Identification of novel biomarkers for retinopathy of prematurity in preterm infants by use of innovative technologies and artificial intelligence

Abstract

Retinopathy of prematurity (ROP) is a leading cause of preventable vision loss in preterm infants. While appropriate screening is crucial for early identification and treatment of ROP, current screening guidelines remain limited by inter-examiner variability in screening modalities, absence of local protocol for ROP screening in some settings, a paucity of resources and an increased survival of younger and smaller infants. This review summarizes the advancements and challenges of current innovative technologies, artificial intelligence (AI), and predictive biomarkers for the diagnosis and management of ROP. We provide a contemporary overview of AI-based models for detection of ROP, its severity, progression, and response to treatment. To address the transition from experimental settings to real-world clinical practice, challenges to the clinical implementation of AI for ROP are reviewed and potential solutions are proposed. The use of optical coherence tomography (OCT) and OCT angiography (OCTA) technology is also explored, providing evaluation of subclinical ROP characteristics that are often imperceptible on fundus examination. Furthermore, we explore several potential biomarkers to reduce the need for invasive procedures, to enhance diagnostic accuracy and treatment efficacy. Finally, we emphasize the need of a symbiotic integration of biologic and imaging biomarkers and AI in ROP screening, where the robustness of biomarkers in early disease detection is complemented by the predictive precision of AI algorithms.