Detection of subretinal and intraretinal fluids in optopol SD‐OCT using transfer learning

Abstract

Aims/Purpose: The detection of subretinal fluid (SRF) and intraretinal fluid (IRF) in optical coherence tomography (OCT) images plays a crucial role in the diagnosis and treatment of certain eye diseases. In this study, we investigated the performance of a Transfer Learning artificial intelligence (AI) technique, which we used to perform the model adaptation from the domain of Heidelberg OCT images to Optopol devices.Methods: A data set of 133 Optopol OCT images. 69 images indicating subretinal or intraretinal fluid and 64 fluid‐free images, were analysed. The AI algorithm that was initially trained on OCT images from Heidelberg device was fine‐tuned to perform the biomarker segmentation in Optopol (Revo NX, SOCT Copernicus REVO, REVO FC) OCT images. Manual assessments by the medical experts were used to evaluate the algorithm's performance.Results: Our findings demonstrate the promising performance of the domain‐adapted model. in detecting SRF and IRF, when initially trained on OCT images from a different device. The algorithm achieved an accuracy of 80% while classifying the presence versus absence of the fluids on the balanced test data set.Conclusions: The study demonstrates a successful application of a transfer learning technique for detection of subretinal and intraretinal fluids in OCT images acquired with the Optopol devices, indicating its potential for enhancing eye diagnostics. Further studies utilizing larger data sets, further biomarkers and diverse OCT devices are warranted to validate the algorithm's robustness and generalizability.