ORIGINAL RESEARCH article
Front. Cell Dev. Biol.
Sec. Molecular and Cellular Pathology
Volume 13 - 2025 | doi: 10.3389/fcell.2025.1612455
This article is part of the Research TopicArtificial Intelligence Applications in Chronic Ocular Diseases, Volume IIView all 42 articles
Standardized Conversion Model for Retinal Thickness Measurements Between Spectral-Domain and Swept-Source Optical Coherence Tomography Based on Machine Learning
Provisionally accepted
- 1Tongji Hospital Affiliated to Tongji University, Shanghai, China
- 2Medical School, Tongji University, Shanghai, Shanghai Municipality, China
- 3School of Public Health, Zunyi Medical University, Zunyi, Guizhou Province, China
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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.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-linear regression (LR), LASSO regression, random forest regression (RF), and support vector regression (SVR)-were developed to estimate Triton DRI-OCT measurements from Cirrus HD-OCT 5000 outputs. Model efficacy was assessed using coefficient of determination (R²) and root mean square error (RMSE).Results: Statistically significant inter-device discrepancies (P<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 moderateto-strong agreement: macular thickness (0.771-0.906), GCIPL (0.554-0.710), and RNFL (0.451-0.852). Machine learning models exhibited superior performance in central subfield thickness (CST) prediction, achieving test set R² values of 0.930 (LR), 0.926 (LASSO), 0.936 (SVR), and 0.892 (RF). Linear regression maintained consistent predictive accuracy across parameters: CST (R²=0.930), RNFL (R²=0.845), and GCIPL (R²=0.760). Conclusion: Substantial measurement discrepancies preclude direct interchangeability of SD-OCT and SS-OCT datasets. Machine learning-derived conversion algorithms significantly improve crossdevice 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.
Keywords: Optical Coherence Tomography1, Machine Learning2, Retinal Thickness3, Standardized Conversion 4, Spectral-Domain 5, Swept-Source 6
Received: 15 Apr 2025; Accepted: 20 Jun 2025.
Copyright: © 2025 Tian, Guo, Zhou, Liu, Yi and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Li Zhang, Tongji Hospital Affiliated to Tongji University, Shanghai, China
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