Predicting central choroidal thickness from colour fundus photographs using deep learning

TL;DR

This paper introduces a deep learning method to estimate choroidal thickness from fundus images, which could aid in detecting eye diseases.

Contribution

The paper presents the first validated deep learning algorithm for estimating central choroidal thickness from color fundus photographs.

Findings

The standard deviation of 10-fold cross-validation was 73 μm.

Re-learning reduced the standard deviation in validation datasets from other institutions.

The algorithm can help identify choroidal thickening and thinning in clinical settings.

Abstract

The estimation of central choroidal thickness from colour fundus images can improve disease detection. We developed a deep learning method to estimate central choroidal thickness from colour fundus images at a single institution, using independent datasets from other institutions for validation. A total of 2,548 images from patients who underwent same-day optical coherence tomography examination and colour fundus imaging at the outpatient clinic of Jichi Medical University Hospital were retrospectively analysed. For validation, 393 images from three institutions were used. Patients with signs of subretinal haemorrhage, central serous detachment, retinal pigment epithelial detachment, and/or macular oedema were excluded. All other fundus photographs with a visible pigment epithelium were included. The main outcome measure was the standard deviation of 10-fold cross-validation. Validation was performed using the original algorithm and the algorithm after learning based on images from all institutions. The standard deviation of 10-fold cross-validation was 73 μm. The standard deviation for other institutions was reduced by re-learning. We describe the first application and validation of a deep learning approach for the estimation of central choroidal thickness from fundus images. This algorithm is expected to help graders judge choroidal thickening and thinning.

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