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To identify patterns of visual field (VF) loss based on unsupervised machine learning and to identify patterns that are associated with rapid progression. Cross-sectional and longitudinal study. A total of 2231 abnormal VFs from 205 eyes of 176 Ocular Hypertension Treatment Study (OHTS) participants followed over approximately 16 years. Visual fields were assessed by an unsupervised deep archetypal analysis algorithm and an OHTS-certified VF reader to identify prevalent patterns of VF loss. Machine-identified patterns of glaucoma damage were compared against those patterns previously identified (expert-identified) in the OHTS in 2003. Based on the longitudinal VFs of each eye, VF loss patterns that were strongly associated with rapid glaucoma progression were identified. Machine-expert correspondence and type of patterns of VF loss associated with rapid progression. The average VF mean deviation (MD) at conversion to glaucoma was −2.7 decibels (dB) (standard deviation [SD] = 2.4 dB), whereas the average MD of the eyes at the last visit was −5.2 dB (SD = 5.5 dB). Fifty out of 205 eyes had MD rate of −1 dB/year or worse and were considered rapid progressors. Eighteen machine-identified patterns of VF loss were compared with expert-identified patterns, in which 13 patterns of VF loss were similar. The most prevalent expert-identified patterns included partial arcuate, paracentral, and nasal step defects, and the most prevalent machine-identified patterns included temporal wedge, partial arcuate, nasal step, and paracentral VF defects. One of the machine-identified patterns of VF loss predicted future rapid VF progression after adjustment for age, sex, and initial MD. An automated machine learning system can identify patterns of VF loss and could provide objective and reproducible nomenclature for characterizing early signs of visual defects and rapid progression in patients with glaucoma. [ABSTRACT FROM AUTHOR]

To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss. Retrospective, cross-sectional, longitudinal cohort study. Of 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models. We entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a "pipeline" that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an "AI-enabled glaucoma dashboard." We used density-based clustering and the VF decomposition method called "archetypal analysis" to annotate the dashboard. Finally, we used 2 separate benchmark datasets—one representing "likely nonprogression" and the other representing "likely progression"—to validate the dashboard and assess its ability to portray functional change over time in glaucoma. The severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma. After building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting "likely nonprogression" was 94% and the sensitivity for detecting "likely progression" was 77%. The AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article, we present a survey of recent advances in passive human behavior recognition in indoor areas using the channel state information (CSI) of commercial WiFi systems. The movement of the human body parts cause changes in the wireless signal reflections, which result in variations in the CSI. By analyzing the data streams of CSIs for different activities and comparing them against stored models, human behavior 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 performance; 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 networking (RNN) and show the improved performance. We also discuss different challenges such as environment change, frame rate selection, and the multi-user scenario; and finally suggest possible directions for future work. [ABSTRACT FROM AUTHOR]

Localization and tracking of a mobile node (MN) in non-line-of-sight (NLOS) scenarios, based on time-of-arrival (TOA) measurements, is considered in this paper. We develop a constrained form of a square-root unscented Kalman filter (SRUKF), where the sigma points of the unscented transformation are projected onto the feasible region by solving constrained optimization problems. The feasible region is the intersection of several disks formed by the NLOS measurements. We show how we can reduce the size of the optimization problem and formulate it as a convex quadratically constrained quadratic program, which depends on the Cholesky factor of the a posteriori error covariance matrix of the SRUKF. As a result of these modifications, the proposed constrained SRUKF (CSRUKF) is more efficient and has better numerical stability compared to the constrained unscented Kalman filter (UKF). Through simulations, we also show that the CSRUKF achieves a smaller localization error compared to other techniques and that its performance is robust under different NLOS conditions. [ABSTRACT FROM PUBLISHER]

This paper presents a gradient-based optimization approach to achieve reduction of blocking artifacts in compressed JPEG images. This approach involves decomposing a JPEG image into 1-D signals once along the rows or columns and once along the columns or rows. The reduction of blocking artifacts is carried out per 1-D signal by an optimization formulation where the gradient of an original 1-D signal is approximated based on the gradient of a compressed signal. A fixed-weight and an adaptive-weight optimization formulation are considered and solved analytically. A restored image is reconstructed by aggregating recovered 1-D signals. The performance of the developed method is assessed by examining both gray-level and color images and by computing the three measures of PSNR, SSIM, and GBIM. Comparison results with five existing methods are also reported. [ABSTRACT FROM AUTHOR]

This paper presents an optimization method to reduce blocking artifacts in JPEG images by utilizing the image gradient information. A closed-form solution is derived for the optimization method. To address the computational feasibility aspect of the large matrices involved in the closed-form solution, a sliding window approach is devised. The performance of the developed method is compared with several blocking artifacts reduction methods in the literature and also with the deblocking filter deployed in high efficiency video coding by examining the three measures of peak signal-to-noise ratio, generalized block-edge impairment metric (MGBIM), and structural similarity. The comparison results indicate the effectiveness of the introduced method in particular for low bit-rate JPEG images. [ABSTRACT FROM AUTHOR]

A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection testing. First, cross-sectional visual field tests collected from each subject were clustered using a mixture of Gaussians and model parameters were estimated using expectation maximization. The visual field clusters were further estimated to recognize glaucomatous visual field defect patterns by decomposing each cluster into several axes. The glaucoma visual field defect patterns along each axis then were identified. To derive a definition of progression, the longitudinal visual fields of stable glaucoma eyes on the abnormal cluster axes were projected and the slope was approximated using linear regression (LR) to determine the confidence limit of each axis. For glaucoma progression detection, the longitudinal visual fields of each eye on the abnormal cluster axes were projected and the slope was approximated by LR. Progression was assigned if the progression rate was greater than the boundary limit of the stable eyes; otherwise, stability was assumed. The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software. The clinical accuracy of the proposed pipeline was as good as or better than the currently available methods. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

In this paper, the properties of a new class of causal Markov random fields, named symmetric Markov mesh random field, are initially discussed. It is shown that the symmetric Markov mesh random fields from the upper corners are equivalent to the symmetric Markov mesh random fields from the lower corners. Based on this new random field, a symmetric, corner-independent, and isotropic image model is then derived which incorporates the dependency of a pixel on all its neighbors. The introduced image model comprises the product of several local 1D density and 2D joint density functions of pixels in an image thus making it computationally tractable and practically feasible by allowing the use of histogram and joint histogram approximations to estimate the model parameters. An image restoration application is also presented to confirm the effectiveness of the model developed. The experimental results demonstrate that this new model provides an improved tool for image modeling purposes compared to the conventional Markov random field models. [ABSTRACT FROM AUTHOR]

Precise labeling of subcortical structures plays a key role in functional neurosurgical applications. Labels from an atlas image are propagated to a patient image using atlas-based segmentation. Atlas-based segmentation is highly dependent on the registration framework used to guide the atlas label propagation. This paper focuses on atlas-based segmentation of subcortical brain structures and the effect of different registration methods on the generated subcortical labels. A single-step and three two-step registration methods appearing in the literature based on affine and deformable registration algorithms in the ANTS and FSL algorithms are considered. Experiments are carried out with two atlas databases of IBSR and LPBA40. Six segmentation metrics consisting of Dice overlap, relative volume error, false positive, false negative, surface distance, and spatial extent are used for evaluation. Segmentation results are reported individually and as averages for nine subcortical brain structures. Based on two statistical tests, the results are ranked. In general, among four different registration strategies investigated in this paper, a two-step registration consisting of an initial affine registration followed by a deformable registration applied to subcortical structures provides superior segmentation outcomes. This method can be used to provide an improved labeling of the subcortical brain structures in MRIs for different applications. [ABSTRACT FROM PUBLISHER]

Objectives: The purpose of this study was to examine the factors associated with vision impairment (VI), age-related eye disease (ARED), and frequency of eye examinations among older adults. Methods: A cross-sectional study (N = 166) was designed to identify barriers in vision and eye care services among adults 50 years and older in four counties in Appalachian Tennessee. Surveys were administered in March 2023. Simple and multiple logistic regression analyses were used to determine the risk factors of VI and ARED and the frequency of eye examinations. Results: In two out of the three regression models, predictors such as traveling ≥10 mi to an eye care provider, barriers to eye care, and a lack of exposure to eye health information emerged as significant factors. Individuals who traveled ≥10 mi to an eye care provider were more than twice as likely than individuals who traveled shorter distances to have VI and not maintain routine eye care (adjusted odds ratio [AOR] 2.69, 95% confidence interval [CI] 1.08-6.75; AOR 2.82, 95% CI 1.05-7.55, respectively). Reporting barriers to eye care doubled the odds of ARED (AOR 2.33, 95% CI 1.02-5.34) and substantially increased the odds of reporting a 3-year or longer interval since the last eye examination (AOR 7.45, 95% CI 1.85-29.96) compared with having no barriers to eye care. Moreover, limited exposure to eye health information tripled the odds of VI (AOR 3.26, 95% CI 1.15-9.24) and not maintaining routine eye care (AOR 3.07, 95% CI 0.97-9.70) compared with more exposure to eye health information. Other predictors also were uncovered in the analysis. Conclusions: This study contributes to the scarce literature on risk factors associated with vision health among older adults in Appalachia. [ABSTRACT FROM AUTHOR]

The estimation of central choroidal thickness from colour fundus images can improve disease detection. We developed a deep learning method to estimate central choroidal thickness from colour fundus images at a single institution, using independent datasets from other institutions for validation. A total of 2,548 images from patients who underwent same-day optical coherence tomography examination and colour fundus imaging at the outpatient clinic of Jichi Medical University Hospital were retrospectively analysed. For validation, 393 images from three institutions were used. Patients with signs of subretinal haemorrhage, central serous detachment, retinal pigment epithelial detachment, and/or macular oedema were excluded. All other fundus photographs with a visible pigment epithelium were included. The main outcome measure was the standard deviation of 10-fold cross-validation. Validation was performed using the original algorithm and the algorithm after learning based on images from all institutions. The standard deviation of 10-fold cross-validation was 73 μm. The standard deviation for other institutions was reduced by re-learning. We describe the first application and validation of a deep learning approach for the estimation of central choroidal thickness from fundus images. This algorithm is expected to help graders judge choroidal thickening and thinning. [ABSTRACT FROM AUTHOR]

Purpose: To provide a comparative analysis of rates of laser trabeculoplasty (LTP) among eye care providers in the USA. Methods: This retrospective cohort analysis utilized the Centers for Medicare and Medicaid Services (CMS) Public Use File (PUF), 2015– 2018. We used CPT code 65855 to select eye care providers who performed LTP in three key US states (KY, LA, and OK). Primary outcomes were eye provider differences in provider count, service count, unique beneficiary count, and Medicare-allowed payments. Asymptotic two-sided chi-squared tests were executed. Statistical significance was achieved at p< 0.05. Results: The sum of Medicare-allowed payments for LTP in all three states in 2018 was roughly 26% lower than in 2015. The proportion of Medicare-allowed payments furnished by optometrists increased from 11.3% to 17.9% between 2015 and 2018 (p< 0.001). Relative to ophthalmologists, we observed significant increases in optometric Medicare-allowed payments in KY, LA, OK, and the all-inclusive tri-state cohort (all p< 0.001). Furthermore, significant optometric increases in number of providers performing LTP (p=0.007), number of unique Medicare beneficiaries seen (p< 0.001), and number of LTP services billed (p< 0.001) were observed relative to ophthalmologists. Conclusion: The recent expansion of surgical authority by optometrists in key US states is creating a tangible impact on ophthalmologic and optometric practice patterns. The findings of this study may act as provision for policymakers in the context of continually evolving guidelines for optometric surgical expansion. [ABSTRACT FROM AUTHOR]

This document is a response to a comment made about an article titled "The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports." The comment pointed out that the statement in the article about ChatGPT learning from previous interactions was not accurate. The response clarifies that ChatGPT, developed by OpenAI, has been enhanced with the capability of Reinforcement Learning from Human Feedback (RLHF) and the ability to remember details from previous conversations. However, in the context of medical diagnosis, the authors of the response emphasize the importance of evaluating each case independently and initiating each case in a new session to minimize the influence of ChatGPT's capabilities and maintain an unbiased perspective. They also highlight the need to balance innovation with ethical and practical considerations in healthcare. [Extracted from the article]

Infectious keratitis (IK) is a major cause of corneal opacity. IK can be caused by a variety of microorganisms. Typically, fungal ulcers carry the worst prognosis. Fungal cases can be subdivided into filamentous and yeasts, which shows fundamental differences. Delays in diagnosis or initiation of treatment increase the risk of ocular complications. Currently, the diagnosis of IK is mainly based on slit-lamp examination and corneal scrapings. Notably, these diagnostic methods have their drawbacks, including experience-dependency, tissue damage, and time consumption. Artificial intelligence (AI) is designed to mimic and enhance human decision-making. An increasing number of studies have utilized AI in the diagnosis of IK. In this paper, we propose to use AI to diagnose IK (model 1), differentiate between bacterial keratitis and fungal keratitis (model 2), and discriminate the filamentous type from the yeast type of fungal cases (model 3). Overall, 9329 slit-lamp photographs gathered from 977 patients were enrolled in the study. The models exhibited remarkable accuracy, with model 1 achieving 99.3%, model 2 at 84%, and model 3 reaching 77.5%. In conclusion, our study offers valuable support in the early identification of potential fungal and bacterial keratitis cases and helps enable timely management. [ABSTRACT FROM AUTHOR]

Introduction: The purpose of this study was to evaluate the capabilities of large language models such as Chat Generative Pretrained Transformer (ChatGPT) to diagnose glaucoma based on specific clinical case descriptions with comparison to the performance of senior ophthalmology resident trainees. Methods: We selected 11 cases with primary and secondary glaucoma from a publicly accessible online database of case reports. A total of four cases had primary glaucoma including open-angle, juvenile, normal-tension, and angle-closure glaucoma, while seven cases had secondary glaucoma including pseudo-exfoliation, pigment dispersion glaucoma, glaucomatocyclitic crisis, aphakic, neovascular, aqueous misdirection, and inflammatory glaucoma. We input the text of each case detail into ChatGPT and asked for provisional and differential diagnoses. We then presented the details of 11 cases to three senior ophthalmology residents and recorded their provisional and differential diagnoses. We finally evaluated the responses based on the correct diagnoses and evaluated agreements. Results: The provisional diagnosis based on ChatGPT was correct in eight out of 11 (72.7%) cases and three ophthalmology residents were correct in six (54.5%), eight (72.7%), and eight (72.7%) cases, respectively. The agreement between ChatGPT and the first, second, and third ophthalmology residents were 9, 7, and 7, respectively. Conclusions: The accuracy of ChatGPT in diagnosing patients with primary and secondary glaucoma, using specific case examples, was similar or better than senior ophthalmology residents. With further development, ChatGPT may have the potential to be used in clinical care settings, such as primary care offices, for triaging and in eye care clinical practices to provide objective and quick diagnoses of patients with glaucoma. [ABSTRACT FROM AUTHOR]

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91–0.92 and an accuracy range of 88–92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the effect of bupivacaine with dexmedetomidine in comparison with bupivacaine during supraclavicular block with ultrasound guide on the hemodynamics of patients undergoing upper limb orthopedic surgery. Eighty patients (40 patients in each group) who were candidates for upper limb orthopedic surgery randomly received 30 ml of bupivacaine alone (group 1) or 30 ml of bupivacaine with 20 μg of dexmedetomidine (group 2). The supraclavicular nerve block was performed using an ultrasound guide. Patients' hemodynamic data (including mean arterial blood pressure, heart rate per minute, respiration rate per minute, and peripheral blood oxygen saturation), the onset of action, and duration of sensory-motor block were compared between the two groups. The mean arterial blood pressure during surgery in group 2 was lower than in group 1, but the differences were not statistically significant. The onset of sensory and motor block in group 2 was significantly shorter than in group 1 (P=0.0001). The duration of sensory and motor block in group 2 was significantly longer than in group 1 (P=0.0001). During this study, none of the patients had hemodynamic disturbance or surgical complications. The addition of dexmedetomidine to bupivacaine during the supraclavicular block, in addition to hemodynamic stability of the patient during surgery, increases the duration of sensory and motor block. [ABSTRACT FROM AUTHOR]

Motivation To develop and assess the accuracy of deep learning models that identify different retinal cell types, as well as different retinal ganglion cell (RGC) subtypes, based on patterns of single-cell RNA sequencing (scRNA-seq) in multiple datasets. Results Deep domain adaptation models were developed and tested using three different datasets. The first dataset included 44 808 single retinal cells from mice (39 cell types) with 24 658 genes, the second dataset included 6225 single RGCs from mice (41 subtypes) with 13 616 genes and the third dataset included 35 699 single RGCs from mice (45 subtypes) with 18 222 genes. We used four loss functions in the learning process to align the source and target distributions, reduce misclassification errors and maximize robustness. Models were evaluated based on classification accuracy and confusion matrix. The accuracy of the model for correctly classifying 39 different retinal cell types in the first dataset was ∼92%. Accuracy in the second and third datasets reached ∼97% and 97% in correctly classifying 40 and 45 different RGCs subtypes, respectively. Across a range of seven different batches in the first dataset, the accuracy of the lead model ranged from 74% to nearly 100%. The lead model provided high accuracy in identifying retinal cell types and RGC subtypes based on scRNA-seq data. The performance was reasonable based on data from different batches as well. The validated model could be readily applied to scRNA-seq data to identify different retinal cell types and subtypes. Availability and implementation The code and datasets are available on https://github.com/DM2LL/Detecting-Retinal-Cell-Classes-and-Ganglion-Cell-Subtypes. We have also added the class labels of all samples to the datasets. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Abstract: The use of information theoretic measures (ITMs) has been steadily growing in image processing, bioinformatics, and pattern classification. Although the ITMs have been extensively used in rigid and affine registration of multi-modal images, their computation and accuracy are critical issues in deformable image registration. Three important aspects of using ITMs in multi-modal deformable image registration are considered in this paper: computation, inverse consistency, and accuracy; a symmetric formulation of the deformable image registration problem through the computation of derivatives and resampling on both source and target images, and sufficient criteria for inverse consistency are presented for the purpose of achieving more accurate registration. The techniques of estimating ITMs are examined and analytical derivatives are derived for carrying out the optimization in a computationally efficient manner. ITMs based on Shannon’s and Renyi’s definitions are considered and compared. The obtained evaluation results via registration functions, and controlled deformable registration of multi-modal digital brain phantom and in vivo magnetic resonance brain images show the improved accuracy and efficiency of the developed formulation. The results also indicate that despite the recent favorable studies towards the use of ITMs based on Renyi’s definitions, these measures are seen not to provide improvements in this type of deformable registration as compared to ITMs based on Shannon’s definitions. [Copyright &y& Elsevier]

The main goal of this study is to identify the association between corneal shape, elevation, and thickness parameters and visual field damage using machine learning. A total of 676 eyes from 568 patients from the Jichi Medical University in Japan were included in this study. Corneal topography, pachymetry, and elevation images were obtained using anterior segment optical coherence tomography (OCT) and visual field tests were collected using standard automated perimetry with 24-2 Swedish Interactive Threshold Algorithm. The association between corneal structural parameters and visual field damage was investigated using machine learning and evaluated through tenfold cross-validation of the area under the receiver operating characteristic curves (AUC). The average mean deviation was − 8.0 dB and the average central corneal thickness (CCT) was 513.1 µm. Using ensemble machine learning bagged trees classifiers, we detected visual field abnormality from corneal parameters with an AUC of 0.83. Using a tree-based machine learning classifier, we detected four visual field severity levels from corneal parameters with an AUC of 0.74. Although CCT and corneal hysteresis have long been accepted as predictors of glaucoma development and future visual field loss, corneal shape and elevation parameters may also predict glaucoma-induced visual functional loss. [ABSTRACT FROM AUTHOR]

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. Design Observational, cross-sectional study. Participants A total of 153 eyes from 31 healthy participants, 48 glaucoma suspects, and 74 glaucoma patients enrolled in the Diagnostic Innovations in Glaucoma Study. Methods All eyes underwent imaging using OCT-A (Angiovue; Optovue, Fremont, CA), spectral-domain OCT (Avanti; Optovue), and standard automated perimetry (SAP). Retinal vasculature information was summarized as vessel density, the percentage of area occupied by flowing blood vessels in the selected region. Two measurements from the retinal nerve fiber layer (RNFL) were used: 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 the severity of visual field loss, reported as SAP mean deviation (MD), and OCT-A vessel density. Results Compared with glaucoma 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%, and 41.7%, respectively) and for cpVD (62.8%, 61.0%, 57.5%, 49.6%, respectively) ( P < 0.001 for both). The association between SAP MD with cpVD and wiVD was stronger ( R 2 = 0.54 and R 2 = 0.51, respectively) than the association between SAP MD with RNFL ( R 2 = 0.36) and rim area ( R 2 = 0.19) ( P < 0.05 for all). Multivariate regression analysis showed that each 1% decrease in wiVD was associated with 0.66 decibel (dB) loss in MD and each 1% decrease in cpVD was associated with 0.64 dB loss in MD. In addition, the association between vessel density and 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 the severity of visual field damage independent of the structural loss. Optical coherence tomography angiography is a promising technology in glaucoma management, potentially enhancing the understanding of the role of vasculature in the pathophysiology of the disease. [ABSTRACT FROM AUTHOR]

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 A total of 82 patients with primary open-angle glaucoma (POAG) 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 Optical coherence tomography (OCT) angiography–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 RNFL (cpRNFL) thicknesses in eyes with and without LC defects. Results Age, global, and sectoral cpRNFL thicknesses, VF mean deviation (MD) and pattern standard deviation, presence of optic disc hemorrhage, and mean ocular perfusion pressure did not differ between patients with and without LC defects ( P > 0.05 for all comparisons). Mean cpVDs of eyes with LC defects were significantly lower than in eyes 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 eyes without a defect in the corresponding IT and IN sectors ( P < 0.05 for all). Eyes with LC defects in the ST sector (n = 19) had lower cpVDs in the ST, IT, and IN sectors ( P < 0.05 for all). Conclusions In eyes with similar severity of glaucoma, OCT-A-measured vessel density was significantly lower in POAG eyes with focal LC defects than in eyes without an LC defect. Moreover, reduction of vessel density was spatially correlated with the location of the LC defect. [ABSTRACT FROM AUTHOR]

Purpose: The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters. Methods: FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age. Results: FDT mean deviation was −1.00 dB (S.D. = 2.80 dB) and −5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p<0.001). VIM identified meaningful clusters of FDT data and positioned a set of statistically independent axes through the mean of each cluster. The optimal VIM model separated the FDT fields into 3 clusters. Cluster N contained primarily normal fields (1109/1190, specificity 93.1%) and clusters G1 and G2 combined, contained primarily abnormal fields (651/786, sensitivity 82.8%). For clusters G1 and G2 the optimal number of axes were 2 and 5, respectively. Patterns automatically generated along axes within the glaucoma clusters were similar to those known to be indicative of glaucoma. Fields located farther from the normal mean on each glaucoma axis showed increasing field defect severity. Conclusions: VIM successfully separated FDT fields from healthy and glaucoma eyes without a priori information about class membership, and identified familiar glaucomatous patterns of loss. [ABSTRACT FROM AUTHOR]

Myopia has been discussed as a risk factor for glaucoma. In this study, we characterized the relationship between ametropia and patterns of visual field (VF) loss in glaucoma. Reliable automated VFs (SITA Standard 24-2) of 120,019 eyes from 70,495 patients were selected from five academic institutions. The pattern deviation (PD) at each VF location was modeled by linear regression with ametropia (defined as spherical equivalent (SE) starting from extreme high myopia), mean deviation (MD), and their interaction (SE × MD) as regressors. Myopia was associated with decreased PD at the paracentral and temporal VF locations, whereas hyperopia was associated with decreased PD at the Bjerrum and nasal step locations. The severity of VF loss modulated the effect of ametropia: with decreasing MD and SE, paracentral/nasal step regions became more depressed and Bjerrum/temporal regions less depressed. Increasing degree of myopia was positively correlated with VF depression at four central points, and the correlation became stronger with increasing VF loss severity. With worsening VF loss, myopes have increased VF depressions at the paracentral and nasal step regions, while hyperopes have increased depressions at the Bjerrum and temporal locations. Clinicians should be aware of these effects of ametropia when interpreting VF loss. [ABSTRACT FROM AUTHOR]