{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T05:40:25Z","timestamp":1785476425133,"version":"3.56.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>To conduct a systematic comparative analysis of macular retinal thickness, retinal nerve fiber layer (RNFL) thickness, and ganglion cell-inner plexiform layer (GCIPL) thickness measurements between spectral-domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) in healthy individuals, while establishing standardized cross-platform conversion algorithms through machine learning methodologies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this cross-sectional investigation, 48 healthy adults (96 eyes) underwent macular retinal thickness assessment (ETDRS grid sectors), RNFL analysis (quadrant sectors), and GCIPL evaluation (six-sector annular divisions) using both SD-OCT (Cirrus HD-OCT 5000) and SS-OCT (Triton DRI-OCT). Inter-device measurement differences were evaluated through paired t-tests. Agreement metrics were quantified via intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Four predictive models\u2014linear regression (LR), LASSO regression, random forest regression (RF), and support vector regression (SVR)\u2014were developed to estimate Triton DRI-OCT measurements from Cirrus HD-OCT 5000 outputs. Model efficacy was assessed using coefficient of determination (R<jats:sup>2<\/jats:sup>) and root mean square error (RMSE).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Statistically significant inter-device discrepancies (P &amp;lt; 0.001) were identified in 9 macular sectors, all GCIPL parameters (average and six-sector measurements), and RNFL measurements (average thickness and three quadrants, excluding nasal sector). ICC values demonstrated moderate-to-strong agreement: macular thickness (0.771\u20130.906), GCIPL (0.554\u20130.710), and RNFL (0.451\u20130.852). Machine learning models exhibited superior performance in central subfield thickness (CST) prediction, achieving test set R<jats:sup>2<\/jats:sup> values of 0.930 (LR), 0.926 (LASSO), 0.936 (SVR), and 0.892 (RF). Linear regression maintained consistent predictive accuracy across parameters: CST (R<jats:sup>2<\/jats:sup> = 0.930), RNFL (R<jats:sup>2<\/jats:sup> = 0.845), and GCIPL (R<jats:sup>2<\/jats:sup> = 0.760).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Substantial measurement discrepancies preclude direct interchangeability of SD-OCT and SS-OCT datasets. Machine learning-derived conversion algorithms significantly improve cross-device comparability, offering a robust standardization framework for multicenter research and longitudinal data integration. This methodological advancement enables harmonized analysis of OCT metrics across heterogeneous imaging platforms.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcell.2025.1612455","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T05:25:36Z","timestamp":1752729936000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Standardized conversion model for retinal thickness measurements between spectral-domain and swept-source optical coherence tomography based on machine learning"],"prefix":"10.3389","volume":"13","author":[{"given":"Zhongping","family":"Tian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinning","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifeng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhizhu","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1212\/WNL.0000000000012125","article-title":"APOSTEL 2.0 recommendations for reporting quantitative optical coherence tomography studies","volume":"97","author":"Aytulun","year":"2021","journal-title":"Neurology"},{"key":"B2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1167\/tvst.4.1.5","article-title":"Reproducibility of optovue RTVue optical coherence tomography retinal thickness measurements and conversion to equivalent zeiss stratus metrics in diabetic macular edema","volume":"4","author":"Bressler","year":"2015","journal-title":"Transl. 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