The assessment of fundus image quality labeling reliability among graders with different backgrounds.

ABSTRACT:

Purpose

For the training of machine learning (ML) algorithms, correctly labeled ground truth data are inevitable. In this pilot study, we assessed the performance of graders with different backgrounds in the labeling of retinal fundus image quality.

Methods

Color fundus photographs were labeled using a Python-based tool using four image categories: excellent (E), good (G), adequate (A) and insufficient for grading (I). We enrolled 8 subjects (4 with and 4 without medical background, groups M and NM, respectively) to whom a tutorial was presented on image quality requirements. We randomly selected 200 images from a pool of 18,145 expert-labeled images (50/E, 50/G, 50/A, 50/I). The performance of the grading was timed and the agreement was assessed. An additional grading round

SUBMITTER: Laurik-Feuerstein KL 

PROVIDER: S-EPMC9321443 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature