Abstract
Introduction: To assess the diagnostic accuracy of automatically quantified macular fluid volume (MFV) for treatment-required diabetic macular edema (DME). Methods: This retrospective cross-sectional study included eyes with DME. The commercial software on optical coherence tomography (OCT) produced the central subfield thickness (CST), and a custom deep-learning algorithm automatically segmented the fluid cysts and quantified the MFV from the volumetric scans of an OCT angiography system. Retina specialists treated patients per standard of care based on clinical and OCT findings without access to the MFV. The main outcome measures were the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of the CST, MFV, and visual acuity (VA) for treatment indication. Results: Of 139 eyes, 39 (28%) were treated for DME during the study period and 101 (72%) were previously treated. The algorithm detected fluid in all eyes; however, only 54 eyes (39%) met the DRCR.net criteria for center-involved ME. The AUROC of MFV predicting a treatment decision of 0.81 was greater than that of CST (0.67) (P = .0048). Untreated eyes that met the optimal threshold for treatment-required DME based on MFV (>0.031 mm(3)) had better VA than treated eyes (P = .0053). A multivariate logistic regression model showed that MFV (P = .0008) and VA (P = .0061) were significantly associated with a treatment decision, but CST was not. Conclusions: MFV had a higher correlation with the need for treatment for DME than CST and may be especially useful for ongoing management of DME.