On Optical Coherence Tomography Images Research Articles

Wavelet-based selection-and-recalibration network for Parkinson’s disease screening in OCT images

Background and Objective:Parkinson’s disease (PD) is one of the most prevalent neurodegenerative brain diseases worldwide. Therefore, accurate PD screening is crucial for early clinical intervention and treatment. Recent clinical research indicates that changes in pathology, such as the texture and thickness of the retinal layers, can serve as biomarkers for clinical PD diagnosis based on optical coherence tomography (OCT) images. However, the pathological manifestations of PD in the retinal layers are subtle compared to the more salient lesions associated with retinal diseases. Methods:Inspired by textural edge feature extraction in frequency domain learning, we aim to explore a potential approach to enhance the distinction between the feature distributions in retinal layers of PD cases and healthy controls. In this paper, we introduce a simple yet novel wavelet-based selection and recalibration module to effectively enhance the feature representations of the deep neural network by aggregating the unique clinical properties, such as the retinal layers in each frequency band. We combine this module with the residual block to form a deep network named Wavelet-based Selection and Recalibration Network (WaveSRNet) for automatic PD screening. Results:The extensive experiments on a clinical PD-OCT dataset and two publicly available datasets demonstrate that our approach outperforms state-of-the-art methods. Visualization analysis and ablation studies are conducted to enhance the explainability of WaveSRNet in the decision-making process. Conclusions:Our results suggest the potential role of the retina as an assessment tool for PD. Visual analysis shows that PD-related elements include not only certain retinal layers but also the location of the fovea in OCT images.

A deep learning approach to hard exudates detection and disorganization of retinal inner layers identification on OCT images

The purpose of the study was to detect Hard Exudates (HE) and classify Disorganization of Retinal Inner Layers (DRIL) implementing a Deep Learning (DL) system on optical coherence tomography (OCT) images of eyes with diabetic macular edema (DME). We collected a dataset composed of 442 OCT images on which we annotated 6847 HE and the presence of DRIL. A complex operational pipeline was defined to implement data cleaning and image transformations, and train two DL models. The state-of-the-art neural network architectures (Yolov7, ConvNeXt, RegNetX) and advanced techniques were exploited to aggregate the results (Ensemble learning, Edge detection) and obtain a final model. The DL approach reached good performance in detecting HE and classifying DRIL. Regarding HE detection the model got an AP@0.5 score equal to 34.4% with Precision of 48.7% and Recall of 43.1%; while for DRIL classification an Accuracy of 91.1% with Sensitivity and Specificity both of 91.1% and AUC and AUPR values equal to 91% were obtained. The P-value was lower than 0.05 and the Kappa coefficient was 0.82. The DL models proved to be able to identify HE and DRIL in eyes with DME with a very good accuracy and all the metrics calculated confirmed the system performance. Our DL approach demonstrated to be a good candidate as a supporting tool for ophthalmologists in OCT images analysis.

Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research.

With the advancement of retinal imaging, hyperreflective foci (HRF) on optical coherence tomography (OCT) images have gained significant attention as potential biological biomarkers for retinal neuroinflammation. However, these biomarkers, represented by HRF, present pose challenges in terms of localization, quantification, and require substantial time and resources. In recent years, the progress and utilization of artificial intelligence (AI) have provided powerful tools for the analysis of biological markers. AI technology enables use machine learning (ML), deep learning (DL) and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments. Based on ophthalmic images, AI has significant implications for early screening, diagnostic grading, treatment efficacy evaluation, treatment recommendations, and prognosis development in common ophthalmic diseases. Moreover, it will help reduce the reliance of the healthcare system on human labor, which has the potential to simplify and expedite clinical trials, enhance the reliability and professionalism of disease management, and improve the prediction of adverse events. This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration (AMD), diabetic macular edema (DME), retinal vein occlusion (RVO) and other retinal diseases and presents prospects for their utilization.

Deep Neural Networks for Automated Outer Plexiform Layer Subsidence Detection on Retinal OCT of Patients With Intermediate AMD.

The subsidence of the outer plexiform layer (OPL) is an important imaging biomarker on optical coherence tomography (OCT) associated with early outer retinal atrophy and a risk factor for progression to geographic atrophy in patients with intermediate age-related macular degeneration (AMD). Deep neural networks (DNNs) for OCT can support automated detection and localization of this biomarker. The method predicts potential OPL subsidence locations on retinal OCTs. A detection module (DM) infers bounding boxes around subsidences with a likelihood score, and a classification module (CM) assesses subsidence presence at the B-scan level. Overlapping boxes between B-scans are combined and scored by the product of the DM and CM predictions. The volume-wise score is the maximum prediction across all B-scans. One development and one independent external data set were used with 140 and 26 patients with AMD, respectively. The system detected more than 85% of OPL subsidences with less than one false-positive (FP)/scan. The average area under the curve was 0.94 ± 0.03 for volume-level detection. Similar or better performance was achieved on the independent external data set. DNN systems can efficiently perform automated retinal layer subsidence detection in retinal OCT images. In particular, the proposed DNN system detects OPL subsidence with high sensitivity and a very limited number of FP detections. DNNs enable objective identification of early signs associated with high risk of progression to the atrophic late stage of AMD, ideally suited for screening and assessing the efficacy of the interventions aiming to slow disease progression.

Deep learning automatically assesses 2-µm laser-induced skin damage OCT images.

The present study proposed a noninvasive, automated, in vivo assessment method based on optical coherence tomography (OCT) and deep learning techniques to qualitatively and quantitatively analyze the biological effects of 2-µm laser-induced skin damage at different irradiation doses. Different doses of 2-µm laser irradiation established a mouse skin damage model, after which the skin-damaged tissues were imaged non-invasively in vivo using OCT. The acquired images were preprocessed to construct the dataset required for deep learning. The deep learning models used were U-Net, DeepLabV3+, PSP-Net, and HR-Net, and the trained models were used to segment the damage images and further quantify the damage volume of mouse skin under different irradiation doses. The comparison of the qualitative and quantitative results of the four network models showed that HR-Net had the best performance, the highest agreement between the segmentation results and real values, and the smallest error in the quantitative assessment of the damage volume. Based on HR-Net to segment the damage image and quantify the damage volume, the irradiation doses 5.41, 9.55, 13.05, 20.85, 32.71, 52.92, 76.71, and 97.24J/cm² corresponded to a damage volume of 4.58, 12.56, 16.74, 20.88, 24.52, 30.75, 34.13, and 37.32mm³. The damage volume increased in a radiation dose-dependent manner.

An adaptive weighted ensemble learning network for diabetic retinopathy classification.

Diabetic retinopathy (DR) is one of the leading causes of blindness. However, because the data distribution of classes is not always balanced, it is challenging for automated early DR detection using deep learning techniques. In this paper, we propose an adaptive weighted ensemble learning method for DR detection based on optical coherence tomography (OCT) images. Specifically, we develop an ensemble learning model based on three advanced deep learning models for higher performance. To better utilize the cues implied in these base models, a novel decision fusion scheme is proposed based on the Bayesian theory in terms of the key evaluation indicators, to dynamically adjust the weighting distribution of base models to alleviate the negative effects potentially caused by the problem of unbalanced data size. Extensive experiments are performed on two public datasets to verify the effectiveness of the proposed method. A quadratic weighted kappa of 0.8487 and an accuracy of 0.9343 on the DRAC2022 dataset, and a quadratic weighted kappa of 0.9007 and an accuracy of 0.8956 on the APTOS2019 dataset are obtained, respectively. The results demonstrate that our method has the ability to enhance the ovearall performance of DR detection on OCT images.

OCT analysis of in-stent neointima over 5 years post-DES implantation

Objective: To investigate the characteristics of neointimal hyperplasia (NIH) in patients with in-stent restenosis (ISR) over 5 years post-drug-eluting stent (DES) implantation based on optical coherence tomography (OCT). Methods: In this cross-sectional study, patients with DES-ISR who underwent OCT examination at PLA General Hospital between March 2010 and March 2022 were retrospectively included. All patients were divided into≤5 years DES-ISR group and>5 years DES-ISR group according to the time interval after DES implantation. Quantitative and qualitative analyses were conducted on OCT images to compare the clinical data and lesion characteristics of two patient groups. Furthermore, the independent clinical predictive factors of in-stent neoatherosclerosis (ISNA) were analyzed by multivariable logistic regression. Results: A total of 230 DES-ISR patients with 249 lesions were included, with an age of (63.1±10.4) years and 188 males (81.7%). The median interval after DES implantation was 6 (2, 9) years. There were 117 patients (122 ISR lesions) in the≤5 years DES-ISR group, and 113 patients (127 ISR lesions) in the>5 years DES-ISR group. Compared with≤5 years DES-ISR,>5 years DES-ISR showed more heterogeneous patterns (65.4% (83/127) vs. 48.4% (59/122), P=0.007), diffuse patterns (46.5% (59/127) vs. 31.2% (38/122), P=0.013), macrophage accumulations (44.1% (56/127) vs. 31.2% (38/122), P=0.035) in NIH and higher prevalence of ISNA (83.5% (106/127) vs. 72.1% (88/122), P=0.031). According to multivariable logistic regression, the independent predictive factor for ISNA was female (OR=0.44, 95%CI 0.21-0.90, P=0.026). Female (OR=0.48, 95%CI 0.23-0.99, P=0.046) and low-density lipoprotein cholesterol level (OR=1.62, 95%CI 1.01-2.59, P=0.046) were independent predictive factors, respectively, for lipid ISNA. Calcified ISNA was independently associated with time interval of post-DES implantation (OR=1.18, 95%CI 1.07-1.29, P=0.001). Conclusion: DES-ISR patients with a time interval of>5 years after stent implantation have a higher prevalence of ISNA and more complex lesions. Gender, the level of low-density lipoprotein cholesterol, and the time interval post-DES implantation are independently correlated with ISNA, lipid ISNA, and calcified ISNA.

Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2

Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases. To develop a self-supervised approach for automated classification of macular telangiectasia type 2 (MacTel) on optical coherence tomography (OCT) with limited labeled data. This was a retrospective comparative study. OCT images from May 2014 to May 2019 were collected by the Lowy Medical Research Institute, La Jolla, California, and the University of Washington, Seattle, from January 2016 to October 2022. Clinical diagnoses of patients with and without MacTel were confirmed by retina specialists. Data were analyzed from January to September 2023. Two convolutional neural networks were pretrained using the Bootstrap Your Own Latent algorithm on unlabeled training data and fine-tuned with labeled training data to predict MacTel (self-supervised method). ResNet18 and ResNet50 models were also trained using all labeled data (supervised method). The ground truth yes vs no MacTel diagnosis is determined by retinal specialists based on spectral-domain OCT. The models' predictions were compared against human graders using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under precision recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC). Uniform manifold approximation and projection was performed for dimension reduction and GradCAM visualizations for supervised and self-supervised methods. A total of 2636 OCT scans from 780 patients with MacTel and 131 patients without MacTel were included from the MacTel Project (mean [SD] age, 60.8 [11.7] years; 63.8% female), and another 2564 from 1769 patients without MacTel from the University of Washington (mean [SD] age, 61.2 [18.1] years; 53.4% female). The self-supervised approach fine-tuned on 100% of the labeled training data with ResNet50 as the feature extractor performed the best, achieving an AUPRC of 0.971 (95% CI, 0.969-0.972), an AUROC of 0.970 (95% CI, 0.970-0.973), accuracy of 0.898%, sensitivity of 0.898, specificity of 0.949, PPV of 0.935, and NPV of 0.919. With only 419 OCT volumes (185 MacTel patients in 10% of labeled training dataset), the ResNet18 self-supervised model achieved comparable performance, with an AUPRC of 0.958 (95% CI, 0.957-0.960), an AUROC of 0.966 (95% CI, 0.964-0.967), and accuracy, sensitivity, specificity, PPV, and NPV of 90.2%, 0.884, 0.916, 0.896, and 0.906, respectively. The self-supervised models showed better agreement with the more experienced human expert graders. The findings suggest that self-supervised learning may improve the accuracy of automated MacTel vs non-MacTel binary classification on OCT with limited labeled training data, and these approaches may be applicable to other rare diseases, although further research is warranted.

Difference of central foveal thickness measurement in patients with macular edema using optical coherence tomography in different display modes.

To assess the differences in the measurement of central foveal thickness (CFT) in patients with macular edema (ME) between two display modes (1:1 pixel and 1:1 micron) on optical coherence tomography (OCT). This is a retrospective, cross-sectional study. Group A consisted of participants with well-horizontal OCT B-scan images and group B consisted of participants with tilted OCT B-scan. We manually measured the CFT under the two display modes, and the values were compared statistically using the paired t-test. Spearman's test was used to assess the correlations between the OCT image tilting angle (OCT ITA) and the differences in CFT measurement. The area under the curve (AUC) was calculated to define the OCT ITA cutoff for a defined CFT difference. In group A, the mean CFT in the 1:1 pixel display mode was 420.21 ± 130.61 µm, similar to the mean CFT of 415.27 ± 129.85 µm in the 1:1 micron display mode. In group B, the median CFT in the 1:1 pixel display mode is 409.00 μm (IQR: 171.75 μm) and 368.00 μm (IQR: 149.00 μm) in the 1:1 micron display mode. There were significant differences between the two display modes with the median (IQR) absolute difference and median (IQR) relative difference of 38.00 μm (75.00 μm) and 10.19% (21.91%) (all p = 0.01). The differences in CFT measurement between the two display modes were correlated with the OCT ITA (absolute differences, r = 0.88, p < 0.01; relative differences, r = 0.87, p < 0.01). The AUC for a predefined CFT difference was 0.878 (10 μm), 0.933 (20 μm), 0.938 (30 μm), 0.961 (40 μm), 0.962 (50 μm), and 0.970 (60 μm). In patients with DM, when the OCT B-scan images were well-horizontal, manual CFT measurements under the two display modes were similar, but when the B-scan images were tilted, the CFT measurements were different under the two display modes, and the differences were correlated to the OCT ITA.

Sodium-Glucose Cotransporter-2 Inhibitors Stabilize Coronary Plaques in Acute Coronary Syndrome With Diabetes Mellitus

Sodium-glucose cotransporter-2 inhibitors (SGLT2is) are widely used in cardiology and are effective in treating acute coronary syndrome (ACS). Their effects on unstable plaque in patients with ACS remains unclear. This study aimed to examine the effectiveness of SGLT2is in coronary plaque based on optical coherence tomography (OCT) images and the prognosis of ACS with type 2 diabetes mellitus. This retrospective study included 109 patients in the total cohort and 29 patients in the OCT cohort. Based on SGLT2i administration after ACS, the total cohort was categorized into non-SGLT2i (n = 69) and SGLT2i (n = 40) groups. The OCT cohort had 15 and 14 patients in the non-SGLT2i and SGLT2i groups, respectively. The OCT images of unstable plaque were analyzed in nonstented lesions during ACS catheterization and at the 6-month follow-up. The total cohort was assessed after 1 year for major adverse cardiovascular events, including all-cause mortality, revascularization, cerebrovascular disease, and heart failure hospitalization. SGLT2is improved unstable lesions with a significantly thicker fibrous cap (48 ± 15 μm vs 26 ± 24 μm, p = 0.005), reduced lipid arc (-29 ± 12° vs -18 ± 14°, p = 0.028), higher % decrease in total lipid arc (-35 ± 13% vs -19 ± 18%, p = 0.01), and lower major adverse cardiovascular event incidence (log-rank p = 0.023, hazard ratio 4.72 [1.08 to 20.63]) and revascularization rate (adjusted hazard ratio 6.77 [1.08 to 42.52]) than the non-SGLT2i group. In conclusion, SGLT2is can improve the markers of plaque stability and may improve the prognosis in patients with type 2 diabetes mellitus.

Diverse Data Generation for Retinal Layer Segmentation With Potential Structure Modeling.

Accurate retinal layer segmentation on optical coherence tomography (OCT) images is hampered by the challenges of collecting OCT images with diverse pathological characterization and balanced distribution. Current generative models can produce high-realistic images and corresponding labels without quantitative limitations by fitting distributions of real collected data. Nevertheless, the diversity of their generated data is still limited due to the inherent imbalance of training data. To address these issues, we propose an image-label pair generation framework that generates diverse and balanced potential data from imbalanced real samples. Specifically, the framework first generates diverse layer masks, and then generates plausible OCT images corresponding to these layer masks using two customized diffusion probabilistic models respectively. To learn from imbalanced data and facilitate balanced generation, we introduce pathological-related conditions to guide the generation processes. To enhance the diversity of the generated image-label pairs, we propose a potential structure modeling technique that transfers the knowledge of diverse sub-structures from lowly- or non-pathological samples to highly pathological samples. We conducted extensive experiments on two public datasets for retinal layer segmentation. Firstly, our method generates OCT images with higher image quality and diversity compared to other generative methods. Furthermore, based on the extensive training with the generated OCT images, downstream retinal layer segmentation tasks demonstrate improved results. The code is publicly available at: https://github.com/nicetomeetu21/GenPSM.

ИНТЕГРАЦИЯ МЕХАНИЗМА ВНИМАНИЯ В СВЁРТОЧНУЮ НЕЙРОННУЮ СЕТЬ ДЛЯ УЛУЧШЕНИЯ РЕЗУЛЬТАТОВ КЛАССИФИКАЦИИ ОФТАЛЬМОЛОГИЧЕСКИХ ЗАБОЛЕВАНИЙ

The article considers an approach to improving the classification of diseases based on optical coherence tomography (OCT) images using an attention mechanism integrated into the architecture of a convolutional neural network. An overview and limitations of existing methods of processing OCT images are given. Public datasets are described. The architectural features of the proposed model are shown in detail, including the design of attention blocks and their integration into the convolutional network model. An experimental comparison of the proposed model with traditional convolutional neural networks without an attention mechanism (VGG16, EfficientNetB0) demonstrated improved results on the test dataset (miavgF1 – 0,987, miavgP – 0,998, miavgR – 0,978).

Classification of diabetic maculopathy based on optical coherence tomography images using a Vision Transformer model

PurposeTo develop a Vision Transformer model to detect different stages of diabetic maculopathy (DM) based on optical coherence tomography (OCT) images.MethodsAfter removing images with poor quality, a total of 3319...

OIMHS: An Optical Coherence Tomography Image Dataset Based on Macular Hole Manual Segmentation

Macular holes, one of the most common macular diseases, require timely treatment. The morphological changes on optical coherence tomography (OCT) images provided an opportunity for direct observation of the disease, and accurate segmentation was needed to identify and quantify the lesions. Developments of such algorithms had been obstructed by a lack of high-quality datasets (the OCT images and the corresponding gold standard macular hole segmentation labels), especially for supervised learning-based segmentation algorithms. In such context, we established a large OCT image macular hole segmentation (OIMHS) dataset with 3859 B-scan images of 119 patients, and each image provided four segmentation labels: retina, macular hole, intraretinal cysts, and choroid. This dataset offered an excellent opportunity for investigating the accuracy and reliability of different segmentation algorithms for macular holes and a new research insight into the further development of clinical research for macular diseases, which included the retina, lesions, and choroid in quantitative analyses.

RPGR: Deep Phenotyping and Genetic Characterization With Findings Specific to the 3'-end of ORF15.

To describe a group of patients with retinitis pigmentosa GTPase regulator (RPGR)-related retinopathy with a tapetal-like retinal sheen and corresponding changes in the reflectivity of the ellipsoid zone on optical coherence tomography (OCT) imaging. A retrospective case series of 66 patients with a disease-causing variant in RPGR was performed. An expert examiner, masked to patient demographics, clinical evaluations, and specific RPGR variant, analyzed color fundus photographs for the presence of a tapetal-like retinal sheen and assessed OCT images for the presence of an abnormally broad hyper-reflective band in the outer retina. Longitudinal reflectivity profiles were generated and compared with healthy controls. Twelve patients (18.2%) had a retinal sheen on color images that cosegregated with an abnormally broad hyper-reflective ellipsoid zone band on OCT imaging. Three-fourths of these patients were male, had a cone-rod dystrophy, and had pathogenic RPGR variants located toward the 3'-end of ORF15. This group had a different longitudinal reflectivity profile signature compared with controls. After a period of prolonged dark adaptation, the abnormal hyper-reflective band on OCT became less apparent, and the outer retinal layers adopted a more normal appearance. RPGR-related retinopathy should be considered for males presenting with retinal sheen, abnormal ellipsoid zone hyper-reflectivity, and cone or cone-rod dysfunction on ERG, and pursued with molecular testing. Our results have implications for understanding the role of the C-terminal domain encoded by RPGR ORF15 in the phototransduction cascade. Further, the findings may be important to incorporate into both inclusion criteria and outcome measure developments in future RPGR-related cone or cone-rod dystrophy clinical trials.

OCT-based deep-learning models for the identification of retinal key signs

A new system based on binary Deep Learning (DL) convolutional neural networks has been developed to recognize specific retinal abnormality signs on Optical Coherence Tomography (OCT) images useful for clinical practice. Images from the local hospital database were retrospectively selected from 2017 to 2022. Images were labeled by two retinal specialists and included central fovea cross-section OCTs. Nine models were developed using the Visual Geometry Group 16 architecture to distinguish healthy versus abnormal retinas and to identify eight different retinal abnormality signs. A total of 21,500 OCT images were screened, and 10,770 central fovea cross-section OCTs were included in the study. The system achieved high accuracy in identifying healthy retinas and specific pathological signs, ranging from 93 to 99%. Accurately detecting abnormal retinal signs from OCT images is crucial for patient care. This study aimed to identify specific signs related to retinal pathologies, aiding ophthalmologists in diagnosis. The high-accuracy system identified healthy retinas and pathological signs, making it a useful diagnostic aid. Labelled OCT images remain a challenge, but our approach reduces dataset creation time and shows DL models’ potential to improve ocular pathology diagnosis and clinical decision-making.

Possible depth-resolved reconstruction of shear moduli in the cornea following collagen crosslinking (CXL) with optical coherence tomography and elastography.

Corneal collagen crosslinking (CXL) is commonly used to prevent or treat keratoconus. Although changes in corneal stiffness induced by CXL surgery can be monitored with non-contact dynamic optical coherence elastography (OCE) by tracking mechanical wave propagation, depth dependent changes are still unclear if the cornea is not crosslinked through the whole depth. Here, phase-decorrelation measurements on optical coherence tomography (OCT) structural images are combined with acoustic micro-tapping (AµT) OCE to explore possible reconstruction of depth-dependent stiffness within crosslinked corneas in an ex vivo human cornea sample. Experimental OCT images are analyzed to define the penetration depth of CXL into the cornea. In a representative ex vivo human cornea sample, crosslinking depth varied from ∼100 µm in the periphery to ∼150 µm in the cornea center and exhibited a sharp in-depth transition between crosslinked and untreated areas. This information was used in an analytical two-layer guided wave propagation model to quantify the stiffness of the treated layer. We also discuss how the elastic moduli of partially CXL-treated cornea layers reflect the effective engineering stiffness of the entire cornea to properly quantify corneal deformation.

Comparison between B-Scan and En Face Images for Incomplete and Complete Retinal Pigment Epithelium and Outer Retinal Atrophy

To evaluate and compare the detection of incomplete and complete retinal pigment epithelium and outer retinal atrophy (iRORA and cRORA) assessed on optical coherence tomography (OCT) B-scans versus persistent choroidal hypertransmission defects (hyperTDs) assessed by en face choroidal OCT images. Retrospective, cross-sectional study. Patients with late atrophic age-related macular degeneration imaged on the same day using both Spectralis OCT and Cirrus OCT. Agreement between the B-scan and en face OCT for the detection of hyperTDs and cRORA and iRORA. Two independent graders examined en face OCT and structural OCT to determine the presence and location of hyperTDs, iRORA and cRORA. A total of 239 iRORA and cRORA lesions were detected on the B-scans, and 249 hyperTD lesions were identified on the en face OCT images. There was no significant difference (P = 0.881) in the number of lesions. There was no significant difference in the 134 cRORA lesions identified on B-scans and the 131 hyperTDs detected on en face OCT images (P = 0.125.) A total of 105 iRORA lesions were identified by B-scan assessment; however, 50 of these iRORA lesions met the criteria for a persistent hyperTDs on en face OCT images (P < 0.0001). When considering the topographic correspondence between B-scan and en face OCT detected lesions, the mean percentage of agreement between B-scan detection of cRORA lesions with en face OCT detection was 97.6 % (P=0.125). We observed an overall high agreement between cRORA lesions identified on B-scans and persistent hyperTDs identified on en face OCT. However, en face imaging was able to detect iRORA lesions that had a greatest linear dimension ≥ 250 μm in a non-horizontal en face dimension.

High-resolution characterization of the coagulation and drying processes of whole blood based on optical coherence tomography

Introduction: Escaping whole blood exhibits biochemical and physical coupled mechanisms such as coagulation and drying. However, there is no method for simultaneously monitoring the coagulation and drying procedure.Methods: In this study, a new method based on optical coherence tomography (OCT) combined with speckle variance and thickness is presented for simultaneously capturing spatially high-resolved characteristics of coagulation and drying of whole blood during the procedure. Deep learning based on a convolutional neural network (CNN) is employed for collecting OCT images with a resolution of micron order and quantitatively obtaining pixel-wise information of whole blood.Results and discussion: Then, the pixel-wise thickness map provides high-resolved temporal–spatial dynamics of whole blood during the drying procedure, and the corresponding speckle variance can uncover information of whole blood coagulation. The results demonstrate that coagulation and drying of whole blood have spatially inhomogeneous features. This method could provide the potential for revealing the coupling mechanism between coagulation and drying.

Optical coherence tomography findings in fungal uveitis.

The purpose of the study was to describe the vitreal, retinal, and choroidal features of eyes affected by endogenous endophthalmitis (EE) and evaluate the effects of systemic antifungal drug treatment and pars plana vitrectomy by using spectral domain optical coherence tomography (SD-OCT). Medical records and SD-OCT images of eyes diagnosed with EE at a single uveitis tertiary referral center in Brazil were acquired at the time of diagnosis, after 7 days of high-dose antifungal drug treatment, and at follow-up assessments performed 30days after resolution. Thirteen eyes were enrolled in the study. All patients showed hyperreflective round-shaped lesions on SD-OCT and pre-retinal aggregates. Five eyes responded to antifungal systemic oral drugs despite showing vitreous opacity. The response to treatment was observable on optical coherence tomography (OCT) images. Fungal endophthalmitis showed typical features on SD-OCT, facilitating early diagnosis and treatment despite the absence of vitreous culture or biopsy. This study suggests that OCT images can be used to support diagnosis by physicians who do not have access to vitreoretinal surgery.