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David Lally, Judy E Kim, Michael J Elman, Oren Tomkins-Netzer, Yael Alon, Elad Bergman, Anat Loewenstein; Performance of a novel deep learning algorithm for Automatic Retinal Fluid Quantification in Home OCT Images. Invest. Ophthalmol. Vis. Sci. 2020;61(7):2571.
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© ARVO (1962-2015); The Authors (2016-present)
To evaluate the performance of a deep learning algorithm in identifying and quantifying retinal fluid in output of a patient self-operated Notal Home OCT (NOCT) device
Data were collected from 5 clinics where AMD patients self-imaged the central 10° of their maculae with the NOCT V2.5. 355 eyes from 239 subjects were enrolled, mean age 78 years (range: 54-92). From each cube scan (88 B-scans), avg. of 10 B-scans were selected for manual segmentation. Each B-scan was labeled pixel-wise into 4 compartments: Vitreous/outer layers (V/O), Retina (R), subretinal fluid (SRF), intraretinal fluid (IRF). Eyes were randomly split into learning and validation sets (ratio 8/1). Quantifier was developed using semantic segmentation with convolutional neural network. 1)Fluid quantification was evaluated by correlating each B-scan’s fluid area segmented by human vs machine. 2)Pixel-wise fluid was compared withrecall / precision. 3)Presence of fluid in B-scans were compared for accuracy.
3428 B-scans were manually segmented (75% with fluid). The learning set included 2936 B-scans of 311 eyes.The validation set included 492 B-scans of 44 eyes.The area of SRF and IRF from the automatic segmentation were compared to the human-defined segmented area: 1) Pearson correlation of fluid area was 0.98 (p<0.00001) for SRF and 0.90 for IRF (p<0.00001). 2) SRF Fluid pixel-wise recall was 0.72 and precision was 0.86; The IRF recall was 0.80 and the precision was 0.77. 3) Sensitivity for detecting presence of SRF in B-scans was 0.985 and specificity 0.98. Sensitivity for detecting IRF was 0.99 and specificity was 0.97.
This version of NOA performed comparably to human readers, utilizing deep learning for automatic fluid segmentation and quantification on self-obtained NOCT V2.5 images, as part of a home-based OCT fluid-evaluation system. By analyzing fluid status and its dynamics, and visualizing the locations of fluid, this system has the potential to accurately monitor AMD disease activity in a patient self-operated home-use environment.
This is a 2020 ARVO Annual Meeting abstract.
Figure 1, SegmentationResults: (a) Two B-scans from the validation set. (b,c) Manual and auto-segmentation of the B-scans. (d) Fluid thickness maps -fluid distribution for SRF and IRF extracted from the auto-segmentation. Blue arrow indicates the position of the displayed B-scans (a) in the map. Green circles with diameter of 1 and 3 mm.
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