Comparison between choroidal thickness measurements from swept-source optical coherence tomography devices in highly myopic Chinese adults.
Authors
Affiliations (12)
Affiliations (12)
- Singapore Eye Research Institute, Singapore.
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore.
- Changsha Aier Eye Hospital, Changsha, Hunan, China.
- Aier Academy of Ophthalmology, Central South University, Changsha, Hunan, China.
- Duke-NUS Medical School, National University of Singapore, Singapore.
- Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria.
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore.
- Aier Hospital Group, Changsha, China.
- Department of Ophthamology, National University of Singapore, Singapore.
- Department of Ophthalmology, Columbia University, New York, New York, USA.
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
- Singapore Eye Research Institute, Singapore [email protected].
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.