External validation of a deep learning detection system for glaucomatous optic neuropathy: a real-world multicentre study.

ABSTRACT:

Objectives

To conduct an external validation of an automated artificial intelligence (AI) diagnostic system using fundus photographs from a real-life multicentre cohort.

Methods

We designed external validation in multiple scenarios, consisting of 3049 images from Qilu Hospital of Shandong University in China (QHSDU, validation dataset 1), 7495 images from three other hospitals in China (validation dataset 2), and 516 images from high myopia (HM) population of QHSDU (validation dataset 3). The corresponding sensitivity, specificity and accuracy of this AI diagnostic system to identify glaucomatous optic neuropathy (GON) were calculated.

Results

In validation datasets 1 and 2, the algorithm yielded accuracy of 93.18% and 91.40%, area under the receiver operating curves

SUBMITTER: Qian X 

PROVIDER: S-EPMC10698045 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature