Diagnostics
Deep Learning-Based Automated Detection, Segmentation, and Quantification of Retinal Fluid on Optical Coherence Tomography: A Validation Study
By Nasiq Hasan, Adarsh Gadari, Sharat Chandra Vupparaboina and 12 others.
Abstract
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).