Development and Validation of a Deep Learning Algorithm for Differentiation of Choroidal Nevi from Small Melanoma in Fundus Photographs
Shiva Sabazade, Marco A. Lumia Michalski, Jakub Bartoszek, Maria Filì, Mats Holmström, Gustav Stålhammar
Abstract
Purpose: To develop and validate a deep learning algorithm capable of differentiating small choroidal melanomas from nevi. Design: Retrospective multicenter cohort study. Participants: A total of 802 images from 688 patients diagnosed with choroidal nevi or melanoma. Methods: Wide field and standard field fundus photographs were collected from patients diagnosed with choroidal nevi or melanoma by ocular oncologists during clinical examinations. A lesion was classified as a nevus if it was followed for at least 5 years without being rediagnosed as melanoma. A neural network optimized for image classification was trained and validated on cohorts of 495 and 168 images and subsequently tested on independent sets of 86 and 53 images. Main Outcome Measures: Area under the curve (AUC) in receiver operating characteristic analysis for differentiating small choroidal melanomas from nevi. Results: values Bonferroni corrected). Conclusions: This study develops and validates a deep learning algorithm for differentiating small choroidal melanomas from nevi, matching or surpassing the discriminatory performance of experienced human ophthalmologists. Further research will aim to validate its utility in clinical settings. Financial Disclosures: Financial DisclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.