Dow, ER; Keenan, TDL; Lad, EM; Lee, AY; Lee, CS; Lowenstein, A; Eydelman, MB; ... Collaborative Community for Ophthalmic Imaging executive committ; + view all Dow, ER; Keenan, TDL; Lad, EM; Lee, AY; Lee, CS; Lowenstein, A; Eydelman, MB; Chew, EY; Keane, PA; Lim, JI; Collaborative Community for Ophthalmic Imaging executive committ; - view fewer (2022) From Data to Deployment: The Collaborative Community on Ophthalmic Imaging Roadmap for Artificial Intelligence in Age-Related Macular Degeneration. Ophthalmology , 129 (5) e43-e59. 10.1016/j.ophtha.2022.01.002.

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Abstract

IMPORTANCE: Healthcare systems worldwide are challenged to provide adequate care for the 200 million individuals with age-related macular degeneration (AMD). Artificial intelligence (AI) has the potential to make a significant positive impact on the diagnosis and management of patients with AMD. However, the development of effective AI devices for clinical care faces numerous considerations and challenges, a fact evidenced by a current absence of FDA-approved AI devices for AMD. OBJECTIVES: To delineate the state of AI for AMD including current data, standards, achievements, and challenges. EVIDENCE Members of the Collaborative Community on Ophthalmic Imaging working group for AI in AMD attended an inaugural meeting on September 7, 2020 to discuss the topic. Subsequently, they undertook a comprehensive review of the medical literature relevant to the topic. Members engaged in meetings and discussion through December 2021 to synthesize the information and arrive at consensus. FINDINGS: Existing infrastructure for robust AI development for AMD includes several large, labeled datasets of color fundus photography (CFP) and optical coherence tomography (OCT) images. However, image data often does not contain meta-data 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, although potential solutions are under investigation. Computing resources may be adequate for current applications, but knowledge of machine learning (ML) 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 real-world generalization. CONCLUSIONS: AND RELEVANCE: 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 curation 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 will eventually benefit providers, healthcare systems, and patients.

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