Grading of diabetic retinopathy using a pre-segmenting deep learning classification model: Validation of an automated algorithm.

ABSTRACT:

Purpose

To validate the performance of autonomous diabetic retinopathy (DR) grading by comparing a human grader and a self-developed deep-learning (DL) algorithm with gold-standard evaluation.

Methods

We included 500, 6-field retinal images graded by an expert ophthalmologist (gold standard) according to the International Clinical Diabetic Retinopathy Disease Severity Scale as represented with DR levels 0-4 (97, 100, 100, 103, 100, respectively). Weighted kappa was calculated to measure the DR classification agreement for (1) a certified human grader without, and (2) with assistance from a DL algorithm and (3) the DL operating autonomously. Using any DR (level 0 vs. 1-4) as a cutoff, we calculated sensitivity, specificity, as well as positive and negative predictive values (

SUBMITTER: Similie DE 

PROVIDER: S-EPMC11810534 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature