Optical Coherence Tomography Classification Research Articles

Privacy Preserving Technology using Federated Learning and Blockchain in protecting against Adversarial Attacks for Retinal Imaging

PurposeCollaboration provides valuable data for robust artificial intelligence (AI) model development. Federated learning (FL) is a privacy enhancing technology that allows collaboration while respecting privacy via the development of models without raw data transfer. However state-of-the-art FL models still face challenges in non-independent and identically distributed (non-i.i.d.) health care settings and remain susceptible to privacy breaches. We propose a novel FL framework coupled with blockchain technology to address these challenges. DesignRetrospective multicohort study Subjects and Methods27,145 images from Singapore, China and Taiwan were used to design a novel FL aggregation method for the detection of myopic macular degeneration (MMD) from fundus photographs and macular disease from optical coherence tomography (OCT) scans in feature distribution skew and label distribution imbalance scenarios. We further performed adversarial attacks (label flipping and clean label). As proof of concept, blockchain was incorporated into FL to demonstrate secure transfer of model updates across collaborating sites. Main Outcome MeasuresWe evaluated our FL model performance in MMD and OCT classification and compared our model against state-of the-art FL and centralized models. ResultsOur FL model showed robust performance with areas under the receiving operating characteristic curves (AUC) of 0.868±0.009 for MMD detection and 0.970±0.012 for OCT macular disease classification. In label flipping attack, our FL model had an AUC of 0.861±0.019, similar to the centralized model (AUC 0.856± 0.015) and higher than other FL models (AUC 0.578–0.819) In clean label attack, our FL model had an AUC of 0.878±0.006 which was comparable to the centralized model (AUC 0.878±0.001) and superior to other state-of-the-art FL models with AUC of 0.529-0.838. Simulation showed that the additional time with blockchain in one global epoch was around 5 seconds. The addition of blockchain to the FL framework was feasible with a minimal impact on model development time. ConclusionsOur proposed FL algorithm overcomes the shortcoming of the traditional FL in non i.i.d. situations and remains robust to against adversarial attacks. The integration of blockchain adds further security during the transfer of model updates. Blockchain-enabled FL can be a trusted platform for collaborative health AI research.

Visual Outcome after Anti-Vascular Epithelial Growth Factor Therapy Using New Classification of Diabetic Macular Edema by Optical Coherence Tomography.

The aim of the study was to examine alterations in visual acuity in patients with diabetic macular edema (DME), classified according to the TCED-HFV optical coherence tomography (OCT) system, following anti-vascular epithelial growth factor (VEGF) therapy. The medical records of patients with DME receiving anti-VEGF therapy were retrospectively reviewed. Patients were divided into four groups according to the TCED-HFV OCT classification. Patient demographic and clinical characteristics and best-corrected visual acuity (BCVA) before and after treatment were compared among the groups. The BCVA before treatment was 0.49 ± 0.18, 0.81 ± 0.41, 0.83 ± 0.41, and 0.82 ± 0.49 in the early DME, advanced DME, severe DME, and atrophic maculopathy groups, respectively. The BCVA in the early DME group was therefore significantly lower than that in the other three groups (p = 0.042). After treatment, the BCVA improved to 0.15 ± 0.17, 0.52 ± 0.31, 0.62 ± 0.32, and 0.69 ± 0.47 in the early DME, advanced DME, severe DME, and atrophic maculopathy groups, respectively (p < 0.005). There were some differences among patients in the four groups in terms of the duration of diabetes, percentage of hemoglobin A1c, and duration of hypertension. The TCED-HFV OCT classification of patients with DME is exact and functional and can allow the severity of DME, and its response to anti-VEGF therapy, to be estimated.

A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification

Artificial intelligence (AI) algorithms, encompassing machine learning and deep learning, can assist ophthalmologists in early detection of various ocular abnormalities through the analysis of retinal optical coherence tomography (OCT) images. Despite considerable progress in these algorithms, several limitations persist in medical imaging fields, where a lack of data is a common issue. Accordingly, specific image processing techniques, such as time–frequency transforms, can be employed in conjunction with AI algorithms to enhance diagnostic accuracy. This research investigates the influence of non-data-adaptive time–frequency transforms, specifically X-lets, on the classification of OCT B-scans. For this purpose, each B-scan was transformed using every considered X-let individually, and all the sub-bands were utilized as the input for a designed 2D Convolutional Neural Network (CNN) to extract optimal features, which were subsequently fed to the classifiers. Evaluating per-class accuracy shows that the use of the 2D Discrete Wavelet Transform (2D-DWT) yields superior outcomes for normal cases, whereas the circlet transform outperforms other X-lets for abnormal cases characterized by circles in their retinal structure (due to the accumulation of fluid). As a result, we propose a novel transform named CircWave by concatenating all sub-bands from the 2D-DWT and the circlet transform. The objective is to enhance the per-class accuracy of both normal and abnormal cases simultaneously. Our findings show that classification results based on the CircWave transform outperform those derived from original images or any individual transform. Furthermore, Grad-CAM class activation visualization for B-scans reconstructed from CircWave sub-bands highlights a greater emphasis on circular formations in abnormal cases and straight lines in normal cases, in contrast to the focus on irrelevant regions in original B-scans. To assess the generalizability of our method, we applied it to another dataset obtained from a different imaging system. We achieved promising accuracies of 94.5% and 90% for the first and second datasets, respectively, which are comparable with results from previous studies. The proposed CNN based on CircWave sub-bands (i.e. CircWaveNet) not only produces superior outcomes but also offers more interpretable results with a heightened focus on features crucial for ophthalmologists.

RETRACTED: Deep learning network (DL-Net) based classification and segmentation of multi-class retinal diseases using OCT scans

The most frequent cause of blindness in industrialized nations is age-related macular degeneration (AMD), particularly in persons over 60. In recent years in this field, three key factors have led to an increase in the workload of experts and the healthcare system: 1) population aging worldwide prevalence, 2) AMD's chronic nature, and 3) more widespread usage of the retinal optical coherence tomography (OCT) imaging technology. The establishment of fully automated diagnosis systems has recently been made possible by recent developments in the deep learning field. The main objective of the research is to develop a new deep learning-based model using OCT images. The performance for classification and segmentation of retinal diseases is improved by the proposed model through effectively capturing inter-scale features and combining these features using convolutional blocks. Then, we classify volumetric OCT images via a new deep learning-based model. For multi-class classification and segmentation of ocular diseases, the OCT scans of the human eye are used in this research to demonstrate a Deep Learning Network (DL-Net) model by automatically recognizing normal, diabetic macular edema (DME), choroidal neovascularization (CNV), AMD, and drusen images. This research introduces the Modified ResNet-50 approach and Image Processing for multiple OCT image classification. This research presents an efficient diagnostic technique for image segmentation based on a Bi-LSTM-based deep recurrent convolutional neural network (DRCNN). The modified SqueezeNet model analyzes the volumetric segmentation of OCT images. The volumetric publicly available datasets are used to analyze the proposed model. The proposed multi-scale structure model outperforms several well-known OCT classification and segmentation frameworks. 99.76% accuracy is achieved by the proposed model respectively. In healthcare settings, the proposed methodology can be used as a diagnostic instrument to help medical experts make better-diagnosing decisions, as shown by the encouraging proposed architecture's qualitative evaluations and quantitative results by generating a confusion matrix.

Bibliometric analysis of artificial intelligence and optical coherence tomography images: research hotspots and frontiers.

To explore the latest application of artificial intelligence (AI) in optical coherence tomography (OCT) images, and to analyze the current research status of AI in OCT, and discuss the future research trend. On June 1, 2023, a bibliometric analysis of the Web of Science Core Collection was performed in order to explore the utilization of AI in OCT imagery. Key parameters such as papers, countries/regions, citations, databases, organizations, keywords, journal names, and research hotspots were extracted and then visualized employing the VOSviewer and CiteSpace V bibliometric platforms. Fifty-five nations reported studies on AI biotechnology and its application in analyzing OCT images. The United States was the country with the largest number of published papers. Furthermore, 197 institutions worldwide provided published articles, where University of London had more publications than the rest. The reference clusters from the study could be divided into four categories: thickness and eyes, diabetic retinopathy (DR), images and segmentation, and OCT classification. The latest hot topics and future directions in this field are identified, and the dynamic evolution of AI-based OCT imaging are outlined. AI-based OCT imaging holds great potential for revolutionizing clinical care.

Efficacy of optical coherence tomography in the triage of women with minor abnormal cervical cytology before colposcopy.

To evaluate the efficacy of Optical Coherence Tomography (OCT) for detecting cervical lesions in women with minor abnormal cytology results (atypical squamous cells of undetermined significance (ASC-US) and low-grade squamous intraepithelial lesion (LSIL)). A prospective study was conducted at gynecologic clinic from Mar 2021 to Sep 2021. The recruited women with cervical cytological findings of ASC-US or LSIL were inspected with OCT before colposcopy-directed cervical biopsy. The diagnostic performance of OCT, alone and in combination with high-risk human papillomavirus (hrHPV) testing were evaluated to detect cervical intraepithelial neoplasia of grade 2 or worse (CIN2+)/CIN3 or worse (CIN3+). The rate of colposcopy referral and the immediate risk of CIN3+ of OCT were calculated. A total of 349 women with minor abnormal cervical cytology results were enrolled. For detection of CIN2+/CIN3+, the sensitivity and NPV of OCT were lower than those of hrHPV testing (CIN2+: 71.3% vs. 95.4%, 89.0% vs. 91.1%, P < 0.001; CIN3+: 75% vs. 93.8%, 96.5% vs. 95.6%, P < 0.001), but the specificity, accuracy and PPV were higher than those of hrHPV testing (CIN2+: 77.5% vs. 15.6%, 75.9% vs. 35.5%, 51.2% vs. 27.3%, P < 0.001; CIN3+: 69.4% vs. 13.6%, 69.9% vs. 20.9%, 19.8% vs. 9.9%, P < 0.001). OCT combined with hrHPV testing (CIN2+: 80.9%; CIN3+: 72.6%) showed higher specificity than that of OCT alone (P < 0.001). The colposcopy referral rate base on OCT classification was lower than that based on hrHPV testing (34.7% vs. 87.1%, P < 0.001). Patients with hrHPV-positive ASC-US and hrHPV-negative LSIL cytology, the immediate CIN3+ risk in OCT negative cases was less than 4%. OCT alone or combination with hrHPV testing shows good performance for detecting CIN2+/CIN3+ in patients with ASC-US/LSIL cytology. OCT is an effective method for colposcopy triage in women with hrHPV-positive ASC-US and hrHPV-negative LSIL cytology.

CNN Classification of Multi-Scale Ensemble OCT for Macular Image Analysis

Computer-Aided Diagnosis (CAD) of retinal pathology is a dynamic medical analysis area. The CAD system in the optical coherence tomography (OCT) is important for the monitoring of ocular diseases because of the heavy utilization of the retinal OCT imaging process. The Multi-Scale Expert Convolution Mixture (MCME) is designed to classify the normal retina. OCT is becoming one of the most popular non-invasive evaluation approaches for retinal eye disease. The amount of OCT is growing and the automation of OCT image analysis is becoming increasingly necessary. The surrogate-aided classification approach is to automatically classify retinal OCT images because of the Convolution Neural Network (CNN). The methods to classify OCT images and macular OCT classification are done by using CNN. Maculopathy is a combined collection of diseases to facilitate the effect of the inner region of the retina identified as the macula. Central Serous Choric Retinopathy (CSCR) and macular edema are the main two types of maculopathies. Numerous researches have focused on the detection of these macular disorders with OCT. It is used to overcome retinal diseases.

External Validation and Clinical Applicability of Two Optical Coherence Tomography-Based Risk Calculators for Detecting Glaucoma.

PurposeTo clinically validate the diagnostic ability of two optical coherence tomography (OCT)-based glaucoma diagnostic calculators (GDCs).MethodsWe conducted a retrospective, consecutive sampling of 76 patients with primary open-angle glaucoma, 107 glaucoma suspects, and 67 controls. Demographics, reliable visual field testing, and macular and optic disc OCT were collected. The reference diagnosis was compared against the probability of having glaucoma obtained from two GDCs derived from multivariate logistic regressions using quantitative and qualitative (GDC1) or only quantitative (GDC2) OCT data. The discrimination (area under the curve [AUC]) and calibration (calibration plots) were compared for both calculators and the best OCT parameters.ResultsGDC2 was able to identify 46.9% more suspects and 14.7% more glaucomatous eyes than GDC1. Both GDCs obtained the highest discriminative ability in glaucomatous eyes (GDC1 AUC = 0.949; GDC2 = 0.943 vs inferior peripapillary retinal nerve fiber layer [pRNFL] = 0.931; P = 0.43). The discriminating ability was not as good for glaucoma suspects, but the GDCs were not inferior to pRNFL (GDC 1 AUC = 0.739; GDC2 = 0.730; inferior pRNFL = 0.760; P = 0.54) and GDC2 was still able to correctly identify up to 30.8% more cases than the conventional OCT classification. Calibration showed risk underestimation for both groups and calculators, but it was better in GDC2 and in patients with glaucoma.ConclusionsOCT-based calculators showed an excellent diagnostic performance in glaucomatous eyes. GDC2 was able to identify approximately 30% more cases than the conventional pRNFL inferior OCT classification in both groups, suggesting a potential role of these composite scores in clinical practice.Translational RelevanceThese OCT-based calculators may improve glaucoma diagnosis in clinical care.

FN-OCT: Disease Detection Algorithm for Retinal Optical Coherence Tomography Based on a Fusion Network.

Optical coherence tomography (OCT) is a new type of tomography that has experienced rapid development and potential in recent years. It is playing an increasingly important role in retinopathy diagnoses. At present, due to the uneven distributions of medical resources in various regions, the uneven proficiency levels of doctors in grassroots and remote areas, and the development needs of rare disease diagnosis and precision medicine, artificial intelligence technology based on deep learning can provide fast, accurate, and effective solutions for the recognition and diagnosis of retinal OCT images. To prevent vision damage and blindness caused by the delayed discovery of retinopathy, a fusion network (FN)-based retinal OCT classification algorithm (FN-OCT) is proposed in this paper to improve upon the adaptability and accuracy of traditional classification algorithms. The InceptionV3, Inception-ResNet, and Xception deep learning algorithms are used as base classifiers, a convolutional block attention mechanism (CBAM) is added after each base classifier, and three different fusion strategies are used to merge the prediction results of the base classifiers to output the final prediction results (choroidal neovascularization (CNV), diabetic macular oedema (DME), drusen, normal). The results show that in a classification problem involving the UCSD common retinal OCT dataset (108,312 OCT images from 4,686 patients), compared with that of the InceptionV3 network model, the prediction accuracy of FN-OCT is improved by 5.3% (accuracy = 98.7%, area under the curve (AUC) = 99.1%). The predictive accuracy and AUC achieved on an external dataset for the classification of retinal OCT diseases are 92 and 94.5%, respectively, and gradient-weighted class activation mapping (Grad-CAM) is used as a visualization tool to verify the effectiveness of the proposed FNs. This finding indicates that the developed fusion algorithm can significantly improve the performance of classifiers while providing a powerful tool and theoretical support for assisting with the diagnosis of retinal OCT.

Optical Coherence Tomography Classification Systems for Diabetic Macular Edema and Their Associations With Visual Outcome and Treatment Responses - An Updated Review.

Optical coherence tomography (OCT) is an invaluable imaging tool in detecting and assessing diabetic macular edema (DME). Over the past decade, there have been different proposed OCT-based classification systems for DME. In this review, we present an update of spectral-domain OCT (SDOCT)-based DME classifications over the past 5 years. In addition, we attempt to summarize the proposed OCT qualitative and quantitative parameters from different classification systems in relation to disease severity, risk of progression, and treatment outcome. Although some OCT-based measurements were found to have prognostic value on visual outcome, there has been a lack of consensus or guidelines on which parameters can be reliably used to predict treatment outcomes. We also summarize recent literatures on the prognostic value of these parameters including quantitative measures such as macular thickness or volume, central subfield thickness or foveal thickness, and qualitative features such as the morphology of the vitreoretinal interface, disorganization of retinal inner layers, ellipsoid zone disruption integrity, and hyperreflec-tive foci. In addition, we discuss that a framework to assess the validity of biomarkers for treatment outcome is essentially important in assessing the prognosis before deciding on treatment in DME. Finally, we echo with other experts on the demand for updating the current diabetic retinal disease classification.

Multi-scale convolutional neural network for automated AMD classification using retinal OCT images

Background and objectiveAge-related macular degeneration (AMD) is the most common cause of blindness in developed countries, especially in people over 60 years of age. The workload of specialists and the healthcare system in this field has increased in recent years mainly due to three reasons: 1) increased use of retinal optical coherence tomography (OCT) imaging technique, 2) prevalence of population aging worldwide, and 3) chronic nature of AMD. Recent advancements in the field of deep learning have provided a unique opportunity for the development of fully automated diagnosis frameworks. Considering the presence of AMD-related retinal pathologies in varying sizes in OCT images, our objective was to propose a multi-scale convolutional neural network (CNN) that can capture inter-scale variations and improve performance using a feature fusion strategy across convolutional blocks. MethodsOur proposed method introduces a multi-scale CNN based on the feature pyramid network (FPN) structure. This method is used for the reliable diagnosis of normal and two common clinical characteristics of dry and wet AMD, namely drusen and choroidal neovascularization (CNV). The proposed method is evaluated on the national dataset gathered at Hospital (NEH) for this study, consisting of 12649 retinal OCT images from 441 patients, and the UCSD public dataset, consisting of 108312 OCT images from 4686 patients. ResultsExperimental results show the superior performance of our proposed multi-scale structure over several well-known OCT classification frameworks. This feature combination strategy has proved to be effective on all tested backbone models, with improvements ranging from 0.4% to 3.3%. In addition, gradual learning has proved to be effective in improving performance in two consecutive stages. In the first stage, the performance was boosted from 87.2%±2.5% to 92.0%±1.6% using pre-trained ImageNet weights. In the second stage, another performance boost from 92.0%±1.6% to 93.4%±1.4% was observed as a result of fine-tuning the previous model on the UCSD dataset. Lastly, generating heatmaps provided additional proof for the effectiveness of our multi-scale structure, enabling the detection of retinal pathologies appearing in different sizes. ConclusionThe promising quantitative results of the proposed architecture, along with qualitative evaluations through generating heatmaps, prove the suitability of the proposed method to be used as a screening tool in healthcare centers assisting ophthalmologists in making better diagnostic decisions.

Statistical modeling of retinal optical coherence tomography using the Weibull mixture model.

In this paper, a novel statistical model is proposed for retinal optical coherence tomography (OCT) images. According to the layered structure of the retina, a mixture of six Weibull distributions is proposed to describe the main statistical features of OCT images. We apply Weibull distribution to establish a more comprehensive model but with fewer parameters that has better goodness of fit (GoF) than previous models. Our new model also takes care of features such as asymmetry and heavy-tailed nature of the intensity distribution of retinal OCT data. In order to test the effectiveness of this new model, we apply it to improve the low quality of the OCT images. For this purpose, the spatially constrained Gaussian mixture model (SCGMM) is implemented. Since SCGMM is designed for data with Gaussian distribution, we convert our Weibull mixture model to a Gaussian mixture model using histogram matching before applying SCGMM. The denoising results illustrate the remarkable performance in terms of the contrast to noise ratio (CNR) and texture preservation (TP) compared to other peer methods. In another test to evaluate the efficiency of our proposed model, the parameters and GoF criteria are considered as a feature vector for support vector machine (SVM) to classify the healthy retinal OCT images from pigment epithelial detachment (PED) disease. The confusion matrix demonstrates the impact of the proposed model in our preliminary study on the OCT classification problem.

Diagnostic accuracy of swept source optical coherence tomography classification algorithms for detection of gonioscopic angle closure

PurposeTo evaluate the performance of swept source optical coherence tomography (SS-OCT) to detect gonioscopic angle closure using different classification algorithms.MethodsThis was a cross-sectional study of 2028 subjects without ophthalmic symptoms...

Study of optical coherence tomography classification and outcome of intravitreal anti-VEGF injection in diabetic macular edema

Diabetic macular edema is the most common cause of vision loss in diabetes. OCT is a non-invasive and non-contact imaging modality that gives high-resolution images of the retina. OCT helps in early diagnosis of macular edema, to classify diabetic macular edema quantitatively, to decide the treatment option and assessing visual prognosis. Aim of this study was to analyse the different patterns of diabetic macular edema in OCT and to observe the visual outcome of intravitreal anti-VEGF injection. All patients who were attending ophthalmology OPD in Tirunelveli medical college hospital with diabetic macular edema from January 2019 to July 2019 were included in the study. Patients with macular edema other than diabetes, OCT with poor signal strength less than 5 were excluded. Patients were subjected to a detailed history, examination with Slit-lamp biomicroscopy, Fundus examination with +90 D lens, Oct was taken in patients with macular edema. Macular cube, OCT classified type of macular edema, Central macular thickness value, quadrant wise distribution of edema, cube average thickness were measured. The number of Anti-VEGF injections and its effect on various types of macular edema was measured. The most common age group is between 41-50 years, males were more than females. The most common type of macular edema is CME (38.5%). There was a significant reduction in macular edema and improvement in visual acuity between pre and post-injection in Cystoid macular edema and diffuse retinal thickness. OCT is an essential tool for the quantitative assessment of macular edema. It helps in deciding treatment modalities, explaining prognosis to patients and predicting the visual outcome.

Automatic Classification of Volumetric Optical Coherence Tomography Images via Recurrent Neural Network

Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologists in the diagnosis and grading of macular diseases. Most existing methods classify 3-D retinal OCT volumes by separately analyzing each single-frame 2-D B-scan, and thus inevitably ignore significant temporal information among B-scans. In this paper, we propose to classify volumetric OCT images via a recurrent neural network (VOCT-RNN) which can fully exploit temporal information among B-scans. Specifically, a deep convolutional neural network is first utilized to automatically extract highly representative features from each individual B-scan of the 3-D retinal OCT images. Then, a long short-term memory network is employed to model the temporal dependencies among B-scans and achieve volumetric OCT classification. The proposed VOCT-RNN can be directly learned from volume-level labels, requiring no detailed annotations at each B-scan. Experimental results on two clinically acquired OCT datasets demonstrate the effectiveness of the proposed VOCT-RNN for volumetric retinal OCT image classification.

DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images

PurposeTo remove blood vessel shadows from optical coherence tomography (OCT) images of the optic nerve head (ONH).MethodsVolume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device for both eyes of 13 subjects. A custom generative adversarial network (named DeshadowGAN) was designed and trained with 2328 B-scans in order to remove blood vessel shadows in unseen B-scans. Image quality was assessed qualitatively (for artifacts) and quantitatively using the intralayer contrast—a measure of shadow visibility ranging from 0 (shadow-free) to 1 (strong shadow). This was computed in the retinal nerve fiber layer (RNFL), the inner plexiform layer (IPL), the photoreceptor (PR) layer, and the retinal pigment epithelium (RPE) layer. The performance of DeshadowGAN was also compared with that of compensation, the standard for shadow removal.ResultsDeshadowGAN decreased the intralayer contrast in all tissue layers. On average, the intralayer contrast decreased by 33.7 ± 6.81%, 28.8 ± 10.4%, 35.9 ± 13.0%, and 43.0 ± 19.5% for the RNFL, IPL, PR layer, and RPE layer, respectively, indicating successful shadow removal across all depths. Output images were also free from artifacts commonly observed with compensation.ConclusionsDeshadowGAN significantly corrected blood vessel shadows in OCT images of the ONH. Our algorithm may be considered as a preprocessing step to improve the performance of a wide range of algorithms including those currently being used for OCT segmentation, denoising, and classification.Translational RelevanceDeshadowGAN could be integrated to existing OCT devices to improve the diagnosis and prognosis of ocular pathologies.

Spectral domain optical coherence tomography classification of diabetic macular edema: a new proposal to clinical practice.

To classify the types of diabetic macular edema (DME) and evaluate its morphological features on spectral domain optical coherence tomography (SD-OCT) and determine correlations between visual acuity and OCT findings. We assessed 406 eyes of 309 patients with a diagnosis of DME retrospectively. Three types based on SD-OCT were identified: diffuse macular edema, cystoid macular edema, and cystoid degeneration. Morphological features such as serous macular detachment (SMD), vitreomacular interface abnormalities (VMAI), hard exudates, photoreceptor status, and correlations between visual acuity and those morphological features were also evaluated by SD-OCT. The most common type of DME was cystoid edema (68.5%). No statistically significant difference was found between groups in sex (P = 0.40), type of diabetes (P = 0.50), or diabetic retinopathy (P=0.78). However, the duration of symptoms and BCVA was significantly lower in the group with cystoid degeneration compared with the group with cystoid edema (P < 0.001) and the group with diffuse macular edema (P < 0.001). In the group with cystoid degeneration compared with the groups with cystoid and diffuse edema, the central fovea and central subfield were significantly thicker (both (P < 0.001), the subfoveal choroid was significantly thinner (P = 0.049), rate of serous macular detachment was significantly lower (P < 0.001), and the rate of outer retinal damage was significantly higher (P < 0.001). Cystoid macular degeneration, which is consistent with poor functional and morphological outcomes, should be differentiated from cystoid macular edema. Serous macular detachment, which is mostly seen in eyes with early stages of DME, should be evaluated as an accompanying morphological finding rather than a type of DME.

Патогенетические факторы формирования морфологических форм диабетического макулярного отека (обзор литературы)

Diabetic macular edema (DME) is a pathological accumulation of fluid in the subretinal or intraretinal spaces of the macular area in patients with diabetic retinopathy and leads to a significant decrease in Central vision. Various types of technical equipment for retinal imaging have led to significant advances in the study of diabetic macular edema. In particular, optical coherence tomography (OCT) can provide linear and structural analysis of macular layers in great detail. The important role of inflammation in the development of changes in the macular region, based on OCT classification, associated with characteristic changes in the retina, has been studied. In this review, we will describe the pathogenesis of diabetic macular edema and discuss its types based on the results of OCT and identify the relationship of various types of (DME) with inflammation. Inflammation plays an important role in the pathogenesis of diabetic macular edema, but its role differs depending on the type of changes detected on OCT.

TCT-117 Novel Optical Coherence Tomography Classification of Intravascular Lesions in Patients With Chronic Thromboembolic Pulmonary Hypertension Treated With Balloon Pulmonary Angioplasty

TCT-117 Novel Optical Coherence Tomography Classification of Intravascular Lesions in Patients With Chronic Thromboembolic Pulmonary Hypertension Treated With Balloon Pulmonary Angioplasty

Automatic Classification of Retinal Optical Coherence Tomography Images With Layer Guided Convolutional Neural Network

Optical coherence tomography (OCT) enables instant and direct imaging of morphological retinal tissue and has become an essential imaging modality for ophthalmology diagnosis. As one of the important morphological retinal characteristics, the structural information of retinal layers provides meaningful diagnostic information and is closely related to several retinal diseases. In this letter, we propose a novel layer guided convolutional neural network (LGCNN) to identify normal retina and three common types of macular pathologies, namely, diabetic macular edema, drusen, and choroidal neovascularization. Specifically, an efficient segmentation network is first employed to generate the retinal layer segmentation maps, which can delineate two lesion-related retinal layers associated with the meaningful retinal lesions. Then, two well-designed subnetworks in LGCNN are utilized to integrate the information of two lesion-related layers. Consequently, LGCNN can efficiently focus on the meaningful lesion-related layer regions to improve OCT classification. The experimental results conducted on two clinically acquired datasets demonstrate the effectiveness of the proposed method.