PurposeThe purpose of this study was to compare retinal nerve fiber layer (RNFL) thickness and optical coherence tomography angiography (OCT-A) retinal vasculature measurements in healthy, glaucoma suspect, and glaucoma patients.MethodsTwo hundred sixty-one eyes of 164 healthy, glaucoma suspect, and open-angle glaucoma (OAG) participants from the Diagnostic Innovations in Glaucoma Study with good quality OCT-A images were included. Retinal vasculature information was summarized as a vessel density map and as vessel density (%), which is the proportion of flowing vessel area over the total area evaluated. Two vessel density measurements extracted from the RNFL were analyzed: (1) circumpapillary vessel density (cpVD) measured in a 750-μm-wide elliptical annulus around the disc and (2) whole image vessel density (wiVD) measured over the entire image. Areas under the receiver operating characteristic curves (AUROC) were used to evaluate diagnostic accuracy.ResultsAge-adjusted mean vessel density was significantly lower in OAG eyes compared with glaucoma suspects and healthy eyes. (cpVD: 55.1 ± 7%, 60.3 ± 5%, and 64.2 ± 3%, respectively; P < 0.001; and wiVD: 46.2 ± 6%, 51.3 ± 5%, and 56.6 ± 3%, respectively; P < 0.001). For differentiating between glaucoma and healthy eyes, the age-adjusted AUROC was highest for wiVD (0.94), followed by RNFL thickness (0.92) and cpVD (0.83). The AUROCs for differentiating between healthy and glaucoma suspect eyes were highest for wiVD (0.70), followed by cpVD (0.65) and RNFL thickness (0.65).ConclusionsOptical coherence tomography angiography vessel density had similar diagnostic accuracy to RNFL thickness measurements for differentiating between healthy and glaucoma eyes. These results suggest that OCT-A measurements reflect damage to tissues relevant to the pathophysiology of OAG.

Purpose To evaluate the association between vessel density measurements using optical coherence tomography angiography (OCT-A) and severity of visual field loss in primary open-angle glaucoma (POAG) Design Observational cross-sectional study Participants One hundred and fifty three eyes from 31 healthy, 48 glaucoma suspects, and 74 glaucoma participants enrolled in the Diagnostic Innovations in Glaucoma Study Methods All eyes underwent imaging using an OCT-A (Angiovue, Optovue; Fremont, CA) and a spectral domain OCT (Avanti, Optovue; Fremont, CA), along with standard automated perimetry (SAP). Retinal vasculature information was summarized as vessel density, the percent of area occupied by flowing blood vessels in the selected region. Two measurements from the retinal nerve fiber layer (RNFL) were utilized: circumpapillary vessel density (cpVD) (750-μm-wide elliptical annulus around the optic disc); and whole image vessel density (wiVD) (entire 4.5 × 4.5 mm scan field) Main Outcome Measures Associations between severity of visual field loss, reported as SAP mean deviation (MD) and OCT-A vessel density Results Compared to POAG eyes, normal eyes demonstrated a denser microvascular network within the RNFL. Vessel density was higher in normal eyes followed by glaucoma suspects, mild glaucoma and moderate to severe glaucoma eyes for wiVD (55.5, 51.3, 48.3, 41.7% respectively) and for cpVD (62.8, 61.0, 57.5, 49.6% respectively) (P<0.001 for both). The association between the severity of visual field damage (MD) with cpVD and wiVD was stronger (R2=0.54, and R2=0.51 respectively) than the association between visual field MD and RNFL (R2=0.36) and rim area (R2=0.19) (P<0.05 for all). Multivariate regression analysis, adjusted for confounders, showed that each 1% decrease in cpVD was associated with 0.64 dB loss in MD and each 1% decrease in wiVD, was associated with 0.66 dB loss in MD. In addition, the association between vessel density and the severity of visual field damage was found to be significant even after controlling for the effect of structural loss Conclusions Decreased vessel density was significantly associated with severity of visual field damage independent of the structural loss. OCT-A is a promising technology in glaucoma management, potentially enhancing the understanding of vascular role in the pathophysiology of the disease

In this article, we present a survey of recent advances in passive human behaviour recognition in indoor areas using the channel state information (CSI) of commercial WiFi systems. Movement of human body causes a change in the wireless signal reflections, which results in variations in the CSI. By analyzing the data streams of CSIs for different activities and comparing them against stored models, human behaviour can be recognized. This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers. The techniques from the literature that are presented herein have great performances, however, instead of the machine learning techniques employed in these works, we propose to use deep learning techniques such as long-short term memory (LSTM) recurrent neural network (RNN), and show the improved performance. We also discuss about different challenges such as environment change, frame rate selection, and multi-user scenario, and suggest possible directions for future work.

Purpose To investigate whether vessel density assessed by optical coherence tomography angiography (OCT-A) is reduced in glaucomatous eyes with focal lamina cribrosa (LC) defects. Design Cross-sectional case-control study. Participants Eighty-two primary open angle glaucoma (POAG) patients from the Diagnostic Innovations in Glaucoma Study (DIGS) with and without focal LC defects (41 eyes of 41 patients in each group) matched by severity of visual field (VF) damage. Methods OCT-A-derived circumpapillary vessel density (cpVD) was calculated as the percentage area occupied by vessels in the measured region extracted from the retinal nerve fiber layer (RNFL) in a 750-μm-wide elliptical annulus around the disc. Focal LC defects were detected using swept-source OCT images. Main Outcome Measures Comparison of global and sectoral (eight 45 degree sectors) cpVDs and circumpapillary retinal nerve fiber layer (cpRNFL) thicknesses in eyes with and without LC defects. Results Age, global and sectoral cpRNFL thicknesses, visual field mean deviation and pattern standard deviation, presence of the optic disc hemorrhage, and mean ocular perfusion pressure did not differ between the patients with and without LC defects (P > 0.05, for all comparisons). Mean cpVDs of eyes with LC defects were significantly lower than those without a defect globally (52.9 ± 5.6 vs. 56.8 ± 7.7 %, P = 0.013), and in the inferotemporal (IT) (49.5 ± 10.3 vs. 56.8 ± 12.2 %, P = 0.004), superotemporal (ST) (54.3 ± 8.8 vs. 58.8 ± 9.6 %, P = 0.030), and inferonasal (IN) (52.4 ± 9.0 vs. 57.6 ± 9.1 %, P = 0.009) sectors. Eyes with LC defects in the IT sector (n = 33) had significantly lower cpVDs than those without a defect in the corresponding IT and IN sectors (P < 0.05, respectively). Eyes with LC defects in the ST sector (n = 19) had lower cpVDs in ST, IT, and IN sectors (P < 0.05, respectively). Conclusions In eyes with similar severity of glaucoma, OCT-A-measured vessel density was significantly lower in POAG eyes with focal LC defects than those without a LC defect. Moreover, reduction of vessel density was spatially correlated with the location of the LC defect.

We developed an unsupervised machine learning algorithm and applied it to big corneal parameters to identify and monitor keratoconus stages. A big dataset of corneal swept source optical coherence tomography (OCT) images of 12,242 eyes acquired from SS-1000 CASIA OCT Imaging Systems in multiple centers across Japan was assembled. A total of 3,156 eyes with valid Ectasia Status Index (ESI) between zero and 100% were selected for the downstream analysis. Four hundred and twenty corneal topography, elevation, and pachymetry parameters (excluding ESI Keratoconus indices) were selected. The algorithm included three major steps. 1) Principal component analysis (PCA) was used to linearly reduce the dimensionality of the input data from 420 to eight significant principal components. 2) Manifold learning was used to further reducing the selected principal components nonlinearly to two eigen-parameters. 3) Finally, a density-based clustering was applied to the eigen-parameters to identify eyes with keratoconus. Visualization of clusters in 2-D space was used to validate the quality of learning subjectively and ESI was used to assess the accuracy of the identified clusters objectively. The proposed method identified four clusters; I: a cluster composed of mostly normal eyes (224 eyes with ESI equal to zero, 23 eyes with ESI between five and 29, and nine eyes with ESI greater than 29), II: a cluster composed of mostly healthy eyes and eyes with forme fruste keratoconus (1772 eyes with ESI equal to zero, 698 eyes with ESI between five and 29, and 117 eyes with ESI greater than 29), III: a cluster composed of mostly eyes with mild keratoconus stage (184 eyes with ESI greater than 29, 74 eyes with ESI between five and 29, and 6 eyes with ESI equal to zero), and IV: a cluster composed of eyes with mostly advanced keratoconus stage (80 eyes had ESI greater than 29 and 1 eye had ESI between five and 29). We found that keratoconus status and severity can be well identified using unsupervised machine learning algorithms along with linear and non-linear corneal data transformation. The proposed method can better identify and visualize the keratoconus stages.

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Machine learning classifiers were employed to detect glaucomatous progression using longitudinal series of structural data extracted from retinal nerve fiber layer thickness measurements and visual functional data recorded from standard automated perimetry tests. Using the collected data, a longitudinal feature vector was created for each patient’s eye by computing the norm 1 difference vector of the data at the baseline and at each follow-up visit. The longitudinal features from each patient’s eye were then fed to the machine learning classifier to classify each eye as stable or progressed over time. This study was performed using several machine learning classifiers including Bayesian, Lazy, Meta, and Tree, composing different families. Combinations of structural and functional features were selected and ranked to determine the relative effectiveness of each feature. Finally, the outcomes of the classifiers were assessed by several performance metrics and the effectiveness of structural and functional features were analyzed.