Comparison between choroidal thickness measurements from swept-source optical coherence tomography devices in highly myopic Chinese adults.

Authors

Syna Sreng, Huanhuan Tan, Kai Xiong Cheong, Yanfeng Jiang, Yvonne Ng, Valentina Bellemo, Xiangzhong Tang, Zhongping Chen, Ecosse Luc Lamoureux, Leopold Schmetterer, Quan V Hoang, Seang-Mei Saw, Damon Wong, Weizhong Lan

Published in

The British journal of ophthalmology. Sep 11, 2026. Epub Sep 11, 2026.

Abstract

Reproducibility of choroidal thickness (CT) measurements is imperative when comparing choroidal imaging from different optical coherence tomography (OCT) platforms particularly in high myopia. This study compared CT measurements from commercial swept-source OCT devices in highly myopic Chinese adults using vendor-provided software and a deep learning segmentation model.
97 participants underwent imaging with DREAM OCT and Triton OCT. CT was measured across the Early Treatment Diabetic Retinopathy Study (ETDRS) grid using vendor-provided software and a deep learning model, with ocular magnification correction. Agreement was evaluated using mean differences with 95% CIs, intraclass correlation coefficients (ICCs) and Bland-Altman plots.
With vendor-provided software, DREAM OCT yielded thicker CT than Triton OCT (global ETDRS: 221.48±66.88 µm vs 156.90±57.64 µm), with mean differences of 61.04-71.22 µm (all p<0.001). Global agreement was moderate (ICC=0.63; 95% CI 0.56 to 0.68). Deep learning-derived CT measurements showed much smaller inter-device differences (DREAM: 173.47±59.54 µm; Triton: 171.21±56.62 µm), yielding a mean global difference of 2.26 µm with limits of agreement from -17.38 to 21.90 µm. Sectoral CT differences were similarly minimised (ICCs>0.90 across all ETDRS regions). The deep learning model demonstrated significantly superior inter-device agreement (global ICC=0.98 vs 0.63; p<0.05).
Vendor-provided software produced systematic differences between DREAM OCT and Triton OCT, whereas the deep learning model produced CT measurements with significantly reduced variability. When evaluating CT measurements from different platforms, deep learning-based approaches may provide more comparable results than vendor-provided software.

PMID:
42728039
Bibliographic data and abstract were imported from PubMed on 12 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.