Carol Shields, Ocular Oncology Service, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, USAFollow
Sara E. Lally, Ocular Oncology Service, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, USA
Lauren Dalvin, Department of Ophthalmology, Mayo Clinic, Rochester, MN, USA
Mandeep Sagoo, Ocular Oncology Service, Moorfields Eye Hospital and NIHR Biomedical Research Centre for Ophthalmology at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, UK
Marco Pellegrini, Eye Clinic, Department of Biomedical and Clinical Science “Luigi Sacco”, Luigi Sacco Hospital, University of Milan, Milan, Italy
Swathi Kaliki, The Operation Eyesight Universal Institute for Eye Cancer, LV Prasad Eye Institute, Hyderabad, India
Ahmet Kaan Gündüz, Department of Ophthalmology, Ankara University School of Medicine, Ankara, Turkey
Minoru Furuta, Department of Ophthalmology, Fukushima Medical University, Fukushima, Japan and Department of Ophthalmology, Yachiyo Medical Center, Tokyo Women's Medical University, Tokyo, Japan
Prithvi Mruthyunjaya, Ocular Oncology Service, Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, USA
Adrian T. Fung, Westmead and Central Clinical Schools, Discipline of Clinical Ophthalmology and Eye Health, University of Sydney, Australia and Department of Ophthalmology, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
Jay S. Duker, New England Eye Center, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA
Sara M. Selig, Melanoma Research Foundation, Washington, DC, USA
Antonio Yaghy, Ocular Oncology Service, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, USAFollow
Sandor R. Ferenczy, Ocular Oncology Service, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, USA
Malvina B. Eydelman, Office of Ophthalmic, Anesthesia, Respiratory, Ear, Nose and Throat (ENT), and Dental Devices, Food & Drug Administration (FDA), Washington, DC
Mark S. Blumenkranz, Department of Ophthalmology, Ophthalmology Innovation Program, Byers Eye Institute, Stanford University, Palo Alto, CA, USA

Document Type

Article

Publication Date

2-1-2021

Comments

This article is the author's final published version in Translational Vision Science and Technology, Volume 10, Issue 2, 2021, Article number 24, Pages 1-10.

The published version is available at https://doi.org/10.1167/tvst.10.2.24

Copyright © 2021 The Authors.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Purpose: To discuss the evolution of noninvasive diagnostic methods in the identification of choroidal nevus and determination of risk factors for malignant transformation as well as introduce the novel role that artificial intelligence (AI) can play in the diagnostic process. Methods: White paper. Results: Longstanding diagnostic methods to stratify benign choroidal nevus from choroidal melanoma and to further determine the risk for nevus transformation into melanoma have been dependent on recognition of key clinical features by ophthalmic examination. These risk factors have been derived from multiple large cohort research studies over the past several decades and have garnered widespread use throughout the world. More recent publications have applied ocular diagnostic testing (fundus photog-raphy, ultrasound examination, autofluorescence, and optical coherence tomography) to identify risk factors for the malignant transformation of choroidal nevus based on multimodal imaging features. The widespread usage of ophthalmic imaging systems to identify and follow choroidal nevus, in conjunction with the characterization of malignant transformation risk factors via diagnostic imaging, presents a novel path to apply AI. Conclusions: AI applied to existing ophthalmic imaging systems could be used for both identification of choroidal nevus and as a tool to aid in earlier detection of transformation to malignant melanoma. Translational Relevance: Advances in AI models applied to ophthalmic imaging systems have the potential to improve patient care, because earlier detection and treatment of melanoma has been proven to improve long-term clinical outcomes.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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English

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