Development and Validation of a Deep Learning Algorithm for Differentiation of Choroidal Nevi from Small Melanoma in Fundus Photographs.

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 un

SUBMITTER: Sabazade S 

PROVIDER: S-EPMC11483474 | biostudies-literature | 2025 Jan-Feb

REPOSITORIES: biostudies-literature