Plus Disease in Telemedicine Approaches to Evaluating Acute-Phase ROP (e-ROP) Study: Characteristics, Predictors, and Accuracy of Image Grading

Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities

The diagnosis of retinopathy of prematurity (ROP) is primarily image-based and suitable for implementation of artificial intelligence (AI) systems. Increasing incidence of ROP, especially in low and middle-income countries, has also put tremendous stress on health care systems. Barriers to the implementation of AI include infrastructure, regulatory, legal, cost, sustainability, and scalability. This review describes currently available AI and imaging systems, how a stable telemedicine infrastructure is crucial to AI implementation, and how successful ROP programs have been run in both low and middle-income countries and high-income countries. More work is needed in terms of validating AI systems with different populations with various low-cost imaging devices that have recently been developed. A sustainable and cost-effective ROP screening program is crucial in the prevention of childhood blindness.

Comparison of RetCam and Smartphone-Based Photography for Retinopathy of Prematurity Screening

This study aimed to compare the clinical performance between a smartphone-based fundus photography device and a contact imaging device for retinopathy of prematurity (ROP) screening. All patients were first examined with binocular indirect ophthalmoscopy (BIO), which served as the reference standard. The patients were then assessed by two devices. Imaging quality, ability to judge the zone and stage of ROP, agreement with the BIO results, vital signs, and pain scores were compared between these two devices. In total, 142 eyes of 71 infants were included. For the smartphone-based fundus photography, image quality was graded excellent or acceptable in 91.4% of examinations, although it was still significantly inferior to that of the contact imaging device (p < 0.001). The smartphone-based fundus photography images had moderate agreement with the BIO results regarding the presence or absence of plus disease (Cohen’s κ = 0.619), but evaluating the zone (p < 0.001) and stage (p < 0.001) of ROP was difficult. Systemic parameters, except for heart rate, were similar between the two imaging devices (all p > 0.05). In conclusion, although the smartphone-based fundus photography showed moderate agreement for determining the presence or absence of plus disease, it failed to identify the zone and stage of ROP.

Cost-effectiveness of Artificial Intelligence-Based Retinopathy of Prematurity Screening

Importance

Artificial intelligence (AI)-based retinopathy of prematurity (ROP) screening may improve ROP care, but its cost-effectiveness is unknown.

Objective

To evaluate the relative cost-effectiveness of autonomous and assistive AI-based ROP screening compared with telemedicine and ophthalmoscopic screening over a range of estimated probabilities, costs, and outcomes.

Design, Setting, and Participants

A cost-effectiveness analysis of AI ROP screening compared with ophthalmoscopy and telemedicine via economic modeling was conducted. Decision trees created and analyzed modeled outcomes and costs of 4 possible ROP screening strategies: ophthalmoscopy, telemedicine, assistive AI with telemedicine review, and autonomous AI with only positive screen results reviewed. A theoretical cohort of infants requiring ROP screening in the United States each year was analyzed.

Main Outcomes and Measures

Screening and treatment costs were based on Current Procedural Terminology codes and included estimated opportunity costs for physicians. Outcomes were based on the Early Treatment of ROP study, defined as timely treatment, late treatment, or correctly untreated. Incremental cost-effectiveness ratios were calculated at a willingness-to-pay threshold of $100 000. One-way and probabilistic sensitivity analyses were performed comparing AI strategies to telemedicine and ophthalmoscopy to evaluate the cost-effectiveness across a range of assumptions. In a secondary analysis, the modeling was repeated and assumed a higher sensitivity for detection of severe ROP using AI compared with ophthalmoscopy.

Results

This theoretical cohort included 52 000 infants born 30 weeks' gestation or earlier or weighed 1500 g or less at birth. Autonomous AI was as effective and less costly than any other screening strategy. AI-based ROP screening was cost-effective up to $7 for assistive and $34 for autonomous screening compared with telemedicine and $64 and $91 compared with ophthalmoscopy in the primary analysis. In the probabilistic sensitivity analysis, autonomous AI screening was more than 60% likely to be cost-effective at all willingness-to-pay levels vs other modalities. In a second simulated cohort with 99% sensitivity for AI, the number of late treatments for ROP decreased from 265 when ROP screening was performed with ophthalmoscopy to 40 using autonomous AI.

Conclusions and Relevance

AI-based screening for ROP may be more cost-effective than telemedicine and ophthalmoscopy, depending on the added cost of AI and the relative performance of AI vs human examiners detecting severe ROP. As AI-based screening for ROP is commercialized, care must be given to appropriately price the technology to ensure its benefits are fully realized.

Stable incidence but regional differences in retinopathy of prematurity in Norway from 2009 to 2017

PURPOSE

To explore the changes over time and regional differences in the incidence of retinopathy of prematurity (ROP) in a national cohort of infants born <28 weeks' gestational age (GA).

METHODS

A population-based study of infants with GA <28 weeks in Norway from 2009 to 2017. Prospectively collected data on clinical variables and outcomes were obtained from the Norwegian Neonatal Network.

RESULTS

Of 1499 live-born infants transferred to a neonatal intensive care unit, 1156 were discharged alive. Four-hundred and fifty-eight infants (39.6%) had ROP, 152 (13.1%) had severe ROP, and 110 (9.5%) were treated for ROP. Eleven hundred infants (95.2%) had complete data sets. In a model comprising region of primary care, GA [odds ratios (OR): 0.65; 95% CI: 0.55-0.77], growth velocity (OR: 1.10; 95% CI: 1.00-2.00), medically treated patent ductus arteriosus (OR: 1.80; 95% CI: 1.19-2.72), weeks of supplemental oxygen (OR: 1.07; 95% CI: 1.03 to 1.11) and region of primary care (OR: 4.95; 95% CI: 3.05-8.04 for the pair of regions with the highest estimated OR) were significantly associated with severe ROP. Additionally, institutional differences for severe ROP were found, with ORs from 0.41 (95% CI: 0.05-3.23) to 5.36 (95% CI: 3.05-9.43) using the largest institution as reference. Incidences were stable over time after adjusting for GA. A larger proportion was treated with anti-vascular endothelial growth factor after 2011.

CONCLUSIONS

The incidence of severe ROP was stable between 2009 and 2017 in Norway. Regional and institutional differences need to be explored in future studies.

Progression from preplus to plus disease in the Telemedicine Approaches to Evaluating Acute-Phase Retinopathy of Prematurity (e-ROP) Study: incidence, timing, and predictors

PURPOSE

To determine the incidence of and timing and predictors for progression from pre-plus to plus disease, based on evaluation of images.

METHODS

Two trained readers independently evaluated posterior pole images of infants from 13 North American centers for pre-plus/plus disease, stage, and zone of retinopathy of prematurity (ROP). Discrepancies between readers were adjudicated. To be eligible for analysis, eyes had to have at least two imaging sessions, the earlier one with pre-plus disease.

RESULTS

Of 681 eyes of 444 infants with pre-plus first detected at mean postmenstrual age (PMA) of 35.5 ± 2.1 weeks, 54 (7.9%) progressed to plus disease at a mean PMA of 37.6 ± 2.4 weeks with the mean interval for progression of 2.7 weeks (range, 0.4-8.9 weeks). Progression rate was higher for eyes with larger number of quadrants of pre-plus (44% for eyes with four quadrants vs 4% with one quadrant [P < 0.0001]), earlier PMA with pre-plus (18% for 32 weeks' PMA vs 3% for PMA of >37 weeks [P = 0.02]), higher ROP stage (12% for stage 3, 2.5% for no ROP [P < 0.0001]), lower ROP zone (24% for zone I, 6% for zone II or no ROP [P < 0.0001]) at the time of first pre-plus detection.

CONCLUSIONS

Based on image evaluation, 8% of eyes progressed from pre-plus to plus disease at a mean interval of 3 weeks. Pre-plus in multiple quadrants, higher stages of ROP, and lower zones of ROP were associated with higher risk of progression. Image evaluation for pre-plus may help in the identification of high-risk eyes for developing plus disease.

Artificial intelligence for retinopathy of prematurity

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.

Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy, Third Edition

Contributors The following document and appendices represent the third edition of the Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy. These guidelines were developed by the Diabetic Retinopathy Telehealth Practice Guidelines Working Group. This working group consisted of a large number of subject matter experts in clinical applications for telehealth in ophthalmology. The editorial committee consisted of Mark B. Horton, OD, MD, who served as working group chair and Christopher J. Brady, MD, MHS, and Jerry Cavallerano, OD, PhD, who served as cochairs. The writing committees were separated into seven different categories. They are as follows: 1.Clinical/operational: Jerry Cavallerano, OD, PhD (Chair), Gail Barker, PhD, MBA, Christopher J. Brady, MD, MHS, Yao Liu, MD, MS, Siddarth Rathi, MD, MBA, Veeral Sheth, MD, MBA, Paolo Silva, MD, and Ingrid Zimmer-Galler, MD. 2.Equipment: Veeral Sheth, MD (Chair), Mark B. Horton, OD, MD, Siddarth Rathi, MD, MBA, Paolo Silva, MD, and Kristen Stebbins, MSPH. 3.Quality assurance: Mark B. Horton, OD, MD (Chair), Seema Garg, MD, PhD, Yao Liu, MD, MS, and Ingrid Zimmer-Galler, MD. 4.Glaucoma: Yao Liu, MD, MS (Chair) and Siddarth Rathi, MD, MBA. 5.Retinopathy of prematurity: Christopher J. Brady, MD, MHS (Chair) and Ingrid Zimmer-Galler, MD. 6.Age-related macular degeneration: Christopher J. Brady, MD, MHS (Chair) and Ingrid Zimmer-Galler, MD. 7.Autonomous and computer assisted detection, classification and diagnosis of diabetic retinopathy: Michael Abramoff, MD, PhD (Chair), Michael F. Chiang, MD, and Paolo Silva, MD.

Telemedicine for Retinopathy of Prematurity

Background: Retinopathy of prematurity (ROP) is a disease of the retinal vasculature that remains a leading cause of childhood blindness worldwide despite improvements in the systemic care of premature newborns. Screening for ROP is effective and cost-effective, but in many areas, access to skilled examiners to conduct dilated examinations is poor. Remote screening with retinal photography is an alternative strategy that may allow for improved ROP care. Methods: The current literature was reviewed to find clinical trials and expert consensus documents on the state-of-the-art of telemedicine for ROP. Results: Several studies have confirmed the utility of telemedicine for ROP. In addition, several clinical studies have reported favorable long-term results. Many investigators have reinforced the need for detailed protocols on image acquisition and image interpretation. Conclusions: Telemedicine for ROP appears to be a viable alternative to live ophthalmoscopic examinations in many circumstances. Standardization and documentation afforded by telemedicine may provide additional benefits to providers and their patients. With continued improvements in image quality and affordability of imaging systems as well as improved automated image interpretation tools anticipated in the near future, telemedicine for ROP is expected to play an expanding role for a uniquely vulnerable patient population.