June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Ophthalmic Imaging Roadmap for Artificial Intelligence: from Data to Deployment
Author Affiliations & Notes
  • Emily Y. Chew
    National Eye Institute, Bethesda, Maryland, United States
  • Footnotes
    Commercial Relationships   Emily Chew None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 3284. doi:
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      Emily Y. Chew; Ophthalmic Imaging Roadmap for Artificial Intelligence: from Data to Deployment. Invest. Ophthalmol. Vis. Sci. 2023;64(8):3284.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Presentation Description : Health care systems worldwide are challenged to provide adequate care for the hundreds of millions individuals with diseases such as age-related macular degeneration (AMD) and other retinovascular diseases including diabetic retinopathy. Artificial intelligence (AI) has the potential to make a significant, positive impact on the diagnosis and management of patients with blinding diseases, espeically AMD; however, the development of effective AI devices for clinical care faces numerous considerations and challenges, a fact evidenced by a current absence of Food and Drug Administration (FDA)-approved AI devices for AMD.

Existing infrastructure for robust AI development for AMD includes several large, labeled data sets of color fundus photography and OCT images; however, image data often do not contain the metadata necessary for the development of reliable, valid, and generalizable models. Data sharing for AMD model development is made difficult by restrictions on data privacy and security. Computing resources may be adequate for current applications, but knowledge of machine learning development may be scarce in many clinical ophthalmology settings. Despite these challenges, researchers have produced promising AI models for AMD for screening, diagnosis, prediction, and monitoring. Future goals include defining benchmarks to facilitate regulatory authorization and subsequent clinical setting generalization.

Delivering an FDA-authorized, AI-based device for clinical care in AMD involves numerous considerations, including the identification of an appropriate clinical application; acquisition and development of a large, high-quality data set; development of the AI architecture; training and validation of the model; and functional interactions between the model output and clinical end user. The research efforts undertaken to date represent starting points for the medical devices that eventually will benefit providers, health care systems, and patients.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

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