Deep Learning-Based Automated Detection, Segmentation, and Quantification of Retinal Fluid on Optical Coherence Tomography: A Validation Study

Objectives: To validate a deep learning-based algorithm for automated detection, segmentation, and quantitative assessment of retinal fluid on optical coherence tomography (OCT) across multiple retinal diseases. Methods: In this retrospective validation study, spectral-domain OCT volume scans from eyes with neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), and macular telangiectasia type 2 (MacTel) were analyzed. Automated segmentation was performed using the fluid analyzer on Skopix by NetraMind Innovations, a deep learning-based framework trained to identify intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), and subretinal hyperreflective material (SHRM). Automated segmentations were reviewed and manually corrected by two masked experienced retina graders, with discrepancies adjudicated by a senior retina specialist. Scan-level detection performance was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC), and Gwet’s first-order agreement coefficient (AC1). Spatial segmentation agreement across all B-scans was assessed using the Dice coefficient. Quantitative agreement between automated and manually corrected fluid volumes was evaluated using correlation and Bland–Altman analyses. Inter-grader reliability was assessed using the intraclass correlation coefficient (ICC). Results: A total of 98 OCT volumes comprising 31 nAMD, 25 DME, and 42 MacTel eyes were included. In nAMD, IRF detection demonstrated a sensitivity of 81.8%, specificity of 83.0%, and AUC of 0.876, while SRF demonstrated a sensitivity of 81.4%, specificity of 96.7%, and AUC of 0.901. In DME, sensitivity, specificity, and AUC were 99.6%, 97.3%, and 0.989 for IRF and 98.4%, 96.4%, and 0.978 for SRF, respectively. In MacTel, IRF demonstrated a sensitivity of 87.3%, specificity of 95.1%, and AUC of 0.927, while SRF demonstrated a sensitivity of 86.8%, specificity of 98.4%, and AUC of 0.929. Segmentation agreement between automated and manually corrected annotations was high across disease groups, with Dice coefficients of 0.829 and 0.922 for IRF and SRF in nAMD, 0.858 and 0.941 in DME, and 0.908 and 0.97 in MacTel, respectively. Automated fluid volumes demonstrated strong correlations with manually corrected measurements, with minimal systematic bias on Bland–Altman analysis. Conclusions: The automated segmentation algorithm demonstrated strong validation performance for the detection and quantitative assessment of retinal fluid across nAMD, DME, and MacTel. High segmentation agreement, particularly for IRF and SRF, and strong volumetric agreement with expert-corrected measurements support the potential use of automated OCT fluid quantification in clinical practice and research.