSIONS: Current smokers and past smokers have higher IOP than patients who never smoked. This difference is higher in patients with an underlying glaucoma diagnosis.

7.
Ophthalmology ; 2020 Oct 27.
Artigo em Inglês | MEDLINE | ID: mdl-33121959

RESUMO

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.

8.
J Pediatr Ophthalmol Strabismus ; 57(5): 333-339, 2020 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-32956484

RESUMO

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.].

11.
Br J Ophthalmol ; 2020 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-32816750

RESUMO

Training the modern ophthalmic surgeon is a challenging process. Microsurgical education can benefit from innovative methods to practice surgery in low-risk simulations, assess and refine skills in the operating room through video content analytics, and learn at a distance from experienced surgeons. Developments in emerging technologies may allow us to pursue novel forms of instruction and build on current educational models. Artificial intelligence, which has already seen numerous applications in ophthalmology, may be used to facilitate surgical tracking and evaluation. Within immersive technology, growth in the space of virtual reality head-mounted displays has created intriguing possibilities for operating room simulation and observation. Here, we explore the applications of these technologies and comment on their future in ophthalmic surgical education.

12.
Br J Ophthalmol ; 2020 Aug 23.
Artigo em Inglês | MEDLINE | ID: mdl-32830123

RESUMO

BACKGROUND/AIM: To automatically detect and classify the early stages of retinopathy of prematurity (ROP) using a deep convolutional neural network (CNN). METHODS: This retrospective cross-sectional study was conducted in a referral medical centre in Taiwan. Only premature infants with no ROP, stage 1 ROP or stage 2 ROP were enrolled. Overall, 11 372 retinal fundus images were compiled and split into 10 235 images (90%) for training, 1137 (10%) for validation and 244 for testing. A deep CNN was implemented to classify images according to the ROP stage. Data were collected from December 17, 2013 to May 24, 2019 and analysed from December 2018 to January 2020. The metrics of sensitivity, specificity and area under the receiver operating characteristic curve were adopted to evaluate the performance of the algorithm relative to the reference standard diagnosis. RESULTS: The model was trained using fivefold cross-validation, yielding an average accuracy of 99.93%±0.03 during training and 92.23%±1.39 during testing. The sensitivity and specificity scores of the model were 96.14%±0.87 and 95.95%±0.48, 91.82%±2.03 and 94.50%±0.71, and 89.81%±1.82 and 98.99%±0.40 when predicting no ROP versus ROP, stage 1 ROP versus no ROP and stage 2 ROP, and stage 2 ROP versus no ROP and stage 1 ROP, respectively. CONCLUSIONS: The proposed system can accurately differentiate among ROP early stages and has the potential to help ophthalmologists classify ROP at an early stage.

15.
Transl Vis Sci Technol ; 9(8): 43, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32855889

RESUMO

Purpose: To develop a population pharmacokinetic (PK) model for intravitreal ranibizumab in infants with retinopathy of prematurity (ROP) and assess plasma free vascular endothelial growth factor (VEGF) pharmacodynamics (PD). Methods: The RAnibizumab compared with laser therapy for the treatment of INfants BOrn prematurely With retinopathy of prematurity (RAINBOW) trial enrolled 225 infants to receive a bilateral intravitreal injection of ranibizumab 0.1 mg, ranibizumab 0.2 mg, or laser in a 1:1:1 ratio and included sparse sampling of blood for population PK and PD analysis. An adult PK model using infant body weight as a fixed allometric covariate was re-estimated using the ranibizumab concentrations in the preterm population. Different variability, assumptions, and covariate relationships were explored. Model-based individual predicted concentrations of ranibizumab were plotted against observed free VEGF concentrations. Results: Elimination of ranibizumab had a median half-life of 5.6 days from the eye and 0.3 days from serum, resulting in an apparent serum half-life of 5.6 days. Time to reach maximum concentration was rapid (median: 1.3 days). Maximum concentration (median 24.3 ng/mL with ranibizumab 0.2 mg) was higher than that reported in adults. No differences in plasma free VEGF concentrations were apparent between the groups or over time. Plotted individual predicted concentrations of ranibizumab against observed free VEGF concentrations showed no relationship. Conclusions: In preterm infants with ROP, elimination of ranibizumab from the eye was the rate-limiting step and was faster compared with adults. No reduction in plasma free VEGF was observed. The five-year clinical safety follow-up from RAINBOW is ongoing. Translational Relevance: Our population PK and VEGF PD findings suggest a favorable ocular efficacy: systemic safety profile for ranibizumab in preterm infants.

16.
Curr Opin Ophthalmol ; 31(5): 312-317, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32694266

RESUMO

PURPOSE OF REVIEW: In this article, we review the current state of artificial intelligence applications in retinopathy of prematurity (ROP) and provide insight on challenges as well as strategies for bringing these algorithms to the bedside. RECENT FINDINGS: In the past few years, there has been a dramatic shift from machine learning approaches based on feature extraction to 'deep' convolutional neural networks for artificial intelligence applications. Several artificial intelligence for ROP approaches have demonstrated adequate proof-of-concept performance in research studies. The next steps are to determine whether these algorithms are robust to variable clinical and technical parameters in practice. Integration of artificial intelligence into ROP screening and treatment is limited by generalizability of the algorithms to maintain performance on unseen data and integration of artificial intelligence technology into new or existing clinical workflows. SUMMARY: Real-world implementation of artificial intelligence for ROP diagnosis will require massive efforts targeted at developing standards for data acquisition, true external validation, and demonstration of feasibility. We must now focus on ethical, technical, clinical, regulatory, and financial considerations to bring this technology to the infant bedside to realize the promise offered by this technology to reduce preventable blindness from ROP.


Assuntos
Inteligência Artificial , Interpretação de Imagem Assistida por Computador/métodos , Retinopatia da Prematuridade/diagnóstico , Algoritmos , Humanos , Recém-Nascido , Aprendizado de Máquina , Redes Neurais de Computação
17.
Transl Vis Sci Technol ; 9(2): 5, 2020 Feb 10.
Artigo em Inglês | MEDLINE | ID: mdl-32704411

RESUMO

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The diagnosis of ROP is subclassified by zone, stage, and plus disease, with each area demonstrating significant intra- and interexpert subjectivity and disagreement. In addition to improved efficiencies for ROP screening, artificial intelligence may lead to automated, quantifiable, and objective diagnosis in ROP. This review focuses on the development of artificial intelligence for automated diagnosis of plus disease in ROP and highlights the clinical and technical challenges of both the development and implementation of artificial intelligence in the real world.

18.
Transl Vis Sci Technol ; 9(2): 10, 2020 Feb 14.
Artigo em Inglês | MEDLINE | ID: mdl-32704416

RESUMO

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.

19.
Transl Vis Sci Technol ; 9(2): 13, 2020 Feb 27.
Artigo em Inglês | MEDLINE | ID: mdl-32704419

RESUMO

Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive amounts of clinical data. In ophthalmology in particular, the volume range of data captured in EHR systems has been growing rapidly. Yet making effective secondary use of this EHR data for improving patient care and facilitating clinical decision-making has remained challenging due to the complexity and heterogeneity of these data. Artificial intelligence (AI) techniques present a promising way to analyze these multimodal data sets. While AI techniques have been extensively applied to imaging data, there are a limited number of studies employing AI techniques with clinical data from the EHR. The objective of this review is to provide an overview of different AI methods applied to EHR data in the field of ophthalmology. This literature review highlights that the secondary use of EHR data has focused on glaucoma, diabetic retinopathy, age-related macular degeneration, and cataracts with the use of AI techniques. These techniques have been used to improve ocular disease diagnosis, risk assessment, and progression prediction. Techniques such as supervised machine learning, deep learning, and natural language processing were most commonly used in the articles reviewed.

20.
Transl Vis Sci Technol ; 9(2): 14, 2020 Feb 27.
Artigo em Inglês | MEDLINE | ID: mdl-32704420

RESUMO

Purpose: To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning. Methods: A systematic literature search in PubMed was performed for articles pertinent to the topic of artificial intelligence methods used in medicine with an emphasis on ophthalmology. Results: A review of machine learning and deep learning methodology for the audience without an extensive technical computer programming background. Conclusions: Artificial intelligence has a promising future in medicine; however, many challenges remain. Translational Relevance: The aim of this review article is to provide the nontechnical readers a layman's explanation of the machine learning methods being used in medicine today. The goal is to provide the reader a better understanding of the potential and challenges of artificial intelligence within the field of medicine.

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