Deep learning assisted fluid volume calculation for assessing anti-vascular endothelial growth factor effect in diabetic macular edema

Objective: To develop an algorithm using deep learning methods to calculate the volume of intraretinal and subretinal fluid in optical coherence tomography (OCT) images for assessing diabetic macular edema (DME) patients’ condition changes. Design: Cross-sectional study. Participants: Treatment-naive patients diagnosed with DME recruited from April 2020 to November 2021. Methods: The deep learning network, which was built for autonomous segmentation utilizing an encoder-decoder network based on the U-Net architecture, was used to calculate the volume of intraretinal fluid (IRF) and subretinal fluid (SRF). The alterations of retinal vessel density and thickness, and the correlation between best-corrected visual acuity (BCVA) and OCT parameters were analyzed. Results: 2,955 OCT images of fourteen eyes from DME patients with IRF and SRF who received anti-vascular endothelial growth factor (VEGF) agents were obtained. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the algorithm was 0.993 for IRF and 0.998 for SRF. The volumes of IRF and SRF were significantly decreased from 1.93 ± 0.58 /1.14 ± 0.25 mm3 (baseline) to 0.26 ± 0.13 /0.26 ± 0.18 mm3 (post-injection), respectively (p = 0.0170 for IRF, and p = 0.0004 for SRF). The Spearman correlation demonstrated that the reduction of IRF volume was negatively correlated with age (coefficient = −0.698, p = 0.006). Conclusion: We developed a deep learning assisted fluid volume calculation algorithm with high sensitivity and specificity for assessing the volume of IRF and SRF in DME patients. Key words: deep learning; diabetic macular edema; optical coherence tomography.