Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia.

BACKGROUND: Graph representational learning can detect topological patterns by leveraging both the network structure as well as nodal features. The basis of our exploration involves the application of graph neural network architectures and machine learning to resting-state functional Magnetic Resonance Imaging (rs-fMRI) data for the purpose of detecting schizophrenia. Our study uses single-site data to avoid the shortcomings in generalizability of neuroimaging data obtained from multiple sites. RESULTS: The performance of our graph neural network models is on par with that of our machine learning models, each of which is trained using 69 graph-theoretical measures computed from functional correlations between various regions of interest (ROI) in a brain graph. Our deep graph convolutional neural network (DGCNN) demonstrates a promising average accuracy score of 0.82 and a sensitivity score of 0.84. CONCLUSIONS: This study provides insights into the role of advanced graph theoretical methods and machine learning on fMRI data to detect schizophrenia by harnessing changes in brain functional connectivity. The results of this study demonstrate the capabilities of using both traditional ML techniques as well as graph neural network-based methods to detect schizophrenia using features extracted from fMRI data. The study also proposes two methods to obtain potential biomarkers for the disease, many of which are corroborated by research in this area and can further help in the understanding of schizophrenia as a mental disorder.

Choroidal vascularity features of fundus tessellation in adults with high myopia.

BACKGROUND: To investigate alterations in choroidal vascularity index among highly myopic adults with fundus tessellation, utilizing optical coherence tomography. METHODS: Total of 143 highly myopic adults (234 eyes) with fundus tessellation were collected in this cross-sectional study, which was stratified into different lesion groups based on the novel tessellated fundus classification. Subfoveal choroidal thickness (SFCT), choroidal luminal area (LA), stromal area (SA), total choroidal area (TCA), and choroidal vascularity index (CVI) were analyzed utilizing optical coherence tomography (OCT) with enhanced depth imaging (EDI) mode, enabling precise quantification of these parameters. RESULTS: Comparison analysis demonstrated notable distinctions in spherical equivalent (SE), axial length (AL), and SFCT across the four tessellation grades (p < 0.001). Analysis of the choroidal vascularity parameters, including LA, TCA, and CVI, demonstrated notable disparities across the four groups (p < 0.001), while no significant variations were observed in SA when comparing Grade 1 versus Grade 2, as well as Grade 2 versus Grade 3 (p > 0.05). Logistic regression analyses illustrated that the higher grade of tessellated exhibited a positive association with AL (OR = 1.701, p = 0.027), while negatively associated with SFCT (OR = 0.416, p = 0.007), LA (OR = 0.438, p = 0.010) and CVI (OR = 0.529, p = 0.004). Multiple regression analyses demonstrated a significant negative association between CVI and both SE and AL after adjusting for age, while positively associated with SFCT (p < 0.05). CONCLUSION: Subtle choroidal vascularity changes may have a meaningful contribution to the development and progression of fundus tessellation. CVI and LA dramatically decreased during the early stages of tessellation development and maintained a relatively stable status when in the severe tessellated grades.

(HTBNet)Arbitrary Shape Scene Text Detection with Binarization of Hyperbolic Tangent and Cross-Entropy.

The existing segmentation-based scene text detection methods mostly need complicated post-processing, and the post-processing operation is separated from the training process, which greatly reduces the detection performance. The previous method, DBNet, successfully simplified post-processing and integrated post-processing into a segmentation network. However, the training process of the model took a long time for 1200 epochs and the sensitivity to texts of various scales was lacking, leading to some text instances being missed. Considering the above two problems, we design the text detection Network with Binarization of Hyperbolic Tangent (HTBNet). First of all, we propose the Binarization of Hyperbolic Tangent (HTB), optimized along with which the segmentation network can expedite the initial convergent speed by reducing the number of epochs from 1200 to 600. Because features of different channels in the same scale feature map focus on the information of different regions in the image, to better represent the important features of all objects in the image, we devise the Multi-Scale Channel Attention (MSCA). Meanwhile, considering that multi-scale objects in the image cannot be simultaneously detected, we propose a novel module named Fused Module with Channel and Spatial (FMCS), which can fuse the multi-scale feature maps from channel and spatial dimensions. Finally, we adopt cross-entropy as the loss function, which measures the difference between predicted values and ground truths. The experimental results show that HTBNet, compared with lightweight models, has achieved competitive performance and speed on Total-Text (F-measure:86.0%, FPS:30) and MSRA-TD500 (F-measure:87.5%, FPS:30).

Thermal plasticity of wing size and wing spot size in Drosophila guttifera.

Thermal plasticity of melanin pigmentation patterns in Drosophila species has been studied as a model to investigate developmental mechanisms of phenotypic plasticity. The developmental process of melanin pigmentation patterns on wings of Drosophila is divided into two parts, prepattern specification during the pupal period and wing vein-dependent transportation of melanin precursors after eclosion. Which part can be affected by thermal changes? To address this question, we used polka-dotted melanin spots on wings of Drosophila guttifera, whose spot areas are specified by wingless morphogen. In this research, we reared D. guttifera at different temperatures to test whether wing spots show thermal plasticity. We found that wing size becomes larger at lower temperature and that different spots have different reaction norms. Furthermore, we changed the rearing temperature in the middle of the pupal period and found that the most sensitive developmental periods for wing size and spot size are different. The results suggest that the size control mechanisms for the thermal plasticity of wing size and spot size are independent. We also found that the most sensitive stage for spot size was part of the pupal period including stages at which wingless is expressed in the polka-dotted pattern. Therefore, it is suggested that temperature change might affect the prepattern specification process and might not affect transportation through wing veins.

An Identification Method for Irregular Components Related to Terminal Blocks in Equipment Cabinet of Power Substation.

Despite the continuous advancement of intelligent power substations, the terminal block components within equipment cabinet inspection work still often require loads of personnel. The repetitive documentary works not only lack efficiency but are also susceptible to inaccuracies introduced by substation personnel. To resolve the problem of lengthy, time-consuming inspections, a terminal block component detection and identification method is presented in this paper. The identification method is a multi-stage system that incorporates a streamlined version of You Only Look Once version 7 (YOLOv7), a fusion of YOLOv7 and differential binarization (DB), and the utilization of PaddleOCR. Firstly, the YOLOv7 Area-Oriented (YOLOv7-AO) model is developed to precisely locate the complete region of terminal blocks within substation scene images. The compact area extraction model rapidly cuts out the valid proportion of the input image. Furthermore, the DB segmentation head is integrated into the YOLOv7 model to effectively handle the densely arranged, irregularly shaped block components. To detect all the components within a target electrical cabinet of substation equipment, the YOLOv7 model with a differential binarization attention head (YOLOv7-DBAH) is proposed, integrating spatial and channel attention mechanisms. Finally, a general OCR algorithm is applied to the cropped-out instances after image distortion to match and record the component's identity information. The experimental results show that the YOLOv7-AO model reaches high detection accuracy with good portability, gaining 4.45 times faster running speed. Moreover, the terminal block component detection results show that the YOLOv7-DBAH model achieves the highest evaluation metrics, increasing the F1-score from 0.83 to 0.89 and boosting the precision to over 0.91. The proposed method achieves the goal of terminal block component identification and can be applied in practical situations.

A Two-Stage Automatic Color Thresholding Technique.

Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one's focus to the object of inspection. We propose a two-stage histogram-based background suppression technique based on the chromaticity of the image pixels. The method is unsupervised, fully automated, and does not need any training or ground-truth data. The performance of the proposed method was evaluated using a printed circuit assembly (PCA) board dataset and the University of Waterloo skin cancer dataset. Accurately performing background suppression in PCA boards facilitates the inspection of digital images with small objects of interest, such as text or microcontrollers on a PCA board. The segmentation of skin cancer lesions will help doctors to automate skin cancer detection. The results showed a clear and robust background-foreground separation across various sample images under different camera or lighting conditions, which the naked implementation of existing state-of-the-art thresholding methods could not achieve.

Fast Detection of Missing Thin Propagating Cracks during Deep-Learning-Based Concrete Crack/Non-Crack Classification.

Existing deep learning (DL) models can detect wider or thicker segments of cracks that occupy multiple pixels in the width direction, but fail to distinguish the thin tail shallow segment or propagating crack occupying fewer pixels. Therefore, in this study, we proposed a scheme for tracking missing thin/propagating crack segments during DL-based crack identification on concrete surfaces in a computationally efficient manner. The proposed scheme employs image processing as a preprocessor and a postprocessor for a 1D DL model. Image-processing-assisted DL as a precursor to DL eliminates labor-intensive labeling and the plane structural background without any distinguishable features during DL training and testing; the model identifies potential crack candidate regions. Iterative differential sliding-window-based local image processing as a postprocessor to DL tracks missing thin cracks on segments classified as cracks. The capability of the proposed method is demonstrated on low-resolution images with cracks of single-pixel width, captured using unmanned aerial vehicles on concrete structures with different surface textures, different scenes with complicated disturbances, and optical variability. Due to the multi-threshold-based image processing, the overall approach is invariant to the choice of initial sensitivity parameters, hyperparameters, and the sequence of neuron arrangement. Further, this technique is a computationally efficient alternative to semantic segmentation that results in pixelated mapping/classification of thin crack regimes, which requires labor-intensive and skilled labeling.

Short time effect of sodium fluorescein on chorodial vascularity index.

PURPOSE: This study aimed to investigate the effect of sodium fluorescein on the choroidal vascularity index (CVI). MATERIALS AND METHODS: In this cross-sectional study, a total of 27 eyes of 27 mild nonproliferative diabetic retinopathy patients (without maculopathy and any systemic disease) who performed fluorescein angiography (FA) procedure were included. Choroidal parameters such as choroidal thickness (CT), total choroidal area (TCA), luminal area (LA), stromal area (SA), LA/SA, and CVI were examined with the optical coherence tomography and binarization technique at baseline and 5th, 15th, and 30th minutes after FA. The values of the parameters before and after the procedure were compared. RESULTS: At baseline, the mean values of the TCA, LA, SA, LA/SA, and CVI were 0.44 ± 0.14mm2, 0.29 ± 0.09 mm2, 0.15 ± 0.05 mm2, 1.87 ± 0.19. Five minutes later to FA, the mean values of the TCA, LA, SA, LA/SA, and CVI were 0.43 ± 0.13 mm2, 0.28 ± 0.08 mm2, 0.15 ± 0.05 mm2, 1.82 ± 0.20, and 0.64 ± 0.03, respectively. There was a significant decrease in LA and CVI values 5 minutes after FA (p:0.002 and p:0.021, respectively). On the other hand, the mean, nasal, subfoveal, and temporal CT were 279.22 ± 93.40 µm, 289.78 ± 91.17 µm, 267.44 ± 95.71 µm before FA and 270.33 ± 90.34 µm, 279.67 ± 90.01 µm, 261.82 ± 95.82 µm 5 minutes after FA (p = 0.960, p = 0.952, and p = 0.991, respectively). Although there was a reduction in the value of CT, there was not a statistically significant difference between before and after FA. CONCLUSIONS: This study shows that there was a significant decrease in LA and CVI values 5 minutes after FA in patients with mild nonproliferative diabetic retinopathy.

Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics.

We introduce binacox, a prognostic method to deal with the problem of detecting multiple cut-points per feature in a multivariate setting where a large number of continuous features are available. The method is based on the Cox model and combines one-hot encoding with the binarsity penalty, which uses total-variation regularization together with an extra linear constraint, and enables feature selection. Original nonasymptotic oracle inequalities for prediction (in terms of Kullback-Leibler divergence) and estimation with a fast rate of convergence are established. The statistical performance of the method is examined in an extensive Monte Carlo simulation study, and then illustrated on three publicly available genetic cancer data sets. On these high-dimensional data sets, our proposed method outperforms state-of-the-art survival models regarding risk prediction in terms of the C-index, with a computing time orders of magnitude faster. In addition, it provides powerful interpretability from a clinical perspective by automatically pinpointing significant cut-points in relevant variables.

Alterations of choroidal circulation and vascular morphology in a patient with chronic myeloid leukemia before and after chemotherapy.

BACKGROUND: Chronic myeloid leukemia (CML) is known to cause leukemic retinopathy due to leukemia cell invasion into the choroid; however, details of the circulatory dynamics and morphological changes in the choroid are unknown. The aim of this study was to present a case of leukemic retinopathy and examine choroidal circulatory and structural analyses using laser speckle flowgraphy (LSFG) and optical coherence tomography with a binarization method, respectively. CASE PRESENTATION: A 15-year-old male diagnosed with CML complained of blurred vision in his right eye. He was ophthalmologically diagnosed with leukemic retinopathy due to retinal hemorrhage in both eyes. Tyrosine kinase inhibitors achieved complete cytogenetic remission and resolution of retinal hemorrhages at 6 months after treatment. After the treatment, the best-corrected visual acuity had recovered from 0.1 to 1.2 oculus dexter (OD) and remained at 1.5 oculus sinister (OS). The rate of change in macular blood flow assessed by the mean blur rate on LSFG was 18.3% increase OD and 25.2% decrease OS 19 months after treatment. The central choroidal thickness showed 0.4 and 3.1% reductions OD and OS, respectively. The binarization technique demonstrated that the rate of luminal areas in choroidal areas exhibited 3.2% increase OD but 4.8% decrease OS. CONCLUSION: Choroidal blood flow improved OD after treatment for CML, while it deteriorated OS, together with choroidal thinning due to reduction of luminal areas. The degrees of leukemia cell invasion into the choroidal tissue and tissue destruction might be different between the eyes in this case.

Low-intensity, long-wavelength red light slows the progression of myopia in children: an Eastern China-based cohort.

PURPOSE: To determine the effect of low-intensity, long-wavelength red light therapy (LLRT) on the inhibition of myopia progression in children. METHODS: A retrospective study was conducted. One hundred and five myopic children (spherical equivalent refractive error [SER] -3.09 ± 1.74 dioptres [D]; mean age, 9.19 ± 2.40 years) who underwent LLRT treatment (power 0.4 mW, wavelength 635 nm) twice per day for 3 min each session, with at least a 4-h interval between sessions, and a control group of 56 myopic children (SER -3.04 ± 1.66 D; mean age, 8.62 ± 2.45 years) were evaluated. Both groups wore single-vision distance spectacles. Each child returned for a follow-up examination every 3 months after the initial measurements for a total of 9 months. RESULTS: At 9 months, the mean SER in the LLRT group was -2.87 ± 1.89 D, significantly greater than that of the control group (-3.57 ± 1.49 D, p < 0.001). Axial length (AL) changes were -0.06 ± 0.19 mm and 0.26 ± 0.15 mm in the LLRT group and control group (p < 0.001), respectively. The subfoveal choroidal thickness changed by 45.32 ± 30.88 µm for children treated with LLRT at the 9-month examination (p < 0.001). Specifically, a substantial hyperopic shift (0.31 ± 0.24 D and 0.20 ± 0.14 D, respectively, p = 0.02) was found in the 8-14 year olds compared with 4-7 year old children. The decrease in AL in subjects with baseline AL >24 mm was -0.08 ± 0.19 mm, significantly greater than those with a baseline AL ≤24 mm (-0.04 ± 0.18 mm, p = 0.03). CONCLUSIONS: Repetitive exposure to LLRT therapy was associated with slower myopia progression and reduced axial growth after short durations of treatment. These results require further validation in randomised controlled trials.

Binarization Algorithm Based on Side Window Multidimensional Convolution Classification.

Uneven illumination and space radiation can cause inhomogeneous grayscale distribution, low contrast, and noisy images in in-orbit cameras. A binarization algorithm based on morphological classification is proposed to solve the problem of inaccurate image binarization caused by space image degradation. Traditional local binarization algorithms generally calculate thresholds based on statistical information of gray dimensions within the local window, often ignoring the morphological distribution information, leading to poor results in degraded images. The algorithm presented in this paper demonstrates the property of the side window filtering (SWF) kernel on morphological clustering. First, the eight-dimensional SWF convolution kernel is used to describe the morphological properties of the pixels. Then, the positive and negative types of each pixel in the local window are identified, and the local threshold is calculated according to the difference between the two types. Finally, the positive pixel is used to filter the threshold of each pixel, with the binarization threshold satisfying the morphologically smooth and continuous property. A self-built dataset is used to evaluate the algorithm quantitatively and the results are compared with the three existing classical techniques using the quantitative measures FM, PSNR, and DRD. The experimental results show that the algorithm in this paper yields good binarization results for different degraded images, outperforms the comparison algorithm in terms of accuracy and robustness, and is insensitive to noise.

Attention-Based Scene Text Detection on Dual Feature Fusion.

The segmentation-based scene text detection algorithm has advantages in scene text detection scenarios with arbitrary shape and extreme aspect ratio, depending on its pixel-level description and fine post-processing. However, the insufficient use of semantic and spatial information in the network limits the classification and positioning capabilities of the network. Existing scene text detection methods have the problem of losing important feature information in the process of extracting features from each network layer. To solve this problem, the Attention-based Dual Feature Fusion Model (ADFM) is proposed. The Bi-directional Feature Fusion Pyramid Module (BFM) first adds stronger semantic information to the higher-resolution feature maps through a top-down process and then reduces the aliasing effects generated by the previous process through a bottom-up process to enhance the representation of multi-scale text semantic information. Meanwhile, a position-sensitive Spatial Attention Module (SAM) is introduced in the intermediate process of two-stage feature fusion. It focuses on the one feature map with the highest resolution and strongest semantic features generated in the top-down process and weighs the spatial position weight by the relevance of text features, thus improving the sensitivity of the text detection network to text regions. The effectiveness of each module of ADFM was verified by ablation experiments and the model was compared with recent scene text detection methods on several publicly available datasets.

Choroidal Assessment in Patients with Type 2 Diabetes Mellitus and Non-Proliferative Diabetic Retinopathy by Swept-Source Ocular Coherence Tomography and Image Binarization.

Background and Objectives: The aim of this study was to evaluate choroidal structure and vascularity indices in patients with non-proliferative diabetic retinopathy (NPDR). Materials and Methods: Sixty-three eyes from sixty-three patients were evaluated: 21 from healthy subjects, 20 with diabetes mellitus (DM) and no diabetic retinopathy (DR), and 22 with DM and non-proliferative diabetic retinopathy without diabetic macular edema (DME). Each patient underwent ocular examination, macular swept-source ocular coherence tomography (SS-OCT) imaging, glycemic control, and systemic high blood pressure (HBP) evaluation. Subfoveal choroidal thickness (SF-CT) was manually assessed on a line scan. Line scan OCT images were exported to ImageJ program. The areas under a 1.5, 3 and 6 mm horizontal line centered on the fovea were assessed by converting the OCT images to binary images, and total choroidal area (TCA), luminal area (LA), stromal area (SA), LA:SA ratio, and choroidal vascularity index (CVI) were evaluated. SF-CT and choroidal parameters were compared between groups, and correlations with ocular and systemic factors were analyzed. Results: SF-CT, TCA, LA, and SA were similar between groups. CVIs were significantly different between groups for all three studied areas (CVI-1.5: 66.21% vs. 66.06% vs. 63.74%, p = 0.003; CVI-3: 65.88% vs. 66.46% vs. 63.79%, p = 0.008; CVI-6: 64.79% vs. 65.40% vs. 63.61%, p = 0.032). NPDR patients had significantly lower CVIs compared to DM patients (p < 0.05). No association of choroidal parameters with glycemic control, DM duration and HBP was found significant (p < 0.05). Conclusions: Choroidal assessment by SS-OCT and image binarization in healthy subjects, subjects with DM without DR, and subjects with DM and NPDR indicated that CVI changes were identifiable and significant in early DR. The lack of association with ocular and systemic factors suggest that CVIs are reliable assessment parameters of choroidal vascular structure.

Choroidal vascular structures in diabetic patients: a meta-analysis.

PURPOSE: Choroidal vascular structures are likely to be affected in diabetic patients. The aim of this study was to conduct a meta-analysis of choroidal vascular structures in diabetic eyes with no diabetic retinopathy (NDR) and healthy control eyes, which was systematically evaluated by various factors involving the measurements. METHODS: This study identified clinical data from publications in PubMed and web of science until May 2020. Independent retrospective or prospective clinical studies comparing NDR and healthy control eyes regarding choroidal vascular structures were extracted. Five related studies were enrolled, cumulating in a total of 282 diabetic eyes and 511 control eyes examined in this study. Heterogeneity was statistically quantified by I2 statistics, and meta-analysis was performed using a random effects model. This study included 2 different algorisms of binarization determining the ratio of luminal areas in total choroidal areas, both of which were consolidated and called "choroidal vascular ratio." RESULTS: Meta-analysis clearly showed that the choroidal vascular ratio was significantly lower in NDR eyes than in healthy control eyes (weighted mean difference = - 2.16; 95%CI: - 3.19 to - 1.13; P < 0.005). Similar results were obtained in sub-analysis based on adjustment of serum HbA1c levels and duration of diabetes. CONCLUSIONS: The choroidal vascular ratio of NDR eyes was significantly lower than that of healthy control eyes. The ratio might contribute to a better understanding of the pathophysiology involved in the development of diabetic retinopathy, although there was some heterogeneity in primary analysis studies.

The effect of optic neuritis attacks on choroidal vascularity index in patients with multiple sclerosis.

PURPOSE: To investigate the changes in the choroidal vascularity index (CVI) of patients with multiple sclerosis (MS) using binarization on enhanced depth imaging optical coherence tomography (EDI-OCT) images and to evaluate the effect of optic neuritis (ON) attacks on these measurements. METHOD: Three groups were created by including forty eyes of 20 patients diagnosed with relapsing-remitting MS and had a unilateral history of ON attack and the randomly selected eyes of 30 healthy age- and sex-matched control subjects. Group 1 (n = 20) consisted of the ON-affected eyes of the MS patients (MSON); group 2 (n = 20) included their fellow healthy eyes (MSNON); group 3 (n = 30) included the eyes of healthy controls. RESULTS: The mean age was 33.3 ± 9.4 years in the MS group and 33.4 ± 11.1 years in the healthy control group. Mean choroidal vascularity index (CVI) was significantly lower in the MSON group than the MSNON group (59.6 ± 3.72 % vs 61.7 ± 3.16 %, p = 0.007). The CVI values of both the MSON and MSNON groups were significantly lower when compared to the controls (63.9 ± 2.76) (p < 0.001, p = 0.030). Compared to controls, the subfoveal total choroidal area and luminal area values were significantly greater in the MSON (p = 0.009, p = 0.009, respectively) and MSNON groups (p = 0.031, p = 0.013, respectively). CONCLUSION: The presented study demonstrates that, compared to healthy subjects, CVI values are lower in the affected and unaffected eyes of patients who had a history of ON in relation with their MS diagnosis. The presence of significant anatomical changes, especially in the luminal area, may suggest that ON causes vascular disorganization which contributes to MS pathophysiology.

Novel Features for Binary Time Series Based on Branch Length Similarity Entropy.

Branch length similarity (BLS) entropy is defined in a network consisting of a single node and branches. In this study, we mapped the binary time-series signal to the circumference of the time circle so that the BLS entropy can be calculated for the binary time-series. We obtained the BLS entropy values for "1" signals on the time circle. The set of values are the BLS entropy profile. We selected the local maximum (minimum) point, slope, and inflection point of the entropy profile as the characteristic features of the binary time-series and investigated and explored their significance. The local maximum (minimum) point indicates the time at which the rate of change in the signal density becomes zero. The slope and inflection points correspond to the degree of change in the signal density and the time at which the signal density changes occur, respectively. Moreover, we show that the characteristic features can be widely used in binary time-series analysis by characterizing the movement trajectory of Caenorhabditis elegans. We also mention the problems that need to be explored mathematically in relation to the features and propose candidates for additional features based on the BLS entropy profile.

Informative frailty indices from binarized biomarkers.

Frailty indices (FIs) based on continuous valued health data, such as obtained from blood and urine tests, have been shown to be predictive of adverse health outcomes. However, creating FIs from such biomarker data requires a binarization treatment that is difficult to standardize across studies. In this work, we explore a "quantile" methodology for the generic treatment of biomarker data that allows us to construct an FI without preexisting medical knowledge (i.e. risk thresholds) of the included biomarkers. We show that our quantile approach performs as well as, or even slightly better than, established methods for the National Health and Nutrition Examination Survey and the Canadian Study of Health and Aging data sets. Furthermore, we show that our approach is robust to cohort effects within studies as compared to other data-based methods. The success of our binarization approaches provides insight into the robustness of the FI as a health measure, and the upper limits of the FI observed in various data sets, and also highlights general difficulties in obtaining absolute scales for comparing FIs between studies.

Alteration of choroidal vascular structure in diabetic macular edema.

PURPOSE: The aim of this study was to analyze choroidal structures in healthy subjects and patients with/without diabetic macular edema (DME). METHODS: This was a retrospective observation case control study. Four hundred and two eyes of patients with diabetes mellitus (DM), and 124 age-matched eyes of healthy subjects were enrolled in this study. DM patients were divided into 3 groups: presence of central-involved (CI) DME (n = 81) and nonCI-DME/non-DME (n = 321), based on OCT findings. Central choroidal thickness (CCT) and total choroidal, luminal, and stromal areas were determined using EDI-OCT and a binarization method, respectively. The luminal area expressed as a ratio of the total choroidal area was defined as the L/C ratio. RESULTS: DM eyes showed a significantly lower L/C ratio than control eyes, whereas there was no significant difference in CCT or total choroidal, luminal, or stromal areas. There was no significant difference between CI-DME and non-DME groups in HbA1c, blood pressure, dyslipidemia, or renal function. CCT and total choroidal, luminal, and stromal areas were significantly greater in the CI-DME group than non-DME group (each P < 0.05). CONCLUSIONS: These results suggest that CCT was thickened in the presence of DME, associated with both increased luminal and stromal areas, which might be related to the pathology of DME.

Association of retinal biomarkers and choroidal vascularity index on optical coherence tomography using binarization method in retinitis pigmentosa.

PURPOSE: To investigate the association of retinal biomarkers with the choroidal parameters in retinitis pigmentosa (RP). METHODS: This prospective study included 69 eyes of 36 patients with RP. Choroidal vascularity index (CVI) was defined as the ratio of luminal area to stromal area after binarization on EDI-OCT images. Choroidal thickness (CT); peripapillary CT, the disruptions of the ellipsoid zone (EZ) and external limiting membrane (ELM); and the existence of disorganization of the retinal inner layers (DRIL) and epiretinal membrane (ERM) in central 1000 µm were noted. RESULTS: Having DRIL and the disruption of EZ and ELM was significantly associated with higher CVI (p < 0.001, p = 0.001, and p = 0.002 respectively) and lower peripapillary CT in temporal sector (p = 0.031, p = 0.012, and p = 0.043 respectively). Having ERM, the disruption of EZ and ELM was significantly associated with lower visual acuity (VA) (p = 0.044, p < 0.001, and p < 0.001 respectively). The eyes with ERM had significantly lower peripapillary retinal nerve fiber thickness (pRNFLT) (p = 0.040). The mean peripapillary CT significantly and positively correlated with the temporal, nasal, superonasal, and the mean pRNFLT (r = 0.258, p = 0.036, r = 0.252, p = 0.041, r = 0.260, p = 0.035, r = 0.280, p = 0.023 respectively). VA did not significantly correlate with CT, peripapillary CT, or CVI (p > 0.05). CONCLUSION: The disruption outer retinal segment integrity was significantly associated with higher CVI and lower peripapillary CT in temporal segment. ERM and disruption of ELM and EZ were associated with worse VA. VA did not significantly correlate with CT, peripapillary CT, or CVI.