Evaluation of the red reflex: An overview for the pediatrician

The newborn screening tests in Brazil: regional and socioeconomic prevalence and inequalities in 2013 and 2019

OBJECTIVE

To analyze the prevalence evolution of Guthrie, hearing, and eye screening testing among newborns in Brazil, between 2013 and 2019, according to demographic and socioeconomic characteristics.

METHODS

This is a cross-sectional study with data from 5231 infants from the Pesquisa Nacional de Saúde (PNS), in 2013, and 6637 infants, in 2019, for the Guthrie test, hearing, and red reflex tests. The authors analyzed the outcomes according to the region of residence, self-reported color/race, having health insurance, and per capita household income. By using bivariate and multivariate Poisson regression models, the prevalence ratios and their respective 95 % Confidence Intervals (CI95%) were calculated for each year.

RESULTS

In 2013, Guthrie test, hearing, and red reflex tests were performed in 96.5 % (95%CI 95,8;97,0), 65.8 % (95%CI 63,9;67,7), and 60.4 % (95%CI 58,5;62,3) of infants, respectively. In 2019, the prevalence was 97.8 % (95%CI 97,3;98,2) in the Guthrie test, 81.6 % (95%CI 80,3;82,9) in the hearing test, and 78.6 % (95%CI 77,1;79,9) in the red reflex test. The testing frequency was higher among residents of the Southeast and South regions of Brazil, among infants whose mother or guardian was white, had health insurance, and was in the higher income strata; and the most evident differences were in the eye and hearing testing.

CONCLUSIONS

The coverage inequalities according to the region of residence, income, and having health insurance highlight the need to use strategies that enable exams to be carried out, with more information about their importance, encompassing actions from primary care, prenatal care to the puerperium, aiming at universal access and equity.

Automatic Refractive Error Estimation Using Deep Learning-Based Analysis of Red Reflex Images

Purpose/Background: We evaluate how a deep learning model can be applied to extract refractive error metrics from pupillary red reflex images taken by a low-cost handheld fundus camera. This could potentially provide a rapid and economical vision-screening method, allowing for early intervention to prevent myopic progression and reduce the socioeconomic burden associated with vision impairment in the later stages of life. Methods: Infrared and color images of pupillary crescents were extracted from eccentric photorefraction images of participants from Choithram Hospital in India and Dargaville Medical Center in New Zealand. The pre-processed images were then used to train different convolutional neural networks to predict refractive error in terms of spherical power and cylindrical power metrics. Results: The best-performing trained model achieved an overall accuracy of 75% for predicting spherical power using infrared images and a multiclass classifier. Conclusions: Even though the model's performance is not superior, the proposed method showed good usability of using red reflex images in estimating refractive error. Such an approach has never been experimented with before and can help guide researchers, especially when the future of eye care is moving towards highly portable and smartphone-based devices.

Trends in Neonatal Ophthalmic Screening Methods

Neonatal ophthalmic screening should lead to early diagnosis of ocular abnormalities to reduce long-term visual impairment in selected diseases. If a treatable pathology is diagnosed within a few days after the birth, adequate therapy may be indicated to facilitate the best possible conditions for further development of visual functions. Traditional neonatal ophthalmic screening uses the red reflex test (RRT). It tests the transmittance of the light through optical media towards the retina and the general disposition of the central part of the retina. However, RRT has weaknesses, especially in posterior segment affections. Wide-field digital imaging techniques have shown promising results in detecting anterior and posterior segment pathologies. Particular attention should be paid to telemedicine and artificial intelligence. These methods can improve the specificity and sensitivity of neonatal eye screening. Both are already highly advanced in diagnosing and monitoring of retinopathy of prematurity.