Artificial intelligence for retinopathy of prematurity.

Current Opinion in Ophthalmology
Rebekah H GensureJohn P Campbell

Abstract

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. 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. 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 ...Continue Reading

References

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Citations

Jan 30, 2021·Asia-Pacific Journal of Ophthalmology·Shaun Sebastian SimDaniel Sw Ting
Sep 4, 2021·Ophthalmology·Michael D AbràmoffUNKNOWN Collaborative Community on Ophthalmic Imaging Executive Committee and Foundational Principles of Ophthalmic Imaging and Algo

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