Aggressive Posterior Retinopathy of Prematurity

Bellsmith, Kellyn N.; Brown, James M.; Kim, Sang Jin; Goldstein, Israel; Coyner, Aaron S.; Ostmo, Susan; Gupta, Krishnendu; Chan, Robison Vernon Paul; Kalpathy–Cramer, Jayashree; Chiang, Michael F.; Campbell, J. Peter

doi:10.1016/j.ophtha.2020.01.052

Cited by 30 publications

(14 citation statements)

References 28 publications

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“…This model incorporated 2 variables: GA at birth and an artificial intelligence (AI)−based vascular severity score (VSS) derived from a fundus image at 32 to 33 weeks’ postmenstrual age (PMA). This time point was based on prior work suggesting that infants who would eventually develop TR-ROP could be identified as early as 4 to 6 weeks before diagnosis . In 2 US populations, we found 100.0% sensitivity and 48.9% to 80.8% specificity for TR-ROP, with identification of at-risk infants occurring an average of 4 weeks before diagnosis .…”

Section: Introductionmentioning

confidence: 73%

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

et al. 2022

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ImportanceRetinopathy 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.ObjectiveTo 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 ParticipantsIn 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 MeasuresPrimary 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.ResultsA 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 RelevanceResults 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.

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Section: Introductionmentioning

confidence: 73%

“…By focusing resources on those who screen positive, it is less likely TR-ROP will be missed or treated late, which is a common cause of adverse outcomes in ROP care. This is especially true for A-ROP, which progresses earlier and faster than typical TR-ROP …”

Section: Discussionmentioning

confidence: 97%

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

et al. 2022

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“…This Previous work has described the use of i-ROP VSS for the assessment of overall ROP severity, 15,19 screening for type 1 ROP, [16][17][18] monitoring of disease progression, 20,24 and monitoring of initial response to treatment after laser or anti-VEGF therapy. 21 The results of our current analysis suggest that VSS may be useful for screening of ROP reactivation requiring retreatment after anti-VEGF therapy.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence–Based Vascular Severity Score

et al. 2023

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ImportanceOne of the biggest challenges when using anti–vascular endothelial growth factor (VEGF) agents to treat retinopathy of prematurity (ROP) is the need to perform long-term follow-up examinations to identify eyes at risk of ROP reactivation requiring retreatment.ObjectiveTo evaluate whether an artificial intelligence (AI)–based vascular severity score (VSS) can be used to analyze ROP regression and reactivation after anti-VEGF treatment and potentially identify eyes at risk of ROP reactivation requiring retreatment.Design, Setting, and ParticipantsThis prognostic study was a secondary analysis of posterior pole fundus images collected during the multicenter, double-blind, investigator-initiated Comparing Alternative Ranibizumab Dosages for Safety and Efficacy in Retinopathy of Prematurity (CARE-ROP) randomized clinical trial, which compared 2 different doses of ranibizumab (0.12 mg vs 0.20 mg) for the treatment of ROP. The CARE-ROP trial screened and enrolled infants between September 5, 2014, and July 14, 2016. A total of 1046 wide-angle fundus images obtained from 19 infants at predefined study time points were analyzed. The analyses of VSS were performed between January 20, 2021, and November 18, 2022.InterventionsAn AI-based algorithm assigned a VSS between 1 (normal) and 9 (most severe) to fundus images.Main Outcomes and MeasuresAnalysis of VSS in infants with ROP over time and VSS comparisons between the 2 treatment groups (0.12 mg vs 0.20 mg of ranibizumab) and between infants who did and did not receive retreatment for ROP reactivation.ResultsAmong 19 infants with ROP in the CARE-ROP randomized clinical trial, the median (range) postmenstrual age at first treatment was 36.4 (34.7-39.7) weeks; 10 infants (52.6%) were male, and 18 (94.7%) were White. The mean (SD) VSS was 6.7 (1.9) at baseline and significantly decreased to 2.7 (1.9) at week 1 (P &lt; .001) and 2.9 (1.3) at week 4 (P &lt; .001). The mean (SD) VSS of infants with ROP reactivation requiring retreatment was 6.5 (1.9) at the time of retreatment, which was significantly higher than the VSS at week 4 (P &lt; .001). No significant difference was found in VSS between the 2 treatment groups, but the change in VSS between baseline and week 1 was higher for infants who later required retreatment (mean [SD], 7.8 [1.3] at baseline vs 1.7 [0.7] at week 1) vs infants who did not (mean [SD], 6.4 [1.9] at baseline vs 3.0 [2.0] at week 1). In eyes requiring retreatment, higher baseline VSS was correlated with earlier time of retreatment (Pearson r = −0.9997; P &lt; .001).Conclusions and RelevanceIn this study, VSS decreased after ranibizumab treatment, consistent with clinical disease regression. In cases of ROP reactivation requiring retreatment, VSS increased again to values comparable with baseline values. In addition, a greater change in VSS during the first week after initial treatment was found to be associated with a higher risk of later ROP reactivation, and high baseline VSS was correlated with earlier retreatment. These findings may have implications for monitoring ROP regression and reactivation after anti-VEGF treatment.

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“…26 The National Eye Institute has funded the development of an AI imaging technology to detect retinopathy of prematurity that was recently granted breakthrough status by the US Food and Drug Administration. 27 Specific applications that were identified as having high translatable potential include environmental health and interpersonal communication technologies. Interpersonal communication technologies can aid with communication for patients with functional impairments through braincomputer interfaces, hand sign analysis, and eye tracking, among other methods.…”

Section: Discussionmentioning

confidence: 99%

“…Among the National Center for Advancing Translational Sciences core technologies are machine learning methods for prediction of chemical properties and NLP for extracting knowledge from data in rare diseases . The National Eye Institute has funded the development of an AI imaging technology to detect retinopathy of prematurity that was recently granted breakthrough status by the US Food and Drug Administration …”

Section: Discussionmentioning

confidence: 99%

Translatability Analysis of National Institutes of Health–Funded Biomedical Research That Applies Artificial Intelligence

et al. 2022

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Key Points Question Which applications of artificial intelligence (AI) in biomedical research could generate the most translational value (ie, ability to lead to development that has measurable value for human health)? Findings In this cohort study using bibliometric analysis, 2 citation-based metrics were used as proxies for translational impact, and 75 unique medical applications of AI were identified from a set of 16 629 National Institutes of Health awards related to AI, using natural language processing. Varied applications were found to have high translatability, but bias against applications in basic biochemical analysis was evident. Meaning These findings suggest that National Institutes of Health award data applications of AI in biomedical research are not equivalent: some demonstrate greater potential for translational impact than others.

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Aggressive Posterior Retinopathy of Prematurity

Cited by 30 publications

References 28 publications

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

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

Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence–Based Vascular Severity Score

Translatability Analysis of National Institutes of Health–Funded Biomedical Research That Applies Artificial Intelligence

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