… Specifically, we applied deep learning, a state-of-the-art machine learning technique in the
field of … Deep learning models allow one to learn meaningful abstract data representations. …

… In addition, one of the known issues of deep learning methods is its instability and … on
supervised deep-learning to detect, quantify, and localize the amount of pathological fluid in the SD…

… or fluid height measurements, this study provides a systematic assessment of CSFT and
subretinal fluid (SRF) height values compared with deep learning–based fluid quantification to …

… We analyzed the change in macular fluid to validate this finding. However, no significant
difference in the fluctuations in macular fluid was seen between our two groups. This is likely …

… fluid (IRF), subretinal fluid (SRF), and serous pigment epithelium detachments (PEDs)
using a novel deep learning-based, macular fluid … features and fluid measurements were …

… In this post hoc analysis of a randomized clinical trial, we used a deep learning algorithm to
quantify IRF and SRF and analyzed the association of treatment with fluid volume and BCVA. …

… The estimated fluid volumes were subsequently used to determine the … deep learning model
based on convolutional neural network showed good performance not only in terms of fluid …

… macular fluid presence as a qualitative OCT parameter in their re-treatment protocols (in the
pro re nata arms); this involved the manual detection of IRF or SRF from macular … retinal fluid …

… with deep learning–based fluid detection at the levels of the red lines in images (a) and (d).
Automatically detected intraretinal fluid is in orange-yellow colour and subretinal fluid in blue. …

… can improve the semantic segmentation accuracy and efficiency of macular change analysis,
… different deep learning paradigms reported in the up-to-date literature for the retinal fluid …