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Perspectives on screening in retinopathy of prematurity: new algorithms and AI tools
Helen Kearns1,2, Sarah Hull1,2
1Department of Ophthalmology, University of Auckland, Auckland, New Zealand.
Insights
Retinopathy of prematurity (ROP) screening identifies a blinding eye disorder in premature infants. New methods using weight gain and AI may improve accuracy and reduce the burden of current screening criteria.
Area of Science:
- Ophthalmology
- Neonatology
- Medical Imaging
Background:
- Retinopathy of prematurity (ROP) is a major cause of preventable childhood blindness in premature infants.
- Current screening relies on birth weight and gestational age, lacking specificity and potentially missing at-risk infants.
- Rising survival rates of premature infants increase the clinical and economic burden of ROP screening.
Purpose of the Study:
- To review alternative ROP screening algorithms based on postnatal weight gain.
- To discuss the application and limitations of these alternative algorithms.
- To explore the potential of artificial intelligence (AI) in enhancing ROP screening accuracy, efficiency, and equity.
Main Methods:
- Review of existing literature on ROP screening criteria and alternative algorithms.
- Discussion of proposed algorithms utilizing postnatal weight gain.
- Exploration of AI applications in ROP screening.
Main Results:
- Current ROP screening criteria (birth weight, gestational age) have limitations in specificity.
- Alternative algorithms using postnatal weight gain show promise but require further evaluation.
- AI has the potential to significantly improve ROP screening accuracy and efficiency.
Conclusions:
- There is a need for more specific and efficient ROP screening methods.
- Postnatal weight gain algorithms and AI offer promising avenues for improving ROP detection.
- Enhanced screening strategies are crucial for reducing the burden of preventable childhood blindness globally.
Abstract:
Retinopathy of prematurity (ROP) is a vasoproliferative blinding disorder of the retina, unique to premature infants and a leading cause of preventable childhood blindness globally. Screening of premature babies aims to identify disease reaching a threshold for treatment needed in 6-10% of babies screened. Current screening criteria are based on birth weight (BW) and gestational age (GA) with an additional third criteria for babies deemed at risk by the neonatologist due to an unstable postnatal course or prolonged use of oxygen. These lack specificity and could potentially miss babies at risk. With increased survival rates of premature and extreme premature babies, ROP screening numbers are rising with associated clinical and economic burden. Several alternative algorithms have been proposed based on postnatal weight gain and in this review, the application and pitfalls of these will be discussed as well as the potential role of artificial intelligence in improving accuracy, efficiency, and equity in Australasia and low-middle-income countries.
