Telemedicine for retinopathy of prematurity diagnosis: evaluation and challenges

Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics

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.

Research hotspots and trends in retinopathy of prematurity from 2003 to 2022: a bibliometric analysis

Background

In order to understand the research hotspots and trends in the field of retinopathy of prematurity (ROP), our study analyzed the relevant publications from 2003 to 2022 by using bibliometric analysis.

Methods

The Citespace 6.2.R3 system was used to analyze the publications collected from the Web of Science Core Collection (WoSCC) database.

Results

In total, 4,957 publications were included in this study. From 2003 to 2022, the number of publications gradually increased and peaked in 2022. The United States was the country with the most publications, while Harvard University was the most productive institution. The top co-cited journal PEDIATRICS is published by the United States. Author analysis showed that Hellström A was the author with the most publications, while Good WV was the top co-cited author. The co-citation analysis of references showed seven major clusters: genetic polymorphism, neurodevelopmental outcome, threshold retinopathy, oxygen-induced retinopathy, low birth weight infant, prematurity diagnosis cluster and artificial intelligence (AI). For the citation burst analysis, there remained seven keywords in their burst phases until 2022, including ranibizumab, validation, trends, type 1 retinopathy, preterm, deep learning and artificial intelligence.

Conclusion

Intravitreal anti-vascular endothelial growth factor therapy and AI-assisted clinical decision-making were two major topics of ROP research, which may still be the research trends in the coming years.

Implementation of a pilot teleretinal screening protocol for hydroxychloroquine retinopathy in a Los Angeles County safety net clinic

INTRODUCTION

This study aimed to determine whether teleretinal screening for hydroxychloroquine retinopathy (HCQR) improves clinical efficiency and adherence to recommended screening guidelines compared to face-to-face screening among patients in a large safety net medical system.

METHODS

In this retrospective cohort study of a consecutive sample of 590 adult patients with active HCQ prescriptions seen in the outpatient ophthalmology clinic at Los Angeles County + University of Southern California Medical Center from 1 September 2018 to 25 November 2019, 203 patients underwent technician-only tele-HCQR screening (THRS), and 387 patients underwent screening with traditional face-to-face visits (F2FV) with an eye-care provider. Data on clinic efficiency measures (appointment wait time and encounter duration) and adherence to recommended screening guidelines were collected and compared between the two cohorts.

RESULTS

Compared to F2FV, the THRS cohort experienced significantly shorter median (interquartile range) time to appointment (2.5 (1.5-4.6) vs. 5.1 (2.9-8.4) months; p < 0.0001), shorter median encounter duration (1 (0.8-1.4) vs. 3.7 (2.5-5.2) hours; p < 0.0001) and higher proportion of complete baseline screening (102/104 (98.1%) vs. 68/141 (48.2%); p < 0.001) and complete chronic screening (98/99 (99%) vs. 144/246 (58.5%); p < 0.001).

DISCUSSION

A pilot THRS protocol was successfully implemented at a major safety net eye clinic in Los Angeles County, resulting in a 50.9% reduction in wait times for screening, 72.9% reduction in encounter duration and 49.9% and 40.5% increases in proportions of complete baseline and chronic screening, respectively. Tele-HCQ retinal screening protocols may improve timeliness to care and screening adherence for HCQR in the safety net setting.

Photographic Gel Artifact Simulating International Classification of Retinopathy of Prematurity Notch: Stanford University Network for Diagnosis of Retinopathy of Prematurity (SUNDROP) Report no. 16

PURPOSE

To report a series of fundus photographs taken for retinopathy of prematurity (ROP) screening that contain artifacts with imaging characteristics mimicking a notch, a recently refined classification metric in the International Classification of Retinopathy of Prematurity, third edition.

DESIGN

Retrospective case series.

PARTICIPANTS

Infants requiring ROP screening in neonatal intensive care units from the Stanford University Network for Diagnosis of Retinopathy of Prematurity (SUNDROP) and TeleROP telemedicine screening programs.

METHODS

Preterm infants meeting ROP examination criteria were screened with 130° wide-angle imaging systems. The images were taken by a trained nurse in the neonatal intensive care unit and transferred to an ROP specialist using a Health Insurance Portability and Accountability Act-compliant picture archiving and communication system for interpretation.

MAIN OUTCOME MEASURES

Presence of an artifact that appeared consistent with a notch.

RESULTS

We identified a total of 17 cases in ROP screening with artifact findings that had imaging characteristics similar to a notch. The artifactual appearance of the pseudo-notch was created by the camera illumination system within the gel-lens interface when the lens was not well apposed to the cornea. In telemedicine screening for ROP, we present fundus images of eyes with a pseudo-notch appearance; review of overlapping images can help differentiate between notch and artifact.

CONCLUSIONS

Pediatric retinal specialists need to be aware that artifacts play a confounding role in screening for ROP, that can be mitigated through the use of overlapping and redundant images.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Non-contact widefield neonatal retinal imaging for retinopathy of prematurity using the Clarus 700 high resolution true colour reflectance imaging

OBJECTIVE

To illustrate the role of non-contact widefield retinal imaging (NC-WFI) of retinopathy of prematurity (ROP) using the Clarus 700 high resolution true colour reflectance imaging.

METHODS

All babies were examined by the vitreoretinal faculty in a tertiary eye care centre from a period of March 2021 to November 2021 using the indirect ophthalmoscope after pupillary dilatation. ROP grading was done according to the revised ICROP (2005) classification. NC-WFI was then performed using the Clarus 700 high resolution true colour reflectance imaging (Carl Zeiss Meditec, Dublin, CA) in the retina diagnostic set up of a tertiary eye care centre.

RESULTS

A total of 22 babies (44 eyes) underwent NC-WFI from March 2021 to November 2021. 13 unique cases of retinopathy of prematurity with images captured on Clarus 700 and the clinical summary is described.

CONCLUSION

Clarus is a non-contact wide field imaging (NC-WFI) system that can capture high resolution and true colour images (images of the fundus appear similar colour to direct observation by ophthalmoscopy) helping in more accurate diagnosis and grading of the severity of ROP.

Artificial intelligence in diabetic retinopathy: Bibliometric analysis

BACKGROUND

The use of artificial intelligence in diabetic retinopathy has become a popular research focus in the past decade. However, no scientometric report has provided a systematic overview of this scientific area.

AIMS

We utilized a bibliometric approach to identify and analyse the academic literature on artificial intelligence in diabetic retinopathy and explore emerging research trends, key authors, co-authorship networks, institutions, countries, and journals. We further captured the diabetic retinopathy conditions and technology commonly used within this area.

METHODS

Web of Science was used to collect relevant articles on artificial intelligence use in diabetic retinopathy published between January 1, 2012, and December 31, 2022 . All the retrieved titles were screened for eligibility, with one criterion that they must be in English. All the bibliographic information was extracted and used to perform a descriptive analysis. Bibliometrix (R tool) and VOSviewer (Leiden University) were used to construct and visualize the annual numbers of publications, journals, authors, countries, institutions, collaboration networks, keywords, and references.

RESULTS

In total, 931 articles that met the criteria were collected. The number of annual publications showed an increasing trend over the last ten years. Investigative Ophthalmology & Visual Science (58/931), IEEE Access (54/931), and Computers in Biology and Medicine (23/931) were the most journals with most publications. China (211/931), India (143/931, USA (133/931), and South Korea (44/931) were the most productive countries of origin. The National University of Singapore (40/931), Singapore Eye Research Institute (35/931), and Johns Hopkins University (34/931) were the most productive institutions. Ting D. (34/931), Wong T. (28/931), and Tan G. (17/931) were the most productive researchers.

CONCLUSION

This study summarizes the recent advances in artificial intelligence technology on diabetic retinopathy research and sheds light on the emerging trends, sources, leading institutions, and hot topics through bibliometric analysis and network visualization. Although this field has already shown great potential in health care, our findings will provide valuable clues relevant to future research directions and clinical practice.

Use of Telemedicine in Pediatric Ophthalmology in the Underserved Population

Access to pediatric eye care is critical in diagnosing and treating eye disease promptly to prevent visual impairment. The demand for pediatric ophthalmology is high, even in developed countries, and significant socioeconomic disparities exist in access to care. The purpose of this article is to summarize the current literature on the use of telemedicine in pediatric ophthalmology in the underserved population and to identify areas of opportunity. A detailed literature review was performed in PubMed and Google Scholar on October 1, 2021. All articles in English that described the use of telemedicine in pediatric ophthalmology, with particular attention to the underserved pediatric population, were included. There is a paucity of literature on the visual outcomes from pediatric teleophthalmology alone, and even less in underserved populations specifically. Literature supports its use in subacute to chronic eye disease, return and postoperative visits, and screening for retinopathy in prematurity in particular. Collaboration between pediatric optometrists and pediatric ophthalmologists for both asynchronous and synchronous care delivery models has shown promise in several studies. It is essential to operate within the limits of pediatric teleophthalmology and utilize this valuable service for its strengths. Telemedicine may expand access to pediatric ophthalmologists in underserved populations and may reduce the burden of eye disease.

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

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

Effective field of view of wide-field fundus photography in the Stanford University Network for Diagnosis of Retinopathy of Prematurity (SUNDROP)

Five-field 130° wide-angle imaging is the standard of care for retinopathy of prematurity (ROP) screening with an ideal hypothetical composite field-of-view (FOV) of 180°. We hypothesized that in many real-world scenarios the effective composite FOV is considerably less than ideal. This observational retrospective study analyzed the effective FOV of fundus photos of patients screened for ROP as part of the Stanford University Network for Diagnosis of Retinopathy of Prematurity (SUNDROP) initiative. Five fundus photos were selected from each eye per image session. Effective FOV was defined as the largest circular area centered on the optic disc that encompassed retina in each of the four cardinal views. Seventy-three subjects were analyzed, 35 without ROP and 34 with ROP. Mean effective FOV was 144.55 ± 6.62° ranging from 130.00 to 153.71°. Effective FOV was not correlated with the presence or absence of ROP, gestational age, birth weight, or postmenstrual age. Mean effective FOV was wider in males compared to females. Standard five-field 130° fundus photos yielded an average effective FOV of 144.54° in the SUNDROP cohort. This implies that an imaging FOV during ROP screening considerably less than the hypothetical ideal of 180° is sufficient for detecting treatment warranted ROP.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

External Validation of a Retinopathy of Prematurity Screening Model Using Artificial Intelligence in 3 Low- and Middle-Income Populations

Importance

Retinopathy of prematurity (ROP) is a leading cause of preventable blindness that disproportionately affects children born in low- and middle-income countries (LMICs). In-person and telemedical screening examinations can reduce this risk but are challenging to implement in LMICs owing to the multitude of at-risk infants and lack of trained ophthalmologists.

Objective

To implement an ROP risk model using retinal images from a single baseline examination to identify infants who will develop treatment-requiring (TR)-ROP in LMIC telemedicine programs.

Design, Setting, and Participants

In this diagnostic study conducted from February 1, 2019, to June 30, 2021, retinal fundus images were collected from infants as part of an Indian ROP telemedicine screening program. An artificial intelligence (AI)-derived vascular severity score (VSS) was obtained from images from the first examination after 30 weeks' postmenstrual age. Using 5-fold cross-validation, logistic regression models were trained on 2 variables (gestational age and VSS) for prediction of TR-ROP. The model was externally validated on test data sets from India, Nepal, and Mongolia. Data were analyzed from October 20, 2021, to April 20, 2022.

Main Outcomes and Measures

Primary outcome measures included sensitivity, specificity, positive predictive value, and negative predictive value for predictions of future occurrences of TR-ROP; the number of weeks before clinical diagnosis when a prediction was made; and the potential reduction in number of examinations required.

Results

A total of 3760 infants (median [IQR] postmenstrual age, 37 [5] weeks; 1950 male infants [51.9%]) were included in the study. The diagnostic model had a sensitivity and specificity, respectively, for each of the data sets as follows: India, 100.0% (95% CI, 87.2%-100.0%) and 63.3% (95% CI, 59.7%-66.8%); Nepal, 100.0% (95% CI, 54.1%-100.0%) and 77.8% (95% CI, 72.9%-82.2%); and Mongolia, 100.0% (95% CI, 93.3%-100.0%) and 45.8% (95% CI, 39.7%-52.1%). With the AI model, infants with TR-ROP were identified a median (IQR) of 2.0 (0-11) weeks before TR-ROP diagnosis in India, 0.5 (0-2.0) weeks before TR-ROP diagnosis in Nepal, and 0 (0-5.0) weeks before TR-ROP diagnosis in Mongolia. If low-risk infants were never screened again, the population could be effectively screened with 45.0% (India, 664/1476), 38.4% (Nepal, 151/393), and 51.3% (Mongolia, 266/519) fewer examinations required.

Conclusions and Relevance

Results of this diagnostic study suggest that there were 2 advantages to implementation of this risk model: (1) the number of examinations for low-risk infants could be reduced without missing cases of TR-ROP, and (2) high-risk infants could be identified and closely monitored before development of TR-ROP.

A Systematic Review of Economic Evaluations Conducted for Interventions to Screen, Treat, and Manage Retinopathy of Prematurity (ROP) in the United States, United Kingdom, and Canada

PURPOSE

A systematic literature review (SLR) of economic evaluations (EE) conducted for interventions to screen, treat, and manage retinopathy of prematurity (ROP) in the United States (US), United Kingdom (UK), and Canada was performed.

METHODS

The SLR accessed the MEDLINE, Embase, Cochrane, Web of Science, Health Business Elite, Econ. Lit, NHS EED, and Google Scholar databases over the period 1st January 2000 to 4th August 2021. The key Medical Subject Heading (MeSH) search terms used included: Retinopathy of prematurity, Cost-effectiveness analysis, Cost-utility analysis, Cost of illness, Cost-benefit analysis, Cost minimization analysis, Incremental cost-effectiveness ratio, Quality adjusted life years, return on investment, burden of illness, disability adjusted life years, and Economic evaluation. Screening was conducted using Covidence, and the risk of bias was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. Data extraction was performed using MS Excel.

RESULTS

1,527 articles were examined with nine (9) papers identified, one (1) from the UK; two (2) from Canada and six (6) from the US. Cost-effectiveness analysis was the main form of EE conducted (n = 5) and telemedicine screening (n = 3) was found to be highly cost-effective for ROP with the ICER values ranging from £446 to £4,240 per Quality Adjusted Life Year (QALY) in 2021 figures. 73% of included studies complied with the CHEERS checklist for EE.

CONCLUSIONS

ROP screening and treatment strategies reviewed were highly cost-effective. This review may assist eye health policymakers in planning nationwide screening and treatment programs to combat vision loss and blindness due to ROP.

Comparing the diagnostic accuracy of telemedicine utilization versus in-person clinical examination for retinopathy of prematurity in premature infants: a systematic review

PURPOSE

To synthesize the literature assessing the diagnostic accuracy of telemedicine evaluation compared with clinical examination for retinopathy of prematurity (ROP) in premature infants.

METHODS

Covidence software was used to conduct a systematic literature search from September 14, 2020, through September 27, 2020, on MEDLINE (Ovid), EMBASE (Ovid), CINAHL, and the gray literature to identify studies relevant to telemedicine utilization for ROP detection. After duplicate removal and two-levels of screening, studies comparing telemedicine evaluation with binocular indirect ophthalmoscopic examination were included. Risk of bias assessment was conducted for the included studies following data extraction. A qualitative review was performed to summarize estimates of accuracy of ROP evaluation by telemedicine.

RESULTS

A total of 507 studies were reviewed, of which 323 were found in EMBASE, 115 in MEDLINE, and 79 in CINAHL. Three possibly relevant conference abstracts were found. Following duplicate removal, 410 studies were reviewed based on titles and abstracts. Subsequently, 19 articles were thoroughly examined, and 14 studies (2,655 participants) were included. Most studies found that telemedicine performance for detecting ROP was comparable to ophthalmic examination, especially with regard to identifying treatment-requiring ROP.

CONCLUSIONS

Telemedicine evaluation can reliably detect ROP. Incorporation of telemedicine into conventional neonatal care has the potential to improve access to ROP care.

Federated learning for multi-center collaboration in ophthalmology: implications for clinical diagnosis and disease epidemiology

OBJECTIVE OR PURPOSE

To utilize a deep learning (DL) model trained via federated learning (FL), a method of collaborative training without sharing patient data, to delineate institutional differences in clinician diagnostic paradigms and disease epidemiology in retinopathy of prematurity (ROP).

DESIGN

Evaluation of a diagnostic test or technology SUBJECTS, PARTICIPANTS, AND/OR CONTROLS: 5,245 patients with wide-angle retinal imaging from the neonatal intensive care units of 7 institutions as part of the Imaging and Informatics in ROP (i-ROP) study. Images were labeled with the clinical diagnosis of plus disease (plus, pre-plus, no plus) that was documented in the chart, and a reference standard diagnosis (RSD) determined by three image-based ROP graders and the clinical diagnosis.

METHODS, INTERVENTION OR TESTING

Demographics (birthweight [BW], gestational age [GA]), and clinical diagnoses for all eye exams were recorded from each institution. Using a FL approach, a DL model for plus disease classification was trained using only the clinical labels. The three class probabilities were then converted into a vascular severity score (VSS) for each eye exam, as well as an "institutional VSS" in which the average of the VSS values assigned to patients' higher severity ("worse") eyes at each exam was calculated for each institution.

MAIN OUTCOME MEASURES

We compared demographics, clinical diagnosis of plus disease, and institutional VSS between institutions using the McNemar Bowker test, two-proportion Z test and one-way ANOVA with post-hoc analysis by Tukey-Kramer test. Single regression analysis was performed to explore the relationship between demographics and VSS.

RESULTS

We found that the proportion of patients diagnosed with pre-plus disease varied significantly between institutions (p<0.00l). Using the DL-derived VSS trained on the data from all institutions using FL, we observed differences in the institutional VSS, as well as level of vascular severity diagnosed as no plus (p<0.001) across institutions. A significant, inverse relationship between the institutional VSS and the mean GA was found (p=0.049, adjusted R²=0.49).

CONCLUSIONS

A DL-derived ROP VSS developed without sharing data between institutions using FL identified differences in the clinical diagnosis of plus disease, and overall levels of ROP severity between institutions. FL may represent a method to standardize clinical diagnosis and provide objective measurement of disease for image-based diseases.

Utilizing near infra-red spectroscopy to identify physiologic variations during digital retinal imaging in preterm infants

OBJECTIVE

Evaluate physiologic changes during digital retinal imaging (DRI) using near infra-red spectroscopy (NIRS).

STUDY DESIGN

Prospective observational study of preterm infants undergoing retinopathy of prematurity screening via DRI using wide-field retinal camera. Cardiorespiratory (CR) and NIRS data were collected, trends correlated for changes and coefficient representing "slopes" of outcomes were plotted over time. The p value associated with each slope coefficient was tested to assess for slope differences from time of intervention (time = 0/or no slope).

RESULTS

Thirty-one preterm infants were included in the study. There were no significant changes in pre- and post-slopes for cerebral or mesenteric oxygenation, or CR indices with eye drop administration compared to baseline. DRI resulted in significant increase in post exam slope in cerebral oxygenation, mesenteric oxygenation and respiratory rate.

CONCLUSION

ROP examination using DRI was well tolerated with slight improvements in cerebral and mesenteric perfusion without significant safety concerns.

Extending Hui-Walter framework to correlated outcomes with application to diagnosis tests of an eye disease among premature infants

Diagnostic accuracy, a measure of diagnostic tests for correctly identifying patients with or without a target disease, plays an important role in evidence-based medicine. Diagnostic accuracy of a new test ideally should be evaluated by comparing to a gold standard; however, in many medical applications it may be invasive, costly, or even unethical to obtain a gold standard for particular diseases. When the accuracy of a new candidate test under evaluation is assessed by comparison to an imperfect reference test, bias is expected to occur and result in either overestimates or underestimates of its true accuracy. In addition, diagnostic test studies often involve repeated measurements of the same patient, such as the paired eyes or multiple teeth, and generally lead to correlated and clustered data. Using the conventional statistical methods to estimate diagnostic accuracy can be biased by ignoring the within-cluster correlations. Despite numerous statistical approaches have been proposed to tackle this problem, the methodology to deal with correlated and clustered data in the absence of a gold standard is limited. In this article, we propose a method based on the composite likelihood function to derive simple and intuitive closed-form solutions for estimates of diagnostic accuracy, in terms of sensitivity and specificity. Through simulation studies, we illustrate the relative advantages of the proposed method over the existing methods that simply treat an imperfect reference test as a gold standard in correlated and clustered data. Compared with the existing methods, the proposed method can reduce not only substantial bias, but also the computational burden. Moreover, to demonstrate the utility of this approach, we apply the proposed method to the study of National-Eye-Institute-funded Telemedicine Approaches to Evaluating of Acute-Phase Retinopathy of Prematurity (e-ROP), for estimating accuracies of both the ophthalmologist examination and the image evaluation.

Assessment and management of retinopathy of prematurity in the era of anti-vascular endothelial growth factor (VEGF)

The incidence of retinopathy of prematurity (ROP) continues to rise due to the improved survival of very low birth weight infants in developed countries. This epidemic is also fueled by increased survival of preterm babies with variable use of oxygen and a lack of ROP awareness and screening services in resource-limited regions. Improvements in technology and a basic understanding of the disease pathophysiology have changed the way we screen and manage ROP, educate providers and patients, and improve ROP awareness. Advancements in imaging techniques, expansion of telemedicine services, and the potential for artificial intelligence-assisted ROP screening programs have created opportunities to improve ROP care in areas with a shortage of ophthalmologists trained in ROP. To address the gap in provider knowledge regarding ROP, the Global Education Network for Retinopathy of Prematurity (GEN-ROP) created a web-based tele-education training module that can be used to educate all providers involved in ROP, including non-physician ROP screeners. Over the past 50 years, the treatment of severe ROP has evolved from limited treatment modalities to cryotherapy and laser photocoagulation. More recently, there has been growing evidence to support the use of anti-vascular endothelial growth factor (VEGF) agents for the treatment of severe ROP. However, VEGF is known to be important in organogenesis and microvascular maintenance, and given that intravitreal anti-VEGF treatment can result in systemic VEGF suppression over a period of at least 1-12 weeks, there are concerns regarding adverse effects and long-term ocular and systemic developmental consequences of anti-VEGF therapy. Future research in ophthalmology to address the growing burden of ROP should focus on cost-effective fundus imaging devices, implementation of artificial intelligence platforms, updated treatment algorithms with optimal use of anti-VEGF and careful investigation of its long-term effects, and surgical options in advanced ROP. Addressing these unmet needs will aid the global effort against the ROP epidemic and optimize our understanding and treatment of this blinding disease.

Flying baby optical coherence tomography alters the staging and management of advanced retinopathy of prematurity

PURPOSE

To report the use of flying baby spectral domain optical coherence tomography (SD-OCT) on infants with advanced retinopathy of prematurity (ROP), where clinical findings alone failed to differentiate between retinoschisis and retinal detachment.

METHODS

Prospective, non-interventional case-series study of three premature infants with advanced ROP of clinically uncertain stage, after examination by indirect ophthalmoscopy. To confirm the diagnosis, table-mounted SD-OCT retinal imaging was performed with the infant held in the flying baby position under topical ocular anaesthesia only. Spectral domain optical coherence tomography (SD-OCT) findings were correlated with clinical examination and ultra-widefield scanning laser ophthalmoscopy to determine disease stage and appropriate management.

RESULTS

The flying baby position was well tolerated, and SD-OCT images of central and peripheral retina were successfully obtained in all three cases. Additional information provided by the SD-OCT changed the ROP staging from 3 to 4 in one case, which subsequently required surgical treatment. In two other cases, clinical suspicion of stage 4 ROP was overruled as SD-OCT revealed tractional retinoschisis rather than full-thickness retinal detachment, thereby avoiding the need for immediate surgical intervention.

CONCLUSIONS

In this case-series study, flying baby SD-OCT provided a rapid and widely accessible imaging approach that overruled clinical findings and altered classification and management of infants with advanced ROP. The methodology was suitable for outpatient settings with no risks associated with systemic anaesthesia. The increased use of OCT imaging will make apparent how structural information is useful in management of ROP and may influence future classification of the disease.

Ultra-wide field retinal imaging: A wider clinical perspective

The peripheral retina is affected in a variety of retinal disorders. Traditional fundus cameras capture only a part of the fundus even when montaging techniques are used. Ultra-wide field imaging enables us to delve into the retinal periphery in greater detail. It not only facilitates assessing color images of the fundus, but also fluorescein angiography, indocyanine green angiography, fundus autofluorescence, and red and green free images. In this review, a literature search using the keywords "ultra-widefield imaging", "widefield imaging", and "peripheral retinal imaging" in English and non-English languages was done and the relevant articles were included. Ultra-wide field imaging has made new observations in the normal population as well as in eyes with retinal disorders including vascular diseases, degenerative diseases, uveitis, age-related macular degeneration, retinal and choroidal tumors and hereditary retinal dystrophies. This review aims to describe the utility of ultra-wide field imaging in various retinal disorders.

Key factors in a rigorous longitudinal image-based assessment of retinopathy of prematurity

To describe a database of longitudinally graded telemedicine retinal images to be used as a comparator for future studies assessing grader recall bias and ability to detect typical progression (e.g. International Classification of Retinopathy of Prematurity (ICROP) stages) as well as incremental changes in retinopathy of prematurity (ROP). Cohort comprised of retinal images from 84 eyes of 42 patients who were sequentially screened for ROP over 6 consecutive weeks in a telemedicine program and then followed to vascular maturation or treatment, and then disease stabilization. De-identified retinal images across the 6 weekly exams (2520 total images) were graded by an ROP expert based on whether ROP had improved, worsened, or stayed the same compared to the prior week’s images, corresponding to an overall clinical “gestalt” score. Subsequently, we examined which parameters might have influenced the examiner’s ability to detect longitudinal change; images were graded by the same ROP expert by image view (central, inferior, nasal, superior, temporal) and by retinal components (vascular tortuosity, vascular dilation, stage, hemorrhage, vessel growth), again determining if each particular retinal component or ROP in each image view had improved, worsened, or stayed the same compared to the prior week’s images. Agreement between gestalt scores and view, component, and component by view scores was assessed using percent agreement, absolute agreement, and Cohen’s weighted kappa statistic to determine if any of the hypothesized image features correlated with the ability to predict ROP disease trajectory in patients. The central view showed substantial agreement with gestalt scores (κ = 0.63), with moderate agreement in the remaining views. Of retinal components, vascular tortuosity showed the most overall agreement with gestalt (κ = 0.42–0.61), with only slight to fair agreement for all other components. This is a well-defined ROP database graded by one expert in a real-world setting in a masked fashion that correlated with the actual (remote in time) exams and known outcomes. This provides a foundation for subsequent study of telemedicine’s ability to longitudinally assess ROP disease trajectory, as well as for potential artificial intelligence approaches to retinal image grading, in order to expand patient access to timely, accurate ROP screening.

Current Application of Digital Diagnosing Systems for Retinopathy of Prematurity

BACKGROUND AND OBJECTIVE

Retinopathy of prematurity (ROP), a proliferative vascular eye disease, is one of the leading causes of blindness in childhood and prevails in premature infants with low-birth-weight. The recent progress in digital image analysis offers novel strategies for ROP diagnosis. This paper provides a comprehensive review on the development of digital diagnosing systems for ROP to software researchers. It may also be adopted as a guide to ophthalmologists for selecting the most suitable diagnostic software in the clinical setting, particularly for the remote ophthalmic support.

METHODS

We review the latest literatures concerning the application of digital diagnosing systems for ROP. The diagnosing systems are analyzed and categorized. Articles published between 1998 and 2020 were screened with the two searching engines Pubmed and Google Scholar.

RESULTS

Telemedicine is a method of remote image interpretation that can provide medical service to remote regions, and yet requires training to local operators. On the basis of image collection in telemedicine, computer-based image analytical systems for ROP were later developed. So far, the aforementioned systems have been mainly developed by virtue of classic machine learning, deep learning (DL) and multiple machine learning. During the past two decades, various computer-aided systems for ROP based on classic machine learning (e.g. RISA, ROPtool, CAIER) became available and have achieved satisfactory performance. Further, automated systems for ROP diagnosis based on DL are developed for clinical applications and exhibit high accuracy. Moreover, multiple instance learning is another method to establish an automated system for ROP detection besides DL, which, however, warrants further investigation in future.

CONCLUSION

At present, the incorporation of computer-based image analysis with telemedicine potentially enables the detection, supervision and in-time treatment of ROP for the preterm babies.

ULTRA-WIDE-FIELD IMAGING AND INTRAVENOUS FUNDUS FLUORESCEIN ANGIOGRAPHY IN INFANTS WITH RETINOPATHY OF PREMATURITY

PURPOSE

To determine the feasibility of ultra-wide-field imaging and ultra-wide-field intravenous fundus fluorescein angiography (UWF-IV-FFA) in infants with retinopathy of prematurity (ROP) using Optos 200Tx.

METHODS

We performed Optos 200Tx capturing on 32 premature infants (14 females) and UWF-IV-FFA with Optos 200Tx on 12 of the 32 infants between April 2017 and July 2018 at the affiliated eye hospital of Wenzhou Medical University and analyzed their fundus images.

RESULTS

Ultra-wide-field color images were acquired from 32 infants (64 eyes). UWF-IV-FFA was performed successfully in 12 premature infants (24 eyes). No adverse events were observed. The ultra-wide-field Optos 200Tx color images and UWF-IV-FFA images revealed Stages 1, 2, and 3 ROP and aggressive posterior ROP.

CONCLUSION

Ultra-wide-field imaging and intravenous fundus fluorescein angiography using Optos 200Tx are feasible in infants with ROP, which have the potential to screen, diagnose, and follow-up for ROP.

Open-care telemedicine in ophthalmology during the COVID-19 pandemic: a pilot study

Objetivo

Reportar una experiencia piloto de atención por telemedicina en la especialidad de oftalmología, en el periodo de confinamiento por la pandemia por COVID-19.

Métodos

Estudio descriptivo. Se describen características demográficas y clínicas de pacientes atendidos en periodo de confinamiento de 10 semanas. Se evalúa la satisfacción de los pacientes y médicos participantes mediante una encuesta en línea.

Resultados

En las primeras 10 semanas, se realizaron 291 atenciones de telemedicina oftalmológica. Los principales motivos de consulta fueron afecciones inflamatorias de la superficie ocular y párpados (79,4%), seguido de requerimientos administrativos (6,5%), afecciones no inflamatorias de la superficie ocular (5,2%), sospecha de estrabismo (3,4%) y síntomas vitreorretinales (3,1%); 22 pacientes (7,5%) fueron derivados a atención presencial inmediata. El nivel de satisfacción con la prestación fue alto, tanto en médicos (100%), como en pacientes (93,4%).

Conclusiones

La atención oftalmológica por telemedicina en periodo de pandemia es un instrumento de utilidad para realizar un filtro de potenciales consultas presenciales, ya sea electivas o de urgencia, y para reducir potencialmente el riesgo de contagio por COVID-19.

Auto-Processed Retinal Vessel Shadow View Images From Bedside Optical Coherence Tomography to Evaluate Plus Disease in Retinopathy of Prematurity

Purpose

To describe the creation of en face retinal vessel shadow view (RVSV) optical coherence tomography (OCT) images and assess the feasibility of using these for evaluating vascular disease in preterm infants at risk for retinopathy of prematurity (ROP).

Methods

In this exploratory study, we selected images from eyes with a range of ROP vascular disease, prospectively acquired from preterm infants using an investigational, noncontact, handheld, bedside swept-source OCT. We autosegmented OCT volumes using custom infant-specific software, extracted RVSV-OCT images from volumetric data bracketed around the retinal pigment epithelium, and automontaged the resulting RVSV-OCT images. Three masked ophthalmologists graded the RVSV-OCT montages as plus, pre-plus, or neither and ranked them by relative vascular disease severity.

Results

We selected images from 17 imaging sessions (7 plus, 4 pre-plus, 6 neither on clinical examination). On review, 15/17 (88%) RVSV-OCT montages were gradable for plus, pre-plus, or neither and all 17 montages were rankable for relative severity. Intergrader agreement for plus, pre-plus, or neither grading was good (κ, 0.67; 95% confidence interval, 0.42–0.86) and for relative severity ranking was excellent (intraclass correlation coefficient, 0.98; 95% confidence interval, 0.96–0.99).

Conclusions

Our novel automatic processing method can create RVSV-OCT montages optimized for retinal vessel visualization for ROP screening. Although our data support the feasibility of using RVSV-OCT montages for ranking relative vascular disease severity, there is room for improved OCT image capture and processing methods in preterm infants screened for ROP.

Translational Relevance

Creation and grading of RVSV-OCT images could eventually be integrated into an alternative method for ROP screening.

Proposed measures to be taken by ophthalmologists during the coronavirus disease 2019 pandemic: Experience from Chang Gung Memorial Hospital, Linkou, Taiwan

PURPOSE

Although Taiwan was one of the first countries to develop coronavirus disease 2019 (COVID-19), with effective antiepidemic measures, Taiwan has effectively controlled the spread of the disease. The purpose of this article is to provide useful safety strategies for ophthalmologists in daily practice during the COVID-19 pandemic.

MATERIALS AND METHODS

Infection control strategies in the hospital and Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou, are discussed.

RESULTS

Ophthalmologists are at high risk of contracting COVID-19 infection, as they have close contact with patients during ocular examinations, and are also facing high patient volume in outpatient clinics as well as emergency consultations. Furthermore, ocular symptoms, such as conjunctivitis, may be the presenting signs of COVID-19 infection. We provide our strategies, which include hospital's gate control with triage station, patient volume control, proper personal protective equipment, and consultation with telemedicine technology, to decrease the risk of cross-infection between medical staffs and patients.

CONCLUSION

To achieve the goal of preventing viral spread and maximizing patient and medical staffs' safety, besides providing proper protective equipment, it is also crucial for staffs and patients to strictly follow antiepidemic measures. We hope that our experience can help ophthalmologists and health-care workers to have a safer working environment when facing COVID-19 pandemic.

Non-contact smartphone-based fundus imaging compared to conventional fundus imaging: a low-cost alternative for retinopathy of prematurity screening and documentation

Retinopathy of prematurity (ROP) is a frequent cause of treatable childhood blindness. The current dependency of telemedicine-based ROP screening on cost-intensive equipment does not meet the needs in economically disadvantaged regions. Smartphone-based fundus imaging (SBFI) allows for affordable and mobile fundus examination and, therefore, could facilitate cost-effective telemedicine-based ROP screening in low-resources settings. We compared non-contact SBFI and conventional contact fundus imaging (CFI) in terms of feasibility for ROP screening and documentation. Twenty-six eyes were imaged with both SBFI and CFI. Field-of-view was smaller (ratio of diameters, 1:2.5), level of detail was equal, and examination time was longer for SBFI as compared to CFI (109.0 ± 57.8 vs. 75.9 ± 36.3 seconds, p < 0.01). Good agreement with clinical evaluation by indirect funduscopy was achieved for assessment of plus disease and ROP stage for both SBFI (squared Cohen's kappa, 0.88 and 0.81, respectively) and CFI (0.86 and 0.93). Likewise, sensitivity/specificity for detection of plus disease and ROP was high for both SBFI (90%/100% and 88%/93%, respectively) and CFI (80%/100% and 100%/96%). SBFI is a non-contact and low-cost alternative to CFI for ROP screening and documentation that has the potential to considerably improve ROP care in middle- and low-resources settings.

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

Purpose

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

Methods

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

Results

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

Conclusions

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

Translational Relevance

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

Exudative Retinal Detachment Following Laser Photocoagulation for Retinopathy of Prematurity: A Rare Complication

Laser photocoagulation remains the standard of care for retinopathy of prematurity (ROP). Rarely, exudative retinal detachment (ERD) has been observed as a complication of laser treatment. The authors present the clinical course of an infant who developed severe, unilateral ERD after bilateral laser photocoagulation at 37 weeks postmenstrual age (PMA) for Type I ROP. The infant was managed with systemic and topical corticosteroids, and nearcomplete resolution of ERD was observed at 39 weeks. Continued follow-up until 62 weeks PMA with serial examination, fundus photography, and fluorescein angiography documented the time course of resolution and retinal sequelae of this rare complication. [Ophthalmic Surg Lasers Imaging Retina. 2019;50:242-246.].

Historical Perspectives: Telemedicine in Neonatology

Telemedicine is fast becoming integrated into health care as a way to increase access for patients, particularly across the urban/rural divide. Use of telemedicine in neonatology is a newer, yet rapidly expanding modality. This review outlines the history of telemedicine, the evolution of its current uses in neonatology, requirements for starting a telemedicine program, and potential future uses.

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

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.

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

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.

Effectiveness and efficiency of tele-expertise for improving access to retinopathy screening among 351 neonates in a secondary care center: An observational, controlled before-after study

In France, secondary care hospitals encounter difficulties to adhere to retinopathy of prematurity (ROP) screening guidelines. Our objective was to assess the effectiveness and efficacy of a tele-expertise program for ROP screening in neonatal intensive care units without on-site ophthalmologists. We evaluated the impact of a tele-expertise program funded by the Paris Region Health Authority in a secondary care center general hospital of the Paris Region (CHSF), where there was previously no on-site ophthalmologist. We performed an observational, controlled before-after study, with a university tertiary care center with on-site ophthalmologists (Port-Royal) as the control group. Recruitment and data collection for both periods took place from 1 January 2012 to 31 December 31 2012, and from 1 January 2014 to 31 March 2015. The primary endpoint was the percentage of compliance with screening guidelines, secondary endpoints included pain scores and costs. Over the two periods, at total of 351 infants were recruited in the CHSF. Implementation of the tele-expertise resulted in an absolute +57.3% increase in the proportion of examinations realized in accordance with guidelines (3.8% during the "before" period and 61.1% during the "after" period, p<0.001). As compared with the control group, the proportion of infants appropriately screened improved (57.5% versus 43.1%, p = 0.002); median pain score on the acute pain rating scale for neonates during examination was significantly higher (median score 5.5/10, range [2.5-5.7] versus 2.0/10, range [1.0-3.1], p = 0.002). Screening rates in the control group remained unchanged. The average cost per examination increased from €337 in the "before" period to €353 in the "after period" in the tele-expertise group. The implementation of tele-expertise for ROP screening in the CHSF medical center resulted in a major improvement of access to care with a small cost increase. The issue of pain control during examination with tele-expertise should be further addressed.

Diagnostic accuracy of a digital fundus photographic system for detection of retinopathy of prematurity requiring treatment (ROP-RT)

Objectives

To evaluate the diagnostic accuracy of a digital fundus photographic system that consists of taking fundus photographs by a trained technician using a RetCam^® shuttle and interpreting fundus images by an expert to detect Retinotapthy of Prematurity requiring treatment (ROP-RT) which defined as type I ROP according to the Early Treatment for ROP study (ETROP).

Materials and methods

One hundred infants were examined by (1) an expert ophthalmologist experienced in ROP care using indirect ophthalmoscopy; (2) digital wide-field imaging by a trained technician using a RetCam^® shuttle and images were sent remotely for interpretation by two ophthalmologists experienced in ROP care (Reader A, and Reader B); and (3) local ophthalmologists using indirect ophthalmoscopy. The diagnostic acurracy consisting of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), and negative likelihood ratio (LR-) were calculated. Agreement between all examiners and readers were evaluated.

Results

A total of 100 infants (mean gestational age 31.1 weeks, mean birth weight 1,511.1 grams) participated in the study. Nine infants were classified as ROP-RT. Reader A and B had very good agreement in detection of ROP- RT (Kappa 1.00, 95% CI 1.00, 1.00). For reader A, diagnostic performance parameters (95% confidence intervals) for detecting ROP-RT were; sensitivity 100.0% (66.4, 100.0), specificity 97.8% (92.1, 99.7), PPV 81.8% (48.2, 97.7), NPV 100.0% (95.8, 100.0), LR+ 44.5 (11.3, 175.2), and LR- 0.1 (0.0, 0.8). For reader B these were; sensitivity 100.0% (66.4, 100.0), specificity 95.6% (89.0, 98.8), PPV 69.2% (38.6, 90.9), NPV 100.0% (95.8, 100.0), LR+ 22.5 (8.6, 58.6), LR- 0.1 (0.0, 0.8). No adverse events were reported.

Conclusions

Diagnosis of ROP-RT from RetCam^® images taken by trained technicians and evaluated remotely by an expert ophthalmologist had good diagnostic accuracy for screening purposes.

Smartphones, tele-ophthalmology, and VISION 2020

Telemedicine is an emerging field in recent medical achievements with rapid development. The "smartphone" availability has increased in both developed and developing countries even among people in rural and remotes areas. Tele-based services can be used for screening ophthalmic diseases and also monitoring patients with known diseases. Electronic ophthalmologic records of the patients including captured images by smartphones from anterior and posterior segments of the eye will be evaluated by ophthalmologists, and if patients require further evaluations, they will be referred to experts in the relevant field. Eye diseases such as cataract, glaucoma, age-related macular degeneration, diabetic retinopathy, and retinopathy of prematurity are the most common causes of blindness in many countries and beneficial use of teleophthalmology with smartphones will be a good way to achieve the aim of VISION 2020 all over the world. Numerous studies have shown that teleophthalmology is similar to the conventional eye care system in clinical outcomes and even provides more patient satisfaction as it saves time and cost. This review explains how teleophthalmology helps to improve patient outcomes through smartphones.

Telemedical Diagnosis of Stage 4 and Stage 5 Retinopathy of Prematurity

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.

Telemedicine Applications in Pediatric Retinal Disease

Teleophthalmology is a developing field that presents diverse opportunities. One of its most successful applications to date has been in pediatric retinal disease, particularly in screening for retinopathy of prematurity (ROP). Many studies have shown that using telemedicine for ROP screening allows a remote ophthalmologist to identify abnormal findings and implement early interventions. Here, we review the literature on uses of telemedicine in pediatric retinal disease and consider future applications.

Mobile unit for retinopathy of prematurity screening and management at urban Neonatal Intensive Care Units: Outcomes and impact assessment

Purpose:

To study the outcomes and impact of a mobile unit for retinopathy of prematurity (ROP) screening and management at urban Neonatal Intensive Care Units (NICUs).

Study Design:

Public health intervention study.

Methods:

This study was conducted in 2012. Staff of a mobile unit assessed all infants aged 32 weeks or less and/or weight 1250 g or less admitted in five NICUs between 2009 and 2011. An ophthalmologist performed bedside ROP screening through dilated pupils using indirect ophthalmoscopy. ROP was graded and managed as per the International Classification of ROP treatment guidelines. Counseling and laser treatment were the interventions. The incidence, grade, and determinants of ROP were estimated. Direct and indirect costs were calculated to estimate the unit cost of screening and managing a child with ROP using the mobile unit.

Result:

The study sample included 104 preterm/underweight infants. The prevalence of ROP of different grades in either eye was 32.7% (95% confidence intervals: 23.7–41.7). ROP Stage I was present in 75% of these eyes. The mobile unit could help in preventing/reducing visual disability in 5 infants with advanced stages of ROP. The unit cost of ROP screening, identifying one child with ROP, and addressing visual disability due to ROP was US $310, 950, and 6500, respectively.

Conclusion:

A mobile screening is likely feasible and cost-effective method to detect ROP and offer timely intervention in urban areas with limited resources.

Bedside ROP screening and telemedicine interpretation integrated to a neonatal transport system: Economic aspects and return on investment analysis

BACKGROUND AND AIM

Peter Cerny Ambulance Service - Premature Eye Rescue Program (PCA-PERP) uses digital retinal imaging (DRI) with remote interpretation in bedside ROP screening, which has advantages over binocular indirect ophthalmoscopy (BIO) in screening of premature newborns. We aimed to demonstrate that PCA-PERP provides good value for the money and to model the cost ramifications of a similar newly launched system.

METHODS

As DRI was demonstrated to have high diagnostic performance, only the costs of bedside DRI-based screening were compared to those of traditional transport and BIO-based screening (cost-minimization analysis). The total costs of investment and maintenance were analyzed with micro-costing method. A ten-year analysis time-horizon and service provider's perspective were applied.

RESULTS

From the launch of PCA-PERP up to the end of 2014, 3722 bedside examinations were performed in the PCA covered central region of Hungary. From 2009 to 2014, PCA-PERP saved 92,248km and 3633 staff working hours, with an annual nominal cost-savings ranging from 17,435 to 35,140 Euro. The net present value was 127,847 Euro at the end of 2014, with a payback period of 4.1years and an internal rate of return of 20.8%. Our model presented the NPVs of different scenarios with different initial investments, annual number of transports and average transport distances.

CONCLUSIONS

PCA-PERP as bedside screening with remote interpretation, when compared to a transport-based screening with BIO, produced better cost-savings from the perspective of the service provider and provided a return on initial investment within five years after the project initiation.

Mobile health application for remote oral cancer surveillance

BACKGROUND

To determine the effectiveness of a mobile phone-based remote oral cancer surveillance program (Oncogrid) connecting primary care dental practitioners and frontline health care workers (FHW) with oral cancer specialists.

METHODS

The study population (N = 3,440) included a targeted cohort (n = 2,000) and an opportunistic cohort (n = 1,440) screened by FHW and dental professionals, respectively. The authors compared the screening efficacy in both groups, with specialist diagnosis considered the reference standard. The outcomes measured were lesion detection and capture of interpretable images of the oral cavity.

RESULTS

In the targeted cohort, among 51 of 81 (61%) interpretable images, 23 of 51 (45%) of the lesions were confirmed by specialists, while the opportunistic cohort showed 100% concordance with the specialists (106 of 106). Sixty-two of 129 (48%) of the recommended patients underwent biopsy; 1 of 23 (4%) were in the targeted cohort, and 61 of 106 (57%) were in the opportunistic cohort. Ninety percent of the lesions were confirmed to be malignant or potentially malignant.

CONCLUSIONS

The mobile health-based approach adopted in this study aided remote early detection of oral cancer by primary care dental practitioners in a resource-constrained setting. Further optimization of this program is required to adopt the system for FHW. Evaluation of its efficacy in a larger population is also warranted.

PRACTICAL IMPLICATIONS

The increased efficiency of early detection by dentists, when assisted by a remote mobile health-based approach, is a step toward a more effective oral cancer screening program.

A store-and-forward telemedicine for retinopathy of prematurity screen: is it cost-effective in Thailand?

Background

Prompt diagnosis and treatment of retinopathy of prematurity (ROP) is crucial to prevent blindness. Telemedicine for ROP diagnosis can be applied in regions that lack an expert ophthalmologist.

Objectives

To assess the value-for-money of telemedicine in screening for ROP in high-risk infants.

Methods

A cost-utility analysis of screening and diagnosis of ROP using telemedicine was compared with the current process for ROP screening (Thai Clinical Trials Registry Identification No. TCTR20130911001). We used decision analytical models to compare costs and outcomes in terms of quality-adjusted life years (QALY) to the health provider and society. We used one-way sensitivity analysis and probabilistic sensitivity analysis to consider parameter uncertainty.

Results

The total capital cost for telemedicine to the health provider was 951,000 THB per year. With the base case analysis of 400 children screened per year per RetCam, the performance of screening and diagnosis of ROP using telemedicine (100% sensitivity and 97.8% specificity) was higher compared with the current method (88.9% sensitivity and 93.4% specificity). We therefore expect that blindness can be prevented in 3 children per 400 screening cases. The incremental cost to society of telemedicine compared with the current practice was 837 THB. Preventing just one child from becoming blind can save around 146,000 THB throughout their lifetime based on savings to welfare costs for disabled people. The incremental cost-effectiveness ratio of this telemedicine was 259 THB per case of prevented blindness and 17,397 THB per QALY saved.

Conclusions

Store and forward telemedicine for ROP screening is cost-effective.

A mobile team for screening of retinopathy of prematurity in India: Cost - effectiveness, outcomes, and impact assessment

PURPOSE:

To study the cost effectiveness, outcomes and impact of retinopathy of prematurity (ROP) screening and management model for urban neonatal intensive care units (NICUs).

STUDY DESIGN:

Public health intervention study.

METHODS:

This study was conducted in 2013. Staff of a mobile unit assessed all infants aged less than 34 weeks of Gestation age at birth and/or birth weight 1700 GM or less admitted in five NICUs between 2013 and 2015. A trained ophthalmologist performed bedside ROP screening through dilated pupils using indirect ophthalmoscope. ROP was graded and managed as per the International Classification of Retinopathy of Prematurity treatment guidelines. Counseling and laser treatment were the interventions. The incidence, grade and determinants of ROP were estimated. Direct and indirect costs were calculated to estimate the unit cost of screening and managing a child with ROP using the model.

RESULTS:

The study sample included 102 preterm/underweight infants. The prevalence of ROP of different grades in either eye was 32% (95% Confidence Intervals (CI): 23.2–41.5). ROP stage I was present in 75% of these eyes. The model could help in preventing/reducing visual disability in 4 infants with advanced stages of ROP. The unit cost of ROP screening, identifying one child with ROP and addressing visual disability due to ROP was US $ 198.9, 596.7 and 4,137.4 respectively.

CONCLUSION:

A mobile screening is likely feasible and cost-effective method to detect ROP and offer timely intervention for NICU in urban areas with limited resources.

Plus Disease in Retinopathy of Prematurity: Improving Diagnosis by Ranking Disease Severity and Using Quantitative Image Analysis

PURPOSE

To determine expert agreement on relative retinopathy of prematurity (ROP) disease severity and whether computer-based image analysis can model relative disease severity, and to propose consideration of a more continuous severity score for ROP.

DESIGN

We developed 2 databases of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP (i-ROP) cohort study and recruited expert physician, nonexpert physician, and nonphysician graders to classify and perform pairwise comparisons on both databases.

PARTICIPANTS

Six participating expert ROP clinician-scientists, each with a minimum of 10 years of clinical ROP experience and 5 ROP publications, and 5 image graders (3 physicians and 2 nonphysician graders) who analyzed images that were obtained during routine ROP screening in neonatal intensive care units.

METHODS

Images in both databases were ranked by average disease classification (classification ranking), by pairwise comparison using the Elo rating method (comparison ranking), and by correlation with the i-ROP computer-based image analysis system.

MAIN OUTCOME MEASURES

Interexpert agreement (weighted κ statistic) compared with the correlation coefficient (CC) between experts on pairwise comparisons and correlation between expert rankings and computer-based image analysis modeling.

RESULTS

There was variable interexpert agreement on diagnostic classification of disease (plus, preplus, or normal) among the 6 experts (mean weighted κ, 0.27; range, 0.06-0.63), but good correlation between experts on comparison ranking of disease severity (mean CC, 0.84; range, 0.74-0.93) on the set of 34 images. Comparison ranking provided a severity ranking that was in good agreement with ranking obtained by classification ranking (CC, 0.92). Comparison ranking on the larger dataset by both expert and nonexpert graders demonstrated good correlation (mean CC, 0.97; range, 0.95-0.98). The i-ROP system was able to model this continuous severity with good correlation (CC, 0.86).

CONCLUSIONS

Experts diagnose plus disease on a continuum, with poor absolute agreement on classification but good relative agreement on disease severity. These results suggest that the use of pairwise rankings and a continuous severity score, such as that provided by the i-ROP system, may improve agreement on disease severity in the future.

Teleophthalmology: improving patient outcomes?

Teleophthalmology is gaining importance as an effective eye care delivery modality worldwide. In many developing countries, teleophthalmology is being utilized to provide quality eye care to the underserved urban population and the unserved remote rural population. Over the years, technological innovations have led to improvement in evidence and teleophthalmology has evolved from a research tool to a clinical tool. The majority of the current teleophthalmology services concentrate on patient screening and appropriate referral to experts. Specialty care using teleophthalmology services for the pediatric group includes screening as well as providing timely care for retinopathy of prematurity (ROP). Among geriatric eye diseases, specialty teleophthalmology care is focused toward screening and referral for diabetic retinopathy (DR), glaucoma, age-related macular degeneration (ARMD), and other sight-threatening conditions. Comprehensive vision screening and refractive error services are generally covered as part of most of the teleophthalmology methods. Over the past decades, outcome assessment of health care system includes patients' assessments on their health, care, and services they receive. Outcomes, by and large, remain the ultimate validators of the effectiveness and quality of medical care. Teleophthalmology produces the same desired clinical outcome as the traditional system. Remote portals allow specialists to provide care over a larger region, thereby improving health outcomes and increasing accessibility of specialty care to a larger population. A high satisfaction level and acceptance is reported in the majority of the studies because of increased accessibility and reduced traveling cost and time. Considering the improved quality of patient care and patient satisfaction reported for these telemedicine services, this review explores how teleophthalmology helps to improve patient outcomes.

Influence of Fluorescein Angiography on the Diagnosis and Management of Retinopathy of Prematurity

PURPOSE

To examine the influence of fluorescein angiography (FA) on the diagnosis and management of retinopathy of prematurity (ROP).

DESIGN

Prospective cohort study.

PARTICIPANTS

Nine recognized ROP experts (3 pediatric ophthalmologists and 6 retina specialists) interpreted 32 sets (16 color fundus photographs and 16 color fundus photographs paired with the corresponding FA images) of wide-angle retinal images from infants with ROP.

METHODS

All experts independently reviewed the 32 image sets on a secure website and provided a diagnosis and management plan for the case presented, first based on color fundus photographs alone, and then based on color fundus photographs and corresponding FA images.

MAIN OUTCOME MEASURES

Sensitivity and specificity of the ROP diagnosis (zone, stage, plus disease, and category, i.e., no ROP, mild ROP, type 2 ROP, and ROP requiring treatment) were calculated using a consensus reference standard diagnosis, determined from the diagnosis of the color fundus photographs by 3 experienced readers in combination with the clinical diagnosis based on ophthalmoscopic examination. The κ statistic was used to analyze the average intergrader agreement among experts for the diagnosis of zone, stage, plus disease, and category.

RESULTS

Addition of FA to color fundus photography resulted in a significant improvement in sensitivity for diagnosis of stage 3 or worse disease (39.8% vs. 74.1%; P = 0.008), type 2 or worse ROP (69.4% vs. 86.8%; P = 0.013), and pre-plus or worse disease (50.5 vs. 62.6%; P = 0.031). There was a nonsignificant trend toward improved sensitivity for diagnosis of ROP requiring treatment (22.2% vs. 40.3%; P = 0.063). Using the κ statistic, addition of FA to color fundus photography significantly improved intergrader agreement for diagnosis of ROP requiring treatment. Addition of FA to color fundus photography did not affect intergrader agreement significantly for the diagnosis of stage, zone, or plus disease.

CONCLUSIONS

Compared with color fundus photography alone, FA may improve the sensitivity of diagnosis of ROP by experts, particularly for stage 3 disease. In addition, intergrader agreement for diagnosis of ROP requiring treatment may improve with FA interpretation.