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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>With vendor-provided software, DREAM OCT yielded thicker CT than Triton OCT (global ETDRS: 221.48\u00b166.88\u2009\u00b5m vs 156.90\u00b157.64\u2009\u00b5m), with mean differences of 61.04\u201371.22\u2009\u00b5m (all p&lt;0.001). Global agreement was moderate (ICC=0.63; 95%\u2009CI 0.56 to 0.68). Deep learning-derived CT measurements showed much smaller inter-device differences (DREAM: 173.47\u00b159.54\u2009\u00b5m; Triton: 171.21\u00b156.62\u2009\u00b5m), yielding a mean global difference of 2.26\u2009\u00b5m with limits of agreement from \u221217.38 to 21.90\u2009\u00b5m. Sectoral CT differences were similarly minimised (ICCs&gt;0.90 across all ETDRS regions). The deep learning model demonstrated significantly superior inter-device agreement (global ICC=0.98\u2009vs 0.63; p&lt;0.05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bjo-2026-329740","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T17:20:25Z","timestamp":1789147225000},"page":"bjo-2026-329740","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Comparison between choroidal thickness measurements from swept-source optical coherence tomography devices in highly myopic Chinese adults"],"prefix":"10.1136","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1000-6233","authenticated-orcid":false,"given":"Syna","family":"Sreng","sequence":"first","affiliation":[{"name":"Singapore Eye Research Institute, Singapore"},{"name":"SERI-NTU Advanced Ocular Engineering (STANCE) Program, 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