Structural Change Can Be Detected in Advanced-Glaucoma Eyes.

PurposeTo compare spectral-domain optical coherence tomography (SD-OCT) standard structural measures and a new three-dimensional (3D) volume optic nerve head (ONH) change detection method for detecting change over time in severely advanced-glaucoma (open-angle glaucoma [OAG]) patients.MethodsThirty-five eyes of 35 patients with very advanced glaucoma (defined as a visual field mean deviation < -21 dB) and 46 eyes of 30 healthy subjects to estimate aging changes were included. Circumpapillary retinal fiber layer thickness (cpRNFL), minimum rim width (MRW), and macular retinal ganglion cell-inner plexiform layer (GCIPL) thicknesses were measured using the San Diego Automated Layer Segmentation Algorithm (SALSA). Progression was defined as structural loss faster than 95th percentile of healthy eyes. Three-dimensional volume ONH change was estimated using the Bayesian-kernel detection scheme (BKDS), which does not require extensive retinal layer segmentation.ResultsThe number of progressing glaucoma eyes identified was highest for 3D volume BKDS (13, 37%), followed by GCPIL (11, 31%), cpRNFL (4, 11%), and MRW (2, 6%). In advanced-OAG eyes, only the mean rate of GCIPL change reached statistical significance, -0.18 μm/y (P = 0.02); the mean rates of cpRNFL and MRW change were not statistically different from zero. In healthy eyes, the mean rates of cpRNFL, MRW, and GCIPL change were significantly different from zero. (all P < 0.001).ConclusionsGanglion cell-inner plexiform layer and 3D volume BKDS show promise for identifying change in severely advanced glaucoma. These results suggest that structural change can be detected in very advanced disease. Longer follow-up is needed to determine whether changes identified are false positives or true progression

Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression.

Purpose:To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progression. Methods:Wide-angle SS-OCT, OCT circumpapillary retinal nerve fiber layer (cpRNFL) circle scans spectral-domain (SD)-OCT, standard automated perimetry (SAP), and frequency doubling technology (FDT) visual field tests were completed every 3 months for 2 years from a cohort of 28 healthy participants (56 eyes) and 93 glaucoma participants (179 eyes). RNFL thickness maps were extracted from segmented SS-OCT images and an unsupervised machine learning approach based on principal component analysis (PCA) was used to identify novel structural features. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy of RNFL PCA for detecting glaucoma and progression compared to SAP, FDT, and cpRNFL measures. Results:The RNFL PCA features were significantly associated with mean deviation (MD) in both SAP (R2 = 0.49, P < 0.0001) and FDT visual field testing (R2 = 0.48, P < 0.0001), and with mean circumpapillary RNFL thickness (cpRNFLt) from SD-OCT (R2 = 0.58, P < 0.0001). The identified features outperformed each of these measures in detecting glaucoma with an AUC of 0.95 for RNFL PCA compared to an 0.90 for mean cpRNFLt (P = 0.09), 0.86 for SAP MD (P = 0.034), and 0.83 for FDT MD (P = 0.021). Accuracy in predicting progression was also significantly higher for RNFL PCA compared to SAP MD, FDT MD, and mean cpRNFLt (P = 0.046, P = 0.007, and P = 0.044, respectively). Conclusions:A computational approach can identify structural features that improve glaucoma detection and progression prediction

Deep-Layer Microvasculature Dropout by Optical Coherence Tomography Angiography and Microstructure of Parapapillary Atrophy.

Purpose:To investigate the association between the microstructure of β-zone parapapillary atrophy (βPPA) and parapapillary deep-layer microvasculature dropout assessed by optical coherence tomography angiography (OCT-A). Methods:Thirty-seven eyes with βPPA devoid of the Bruch's membrane (BM) (γPPA) ranging between completely absent and discontinuous BM were matched by severity of the visual field (VF) damage with 37 eyes with fully intact BM (βPPA+BM) based on the spectral-domain (SD) OCT imaging. Parapapillary deep-layer microvasculature dropout was defined as a dropout of the microvasculature within choroid or scleral flange in the βPPA on the OCT-A. The widths of βPPA, γPPA, and βPPA+BM were measured on six radial SD-OCT images. Prevalence of the dropout was compared between eyes with and without γPPA. Logistic regression was performed for evaluating association of the dropout with the width of βPPA, γPPA, and βPPA+BM, and the γPPA presence. Results:Eyes with γPPA had significantly higher prevalence of the dropout than did those without γPPA (75.7% versus 40.8%; P = 0.004). In logistic regression, presence and longer width of the γPPA, worse VF mean deviation, and presence of focal lamina cribrosa defects were significantly associated with the dropout (P < 0.05), whereas width of the βPPA and βPPA+BM, axial length, and choroidal thickness were not (P > 0.10). Conclusions:Parapapillary deep-layer microvasculature dropout was associated with the presence and larger width of γPPA, but not with the βPPA+BM width. Presence and width of the exposed scleral flange, rather than the retinal pigmented epithelium atrophy, may be associated with deep-layer microvasculature dropout

Optical Coherence Tomography Angiography Vessel Density in Healthy, Glaucoma Suspect, and Glaucoma Eyes.

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

Macular Ganglion Cell Inner Plexiform Layer Thickness in Glaucomatous Eyes with Localized Retinal Nerve Fiber Layer Defects

Purpose: To investigate macular ganglion cell–inner plexiform layer (mGCIPL) thickness in glaucomatous eyes with visible localized retinal nerve fiber layer (RNFL) defects on stereophotographs. Methods: 112 healthy and 149 glaucomatous eyes from the Diagnostic Innovations in Glaucoma Study (DIGS) and the African Descent and Glaucoma Evaluation Study (ADAGES) subjects had standard automated perimetry (SAP), optical coherence tomography (OCT) imaging of the macula and optic nerve head, and stereoscopic optic disc photography. Masked observers identified localized RNFL defects by grading of stereophotographs. Result: 47 eyes had visible localized RNFL defects on stereophotographs. Eyes with visible localized RNFL defects had significantly thinner mGCIPL thickness compared to healthy eyes (68.3 ± 11.4 μm versus 79.2 ± 6.6 μm respectively, P<0.001) and similar mGCIPL thickness to glaucomatous eyes without localized RNFL defects (68.6 ± 11.2 μm, P = 1.000). The average mGCIPL thickness in eyes with RNFL defects was 14% less than similarly aged healthy controls. For 29 eyes with a visible RNFL defect in just one hemiretina (superior or inferior) mGCIPL was thinnest in the same hemiretina in 26 eyes (90%). Eyes with inferior-temporal RNFL defects also had significantly thinner inferior-temporal mGCIPL (P<0.001) and inferior mGCIPL (P = 0.030) compared to glaucomatous eyes without a visible RNFL defect. Conclusion: The current study indicates that presence of a localized RNFL defect is likely to indicate significant macular damage, particularly in the region of the macular that topographically corresponds to the location of the RNFL defect

Wide-Field Optical Coherence Tomography Imaging Improves Rate of Change Detection in Progressing Glaucomatous Eyes Compared With Standard-Field Imaging

PURPOSE: To compare rates of retinal nerve fiber layer change over time in healthy, eyes with nonprogressing glaucoma and eyes with progressing glaucoma using single wide-field (SWF) and optic nerve head (ONH) cube scan optical coherence tomography (OCT) images. METHODS: Forty-five eyes of 25 healthy individuals and 263 eyes of 161 glaucoma patients from the Diagnostic Innovations in Glaucoma Study were included. All eyes underwent 24-2 visual field testing and OCT (Spectralis SD-OCT) ONH and macular imaging. SWF images (up to 43° × 28°) were created by stitching together ONH cube scans centered on the optic disc and macular cube scans centered on the fovea. Visual field progression was defined as guided progression analysis likely progression and/or a significant (P \u3c 0.01) mean deviation slope of less than -1.0 dB/year. Mixed effects models were used to compare rates of change. Highly myopic eyes were included. RESULTS: Thirty glaucomatous eyes were classified as progressing. In eyes with glaucoma, mean global rate of change was -1.22 µm/year (P \u3c 0.001) using SWF images and -0.83 µm/year (P = 0.003) using ONH cube scans. Rate of change was significantly greater in eyes with progressing glaucoma compared with eyes with nonprogressing glaucoma (-1.51 µm/year vs. -1.24 µm/year; P = 0.002) using SWF images and was similar using ONH cube scans (P = 0.27). CONCLUSIONS: In this cohort that includes eyes with and without high axial myopia, the mean rate of retinal nerve fiber layer thinning measured using SWF images was faster in eyes with progressing glaucoma than in eyes with nonprogressing glaucoma. Wide-field OCT images including the ONH and macula can be effective for monitoring glaucomatous progression in patients with and without high myopia

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs

The ability of deep learning architectures to identify glaucomatous optic neuropathy (GON) in fundus photographs was evaluated. A large database of fundus photographs (n = 14,822) from a racially and ethnically diverse group of individuals (over 33% of African descent) was evaluated by expert reviewers and classified as GON or healthy. Several deep learning architectures and the impact of transfer learning were evaluated. The best performing model achieved an overall area under receiver operating characteristic (AUC) of 0.91 in distinguishing GON eyes from healthy eyes. It also achieved an AUC of 0.97 for identifying GON eyes with moderate-to-severe functional loss and 0.89 for GON eyes with mild functional loss. A sensitivity of 88% at a set 95% specificity was achieved in detecting moderate-to-severe GON. In all cases, transfer improved performance and reduced training time. Model visualizations indicate that these deep learning models relied on, in part, anatomical features in the inferior and superior regions of the optic disc, areas commonly used by clinicians to diagnose GON. The results suggest that deep learning-based assessment of fundus images could be useful in clinical decision support systems and in the automation of large-scale glaucoma detection and screening programs

Indexing of HSQC spectra and FMRI images for biomarker identification

Les techniques d'acquisition des signaux médicaux sont en constante évolution et fournissent une quantité croissante de données hétérogènes qui doivent être analysées par le médecin. Dans ce contexte, des méthodes automatiques de traitement des signaux médicaux sont régulièrement proposées pour aider l'expert dans l'analyse qualitative et quantitative en facilitant leur interprétation. Ces méthodes doivent tenir compte de la physique de l'acquisition, de l'a priori que nous avons sur ces signaux et de la quantité de données à analyser pour une interprétation plus précise et plus fiable. Dans cette thèse, l'analyse des tissus biologique par spectroscopie RMN et la recherche des activités fonctionnelles cérébrales et leurs connectivités par IRMf sont explorées pour la recherche de nouveaux bio-marqueurs. Chaque information médicale sera caractérisée par un ensemble d'objets que nous cherchons à extraire, à aligner, et à coder. Le regroupement de ces objets par la mesure de leur similitude permettra leur classification et l'identification de bio-marqueurs. C'est ce schéma global d'indexation et de recherche par le contenu d'objets pour la détection des bio-marqueurs que nous proposons. Pour cela, nous nous sommes intéressés dans cette thèse à modéliser et intégrer les connaissances a priori que nous avons sur ces signaux biologiques permettant ainsi de proposer des méthodes appropriées à chaque étape d'indexation et à chaque type de signal.The medical signal acquisition techniques are constantly evolving in recent years and providing an increasing amount of data which should be then analyzed. In this context, automatic signal processing methods are regularly proposed to assist the expert in the qualitative and quantitative analysis of these images in order to facilitate their interpretation. These methods should take into account the physics of signal acquisition, the a priori we have on the signal formation and the amount of data to analyze for a more accurate and reliable interpretation. In this thesis, we focus on the two-dimensional 2D Heteronuclear Single Quantum Coherence HSQC spectra obtained by High-Resolution Magic Angle Spinning HR-MAS NMR for biological tissue analysis and the functional Magnetic Resonance Imaging fMRI images for functional brain activities analysis. Each processed medical information will be characterized by a set of objects that we seek to extract, align, and code. The clustering of these objects by measuring their similarity will allow their classification and then the identification of biomarkers. It is this global content-based object indexing and retrieval scheme that we propose. We are interested in this thesis to properly model and integrate the a priori knowledge we have on these biological signal allowing us to propose there after appropriate methods to each indexing step and each type of signal