The British journal of ophthalmology · 2026
Comparison between choroidal thickness measurements from swept-source optical coherence tomography devices in highly myopic Chinese adults.
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Sreng Syna, Tan Huanhuan, Cheong Kai Xiong, Jiang Yanfeng, Ng Yvonne, Bellemo Valentina, Tang Xiangzhong, Chen Zhongping, Lamoureux Ecosse Luc, Schmetterer Leopold, Hoang Quan V, Saw Seang-Mei, Wong Damon, Lan Weizhong
Аннотация
BACKGROUND/AIMS: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.