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Purpose: To compare detection rates of microaneurysms (MAs) on high‐speed megahertz optical coherence tomography angiography (MHz‐OCTA), fluorescein angiography (FA) and colour fundus photography (CF) in patients with diabetic retinopathy (DR). Methods: For this exploratory cross‐sectional study, MHz‐OCTA data were acquired with a swept‐source OCT prototype (A‐scan rate: 1.7 MHz), and FA and CF imaging was performed using Optos® California. MA count was manually evaluated on en face MHz‐OCTA/FA/CF images within an extended ETDRS grid. Detectability of MAs visible on FA images was evaluated on corresponding MHz‐OCTA and CF images. MA distribution and leakage were correlated with detectability on OCTA and CF imaging. Results: 47 eyes with severe DR (n = 12) and proliferative DR (n = 35) were included. MHz‐OCTA and CF imaging detected on average 56% and 36% of MAs, respectively. MHz‐OCTA detection rate was significantly higher than CF (p < 0.01). The combination of MHz‐OCTA and CF leads to an increased detection rate of 70%. There was no statistically significant association between leakage and MA detectability on OCTA (p = 0.13). For CF, the odds of detecting leaking MAs were significantly lower than non‐leaking MAs (p = 0.012). Using MHz‐OCTA, detection of MAs outside the ETDRS grid was less likely than MAs located within the ETDRS grid (outer ring, p < 0.01; inner ring, p = 0.028). No statistically significant difference between rings was observed for CF measurements. Conclusions: More MAs were detected on MHz‐OCTA than on CF imaging. Detection rate was lower for MAs located outside the macular region with MHz‐OCTA and for leaking MAs with CF imaging. Combining both non‐invasive modalities can improve MA detection. [ABSTRACT FROM AUTHOR]

(1) Underlying Diabetic Retinopathy (DR) is the primary cause of poor vision in young adults. There are automatic image reading systems that can aid screening for DR. (2) Methods: Using our automatic reading system we have counted the number of microaneurysms and hemorrhages in the four quadrants of the ETDRS grid and evaluated the differences between them according to the type of DR. The study was carried out using data from two different databases, MESSIDOR and MIRADATASET. (3) Results: The majority of microaneurysms and hemorrhages are found in the temporal and inferior quadrants of the ETDRS grid. Differences are significant with respect to the other two quadrants at p < 0.001. Differences between the type of DR show that severe-DR has a greater number of microaneurysms and hemorrhages in the temporal and inferior quadrant, being significant at p < 0.001. (4) Conclusions: The count of microaneurysms and hemorrhages is higher in the temporal and inferior quadrants in all types of DR, and those differences are more important in the case of severe-DR. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) stands as a prevalent complication in the eye resulting from diabetes mellitus, predominantly associated with high blood sugar levels and hypertension as individuals age. DR is a severe microvascular complication of both type I and type II diabetes mellitus and the leading cause of vision impairment. The critical approach to combatting and halting the advancement of DR lies in effectively managing blood glucose and blood pressure levels in diabetic patients; however, this is seldom achieved. Both human and animal studies have revealed the intricate nature of this condition involving various cell types and molecules. Aside from photocoagulation, the sole therapy targeting VEGF molecules in the retina to prevent abnormal blood vessel growth is intravitreal anti-VEGF therapy. However, a substantial portion of cases, approximately 30-40%, do not respond to this treatment. This review explores distinctive pathophysiological phenomena of DR and identifiable cell types and molecules that could be targeted to mitigate the chronic changes occurring in the retina due to diabetes mellitus. Addressing the significant research gap in this domain is imperative to broaden the treatment options available for managing DR effectively. [ABSTRACT FROM AUTHOR]

Burkholderia pseudomallei, a Gram-negative bacterium, is the cause of Melioidosis, a disease that is known to cause significant morbidity and mortality in Southeast Asia. It is also classified as a potential biological warfare agent and is considered a category B priority pathogen. Despite being common in these areas, melioidosis is often underdiagnosed due to lack of awareness of its clinical and microbiological characteristics, limited health-care resources, and the varied ways in which it can present. It is important to recognize the clinical features of melioidosis early on and properly manage the disease to reduce its impact.

Modern Medical diagnosis has a significant reliance on systematic image analysis for rational prognosis. With the advancement of computer vision methods, supported by deep learning algorithms, the non-invasive and early-stage detection of various diseases is a significant area of research. Amongst various image-based disease identification scopes, in this paper it is focused on Diabetic Retinopathy (DR), which is an eye disease caused by diabetes that can even lead to blindness. Therefore, early detection is critical to prevent visual disturbances from such diseases. In this study, the objective is to identify diabetic retinopathy by analysing fundus images using deep learning methods. Identifying diabetic retinopathy from fundus images, requires considerable improvement of image quality. In this work the retina images obtained by the fundus camera is improved by Generative Adversarial Network(GAN) for specific analysis. This study aimed to detect lesions using mask-RCNN techniques, where pretrained models such as R-50, R-101, and X-101 are utilized for robust segmentation and masking of the damaged regions of the fundus images. As the lesions are pathologically classified into two categories: 'exudates' and 'microaneurysms', similarly the input images are also mapped into two categories. Evaluation is based on the mean average precision (MAP) and the findings infer the conclusion. The comprehensive set of real-life retina images obtained from the clinical dataset benchmarked with the proposed mechanism and it is found that the proper segmentation and improvisation of fundus images by applying GAN have considerably enhanced the degree of accuracy in the Region-of-interest(ROI) bounding box for exact identification of Diabetic Retinopathy, along with reporting on stage of the disease. The aim of the work is well established in implementing the algorithm and finding the result for mapping it into the automated classification of fundus images in batches where the X-101 model seen to perform better in terms of finding Bounding Box Average Precision for both exudates (75.20%) and microaneurysms (67.202%) followed by Segmented Region Average Precision for exudates (62.363%) and microaneurysms (57.690%). The obtained results may reduce human intervention and classify a large number of input images in field-level examination in a faster manner. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy stands out as one of the highly prevalent causes of vision loss in working people worldwide. In computer vision, deep learning based strategies are seen as a viable solution for efficient diabetic retinopathy detection. We present a UNet-based deep learning architecture for diabetic retinopathy segmentation of blood vessels, exudates, and microaneurysms. Traditional methods often consider the features only from the last convolution unit and discard the remaining features, resulting in low-quality feature maps. However, boundary information plays important role in medical image segmentation. To overcome this, we introduce a skip connection mechanism to concatenate all attributes from each layer. Additionally, we utilize an upsampling layer to aggregate the features at the final sigmoid layer. Finally, we apply channel and spatial attention mechanisms to generate the semantic feature map. Therefore, the proposed approach overcomes the issues of existing methods by incorporating dense skip connection along with channel and spatial attention mechanism which helps to retain the substantial information of image. We tested proposed approach on several publicly available datasets such as IDRiD, DIARETDB1, STARE, ChaseDB1, DRIVE, and HRF datasets. The comparative analysis shows that the proposed approach achieves superior performance, with an average accuracy of 98.10%, average sensitivity of 97.60%, and average specificity of 98.2% for segmentation. [ABSTRACT FROM AUTHOR]

Background. Today, it is recognized that in the pathogenesis of diabetic retinopathy (DR), along with the development of vascular disorders, neuron dysfunction and neurodegeneration processes independently develop in the retina. The purpose was to determine the expression of neuron-specific enolase (NSE) in the retina at the initial stages of the experimental DR and the effect on it of the benzodiazepine receptor agonist carbacetam. Materials and methods. DR was modeled in 45 three-month-old male Wistar rats by a single injection of streptozotocin (50 mg/kg; Sigma-Aldrich Co, China). The rats were divided into 3 groups: control, with the introduction of insulin (30 units; Novo Nordisk A/S, Denmark) and with the introduction of insulin and carbacetam (5 mg/kg). NSE in the retina was detected immunohistochemically (Thermo Fisher Scientific, USA). Results. In the retina of rats with experimental DR, ganglion cells, neurons of the inner and outer nuclear layers, and the inner segment of photoreceptors had NSE-positive staining. The intensity of staining in the control group increased until the day 14, after which it remained at a low level. In the inner nuclear layer, a background staining was observed against which numerous nerve fibers were visualized. After 2 months in the control group, the general intensity of staining in retinal neurons was weak, NSE-positive cells were grouped around microaneurysms, which at this time were formed on the inner surface of the retina. Numerous cellular fibrous proliferations were formed in the outer nuclear layer. The use of insulin contributed to the preservation of NSE-positively stained cells in the layers of the retina while reducing the intensity of background staining. The use of insulin with carbacetam enhanced this trend: NSE-positive staining was detected in ganglion cells, cells of the inner nuclear layer, and the inner segment of photoreceptors. At the same time, the formation of microaneurysms and cell proliferations was not observed. Conclusions. This study shows the protective effect of carbacetam for the development of neurodegeneration processes and the preservation of NSE-positive retinal neurons in DR. [ABSTRACT FROM AUTHOR]

IntroductionMicroaneurysms serve as early signs of diabetic retinopathy, and their accurate detection is critical for effective treatment. Due to their low contrast and similarity to retinal vessels, distinguishing microaneurysms from background noise and retinal vessels in fluorescein fundus angiography (FFA) images poses a significant challenge.MethodsWe present a model for automatic detection of microaneurysms. FFA images were pre-processed using Top-hat transformation, Gray-stretching, and Gaussian filter techniques to eliminate noise. The candidate microaneurysms were coarsely segmented using an improved matched filter algorithm. Real microaneurysms were segmented by a morphological strategy. To evaluate the segmentation performance, our proposed model was compared against other models, including Otsu's method, Region Growing, Global Threshold, Matched Filter, Fuzzy c-means, and K-means, using both self-constructed and publicly available datasets. Performance metrics such as accuracy, sensitivity, specificity, positive predictive value, and intersection-over-union were calculated.ResultsThe proposed model outperforms other models in terms of accuracy, sensitivity, specificity, positive predictive value, and intersection-over-union. The segmentation results obtained with our model closely align with benchmark standard. Our model demonstrates significant advantages for microaneurysm segmentation in FFA images and holds promise for clinical application in the diagnosis of diabetic retinopathy.ConclusionThe proposed model offers a robust and accurate approach to microaneurysm detection, outperforming existing methods and demonstrating potential for clinical application in the effective treatment of diabetic retinopathy.

(1) Underlying Diabetic Retinopathy (DR) is the primary cause of poor vision in young adults. There are automatic image reading systems that can aid screening for DR. (2) Methods: Using our automatic reading system we have counted the number of microaneurysms and hemorrhages in the four quadrants of the ETDRS grid and evaluated the differences between them according to the type of DR. The study was carried out using data from two different databases, MESSIDOR and MIRADATASET. (3) Results: The majority of microaneurysms and hemorrhages are found in the temporal and inferior quadrants of the ETDRS grid. Differences are significant with respect to the other two quadrants at p < 0.001. Differences between the type of DR show that severe-DR has a greater number of microaneurysms and hemorrhages in the temporal and inferior quadrant, being significant at p < 0.001. (4) Conclusions: The count of microaneurysms and hemorrhages is higher in the temporal and inferior quadrants in all types of DR, and those differences are more important in the case of severe-DR.

Microaneurysms is the first stage of diabetic retinopathy (DR) and it plays a vital role in the computerized diagnosis. However, it is difficult to automatically detect microaneurysms in fundus images due to the complicated background and various illumination reasons. The motivation behind this, is the number of increases in diabetic patients is very large when compared with the number of ophthalmologists. The FSCA‐UNET (Frequency Spatial Channel Attention UNET) segmentation model, is proposed and it is an improvement over UNET. We first use the frequency channel attention mechanism to analyze the features that were extracted from the first stage of the convolution layer, and we obtain good results. Then, we included a spatial attention map with frequency attention, also known as FSCA‐UNET, which makes use of inter‐spatial connections between features. Our deep neural model with an encoder‐decoder structure termed FSCA‐UNET produced more accurate results. Our novel algorithm outdated the performance measures of the existing segmentation algorithms. The proposed segmentation algorithm was trained and tested on Indian Diabetic Retinopathy Image Dataset (IDRiD), and E‐ophtha Dataset and we got promising results in terms of sensitivity, specificity, dice coefficient, precision, F1 score, and accuracy. [ABSTRACT FROM AUTHOR]

Persistent diabetes results in diabetic retinopathy (DR), affecting the retinal blood vessels (BVs), causing lesions. Rapid identification and treatment are crucial for preventing vision loss. Low ophthalmologist to patient's ratio results automating the DR detection a dire need. Therefore, a feature extraction method is proposed using a Mamdani fuzzy inference system (FIS) classifier for efficient identification. Mathematical morphology, region growth, and 12-region search computation have been used to mask the BVs and macula. The masked green plane image was subjected to Nick's thresholding to locate the dark lesions, from which statistical features were extracted and employed in the Mamdani FIS to classify the DR. On evaluating a total of 909 images from the MESSIDOR database shows, average sensitivity, specificity, area under the curve receiver operating characteristics, and accuracy of 99.7%, 99.8%, 99.4%, and 99.6%, respectively. The algorithm performs well in real-time images from two local hospitals. The proposed technique provides a powerful yet flexible tool for improving the diagnosis and treatment of this condition that threatens vision, as it combines the strengths of fuzzy logic, clinical knowledge, and adaptive learning to provide precise, timely, non-invasive, and economical solutions. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is an eye disease caused by retinal damage induced by the long-term illness of diabetes mellitus. In the early stages, DR may show no symptoms or only minor vision difficulties, but it can eventually result in vision loss if not treated early. Manual diagnosis of Diabetic retinopathy requires many physical tests like visual acuity, pupil dilation, and tonometry. But the issue with these tests is that they consume more time, cost, and effort, therefore affecting patients. Also, it is challenging to identify the disease through these tests during the earlier stage of the disease due to its tiny structure. Since the current system is highly dependent on the resources available and time-consuming, better alternatives are being sought. The proposed work aims to develop an automated model for detecting the early stage of DR detection based on red lesions present in the retinal images. The pre-processing is carried out to remove the noise present, improve local contrast levels in the image, and is further subjected to UNet architecture for the semantic segmentation of red lesions. Advanced Convolutional Layer Architecture in U-Net was used to support pixel-level class labeling, which is much needed in medical segmentation. The segmented images obtained from the red lesion detection were then used as the input to feed the convolution neural network to train and classify the input images to their corresponding severity classes. Four publicly available datasets, IDRiD, DIARETDB1, MESSIDOR, and STARE, were used in the proposed model to evaluate its performance. On working with the IDRID dataset, the specificity and sensitivity of the proposed Red Lesion detection system were observed as 99% and 89%, respectively, with an accuracy of 95.65%. The specificity, sensitivity, and accuracy obtained for the Diabetic retinopathy severity classification system were 93.8%, 92.3%, and 94%, respectively, on working with the MESSIDOR dataset. [ABSTRACT FROM AUTHOR]

Abstract Purpose To determine whether the rare NLRP3-Associated Autoinflammatory Disease (NLRP3-AID) is associated with retinal changes and to assess the ocular involvement. Methods A retrospective cohort study of 20 patients(40 eyes) diagnosed with rare NLRP3-AID at Peking Union Medical College Hospital, from April 2015 to August 2022. Patients underwent a comprehensive ophthalmological examination, including visual acuity, intraocular pressure examination, slit-lamp examination, fundus photography, optical coherence tomography(OCT), and fluorescence angiography (FA). Some patients also underwent optical coherence tomography angiography (OCTA). Results This study analyzed 40 eyes of 20 patients (11 [55.0%] male; median age, 25.0 years [range, 12–52 years]) and 13 patients (26 eyes, 65%) demonstrated ocular involvement. The most common ophthalmologic manifestation was conjunctivitis (22 eyes, 84.6%), followed by papilledema (14 eyes, 53.8%), retinopathy (10 eyes, 38.5%), optic atrophy (6 eyes, 23.1%), uveitis (4 eyes, 15.4%), reduced pupil light reflex (3 eyes, 11.5%) and cataracts (2 eyes, 7.7%). Ocular involvement was bilateral in 11 patients (55.0%). Five kinds of retinal lesions were seen in 5 patients (10 eyes, 25%) with NLRP3-AID, including peripheral retinal vascular leakage, microaneurysms, macular ischemia, macular epiretinal membrane formation and drusen. Conclusions Peripheral retinal vascular leakage, macular ischemia, microaneurysms and drusen are newly identified retinal findings in patients with NLRP3-AID, which suggests the importance of detailed retinal examination in these patients.

Purpose: To determine whether the rare NLRP3-Associated Autoinflammatory Disease (NLRP3-AID) is associated with retinal changes and to assess the ocular involvement. Methods: A retrospective cohort study of 20 patients(40 eyes) diagnosed with rare NLRP3-AID at Peking Union Medical College Hospital, from April 2015 to August 2022. Patients underwent a comprehensive ophthalmological examination, including visual acuity, intraocular pressure examination, slit-lamp examination, fundus photography, optical coherence tomography(OCT), and fluorescence angiography (FA). Some patients also underwent optical coherence tomography angiography (OCTA). Results: This study analyzed 40 eyes of 20 patients (11 [55.0%] male; median age, 25.0 years [range, 12–52 years]) and 13 patients (26 eyes, 65%) demonstrated ocular involvement. The most common ophthalmologic manifestation was conjunctivitis (22 eyes, 84.6%), followed by papilledema (14 eyes, 53.8%), retinopathy (10 eyes, 38.5%), optic atrophy (6 eyes, 23.1%), uveitis (4 eyes, 15.4%), reduced pupil light reflex (3 eyes, 11.5%) and cataracts (2 eyes, 7.7%). Ocular involvement was bilateral in 11 patients (55.0%). Five kinds of retinal lesions were seen in 5 patients (10 eyes, 25%) with NLRP3-AID, including peripheral retinal vascular leakage, microaneurysms, macular ischemia, macular epiretinal membrane formation and drusen. Conclusions: Peripheral retinal vascular leakage, macular ischemia, microaneurysms and drusen are newly identified retinal findings in patients with NLRP3-AID, which suggests the importance of detailed retinal examination in these patients. [ABSTRACT FROM AUTHOR]

Abstract Background Focal laser photocoagulation is an important treatment option for diabetic macular edema (DME). This study aimed to examine the retinal sensitivity (RS) and morphological changes at the coagulated site after direct photocoagulation of microaneurysms (MAs) in patients with DME using a navigated laser photocoagulator with a short-pulse duration of 30 ms. Methods Images of early-phase fluorescein angiography were merged with images from the optical coherence tomography (OCT) map with 9 Early Treatment Diabetic Retinopathy Study grid circles, and MAs inside the edema area were selected for direct photocoagulation. The best-corrected visual acuity (BCVA), parameters of the OCT map including central retinal thickness and retinal thickness in edema range, central RS, and RS in the edema area were assessed at 1 and 3 months after the laser treatment. The RS points that overlapped with the laser spots were identified by merging the Navilas’ digital treatment reports and the microperimetry images. Results Seventeen eyes from 14 patients were studied. The mean retinal thickness in the edema range decreased at 3 months compared with pretreatment (P = 0.042), but the BCVA, central retinal thickness, central RS, and RS in the edema area remained unchanged. Overall, 32 of 400 sensitivity points overlapped with the laser-coagulated spots. The mean RS at these spots were 22.4 ± 5.3 dB at 1 month and 22.5 ± 4.8 dB at 3 months, with no significant change from the baseline of 22.7 ± 3.5 dB. Conclusions Retinal thickness improved in the coagulated edema area without a decrease in RS after direct photocoagulation of MAs with a short 30-ms pulse using Navilas. This promising therapeutic strategy for DME is effective and minimally invasive.

Purpose: To characterize the relationship between diabetic macular ischemia (DMI) delineated by optical coherence tomography angiography (OCTA) and microaneurysms (MAs) identified by fundus fluorescein angiography (FFA). Methods: Patients with diabetic retinopathy (DR) who underwent OCTA and FFA were retrospectively identified. FFA images were cropped and aligned with their respective OCTA images using i2k Align Retina software (Dual-Align, Clifton Park, NY, USA). Foveal avascular zone (FAZ) and ischemic areas were manually delineated on OCTA images, and MAs were marked on the corresponding FFA images before overlaying paired scans for analysis (ImageJ; National Institutes of Health, Bethesda, MD, USA). Results: Twenty-eight eyes of 20 patients were included. The average number of MAs identified in cropped FFA images was 127 ± 42. More DMI was noted in the superficial capillary plexus (SCP; 36 ± 13%) compared to the deep capillary plexus (DCP; 28 ± 14%, P < 0.001). Similarly, more MAs were associated with ischemic areas in SCP compared to DCP (92.0 ± 35.0 vs. 76.8 ± 36.5, P < 0.001). Most MAs bordered ischemic areas; fewer than 10% localized inside these regions. As DMI area increased, so did associated MAs (SCP: r = 0.695, P < 0.001; DCP: r = 0.726, P < 0.001). Density of MAs surrounding FAZ (7.7 ± 6.0 MAs/mm2) was similar to other DMI areas (SCP: 7.0 ± 4.0 MAs/mm2, P = 0.478; DCP: 9.2 ± 10.9 MAs/mm2, P = 0.394). Conclusion: MAs identified in FFA strongly associate with, and border areas of, DMI delineated by OCTA. Although more MAs are localized to SCP ischemia, the concentration of MAs associated with DCP ischemia is greater. By contrast, few MAs are present inside low-flow regions, likely because capillary loss is associated with their regression.

Diabetic retinopathy causes blindness in diabetics. Early identification and frequent screening of diabetic retinopathy can slow disease progression and visual loss. Retinal lesions result from diabetic retinopathy. Dark and brilliant retinal lesions predominate. Color, shape, and size distinguish lesions. Exudates are bright, while microaneurysms (MAs) and hemorrhages (HEMs) are dark. This study presents a retinal lesion screening method for diabetic retinopathy. The data is saturated at low and high intensities; picture intensity values are adjusted to enhance contrast. This study presents a unique rhombus multilevel retinal image segmentation method. In the proposed study, preprocessing, segmentation algorithms, morphological operation,median filter and gradient are all designed as parts of an effective automated system. With 40 photos, the recommended method produced accuracy and specificity of 99.9% and 99.5%, respectively. [ABSTRACT FROM AUTHOR]

Intravitreal injection of triamcinolone acetonide (TA) is essential for clinical treatment in patients who insufficiently respond to vascular endothelial factor inhibitors for diabetic macular edema (DME). The aim of this study was to investigate microvascular changes treated with TA using optical coherence tomography angiography (OCTA). After TA in twelve eyes of eleven patients with central retinal thickness (CRT), there was a 20% or more reduction observed. Visual acuity, the number of microaneurysms, vessel density, and the foveal avascular zone (FAZ) area were compared before and at 2 months after TA. At baseline, the number of microaneurysms was 2.1 ± 1.1 in the superficial capillary plexuses (SCP) and 2.0 ± 1.1 in the deep capillary plexuses (DCP), with a significant decrease post-treatment to 1.0 ± 1.0 for SCP and 0.8 ± 0.8 for DCP (SCP; p = 0.018, DCP; p = 0.008). There was significant enlargement of the FAZ area from 0.28 ± 0.11 mm2 to 0.32 ± 0.14 mm2 (p = 0.041). There was no significant difference in the visual acuity and vessel density of SCP and DCP. Results indicated that OCTA was useful for the evaluation of qualitative and morphological retinal microcirculation and that intravitreal TA may decrease microaneurysms. [ABSTRACT FROM AUTHOR]

Diabetes is a long-term condition in which the pancreas quits producing insulin or the body's insulin isn't utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is one of the retinal disorders and the leading cause of blindness worldwide. Microaneurysms (MA) is the first clinical indication of DR, and the detection of MA helps in early diagnosis. The retinal fundus image analysis helps screen DR through MA detection. In general, the MA detection method consists of preprocessing, enhancement, and classification stages. Preprocessing is crucial to improve the retinal features and reduce the imaging artifacts. Reducing these artifacts is one of the challenging research problems in retinal fundus image analysis. In this paper, a novel improved Non-Local Mean filter (INLMF) is proposed to remove the imaging artifacts. The proposed method is tested on publicly available databases and images collected from Hospital. The proposed method has achieved the best performance metric than the state-of-the-art. The computational time per image is 6.2 sec which is less than other methods. [ABSTRACT FROM AUTHOR]

Background: Focal laser photocoagulation is an important treatment option for diabetic macular edema (DME). This study aimed to examine the retinal sensitivity (RS) and morphological changes at the coagulated site after direct photocoagulation of microaneurysms (MAs) in patients with DME using a navigated laser photocoagulator with a short-pulse duration of 30 ms. Methods: Images of early-phase fluorescein angiography were merged with images from the optical coherence tomography (OCT) map with 9 Early Treatment Diabetic Retinopathy Study grid circles, and MAs inside the edema area were selected for direct photocoagulation. The best-corrected visual acuity (BCVA), parameters of the OCT map including central retinal thickness and retinal thickness in edema range, central RS, and RS in the edema area were assessed at 1 and 3 months after the laser treatment. The RS points that overlapped with the laser spots were identified by merging the Navilas' digital treatment reports and the microperimetry images. Results: Seventeen eyes from 14 patients were studied. The mean retinal thickness in the edema range decreased at 3 months compared with pretreatment (P = 0.042), but the BCVA, central retinal thickness, central RS, and RS in the edema area remained unchanged. Overall, 32 of 400 sensitivity points overlapped with the laser-coagulated spots. The mean RS at these spots were 22.4 ± 5.3 dB at 1 month and 22.5 ± 4.8 dB at 3 months, with no significant change from the baseline of 22.7 ± 3.5 dB. Conclusions: Retinal thickness improved in the coagulated edema area without a decrease in RS after direct photocoagulation of MAs with a short 30-ms pulse using Navilas. This promising therapeutic strategy for DME is effective and minimally invasive. [ABSTRACT FROM AUTHOR]

According to the results of DSA, 118 microaneurysms were divided into 40 true microaneurysms and 78 pseudo microaneurysms. And there are more common anterior communicating artery aneurysms of ruptured microaneurysms, indicating that tiny anterior communicating artery aneurysms have a high risk of rupture. There was a significant difference in age between pseudomicroaneurysm group and true microaneurysm group (Training goup: p < 0.001, Validation goup: p < 0.001), but sex was not significantly different between pseudomicroaneurysm group and true microaneurysm group (Training goup: p = 0.230. [Extracted from the article]

Background and Objectives: This study aims to elucidate the role of microaneurysms (MAs) in the pathogenesis and treatment of diabetic retinopathy (DR) and diabetic macular edema (DME), the major causes of acquired visual impairment. Materials and Methods: We synthesized the relevance of findings on the clinical characteristics, pathogenesis, and etiology of MAs in DR and DME and their role in anti-vascular endothelial growth factor (VEGF) therapy. Results: MAs, a characteristic feature in DR and DME, can be detected by fluorescein angiography, optical coherence tomography (OCT) and OCT angiography. These instrumental analyses demonstrated a geographic and functional association between MA and ischemic areas. MA turnover, the production and loss of MA, reflects the activity of DME and DR. Several cytokines are involved in the pathogenesis of MAs, which is characterized by pericyte loss and endothelial cell proliferation in a VEGF-dependent or -independent manner. Ischemia and MAs localized in the deep retinal layers are characteristic of refractory DME cases. Even in the current anti-VEGF era, laser photocoagulation targeting MAs in the focal residual edema is still an effective therapeutic tool, but it is necessary to be creative in accurately identifying the location of MAs and performing highly precise and minimally invasive coagulation. Conclusions: MAs play a distinctive and important role in the pathogenesis of the onset, progression of DR and DME, and response to anti-VEGF treatment. Further research on MA is significant not only for understanding the pathogenesis of DME but also for improving the effectiveness of treatment. [ABSTRACT FROM AUTHOR]

Introduction: The study aimed to determine the effect of the scanning area used for high-speed ultra-widefield swept-source optical coherence tomography angiography (SS-OCTA) on the detection rate of diabetic retinopathy (DR) lesions. Methods: This prospective, observational study involved diabetic patients between October 2021 and April 2022. The participants underwent a comprehensive ophthalmic examination and high-speed ultra-widefield SS-OCTA using a 24 mm × 20 mm scanning protocol. A central area denoted as “12 mm × 12 mm-central” was extracted from the 24 mm × 20 mm image, and the remaining area was denoted as “12 mm~24mm-annulus.” The rates of detection of DR lesions using the two scanning areas were recorded and compared. Results: In total, 172 eyes (41 eyes with diabetes mellitus without DR, 40 eyes with mild to moderate non-proliferative diabetic retinopathy (NPDR), 51 eyes with severe NPDR, and 40 eyes with proliferative diabetic retinopathy (PDR) from 101 participants were included. The detection rates of microaneurysms (MAs), intraretinal microvascular abnormalities (IRMAs), and neovascularization (NV) for the 12 mm × 12 mm central and 24 mm × 20 mm images were comparable (p > 0.05). The detection rate of NPAs for the 24 mm × 20 mm image was 64.5%, which was significantly higher than that for the 12 mm × 12 mm central image (52.3%, p < 0.05). The average ischemic index (ISI) was 15.26% for the 12 mm~24mm-annulus, which was significantly higher than that for the 12 mm ×12 mm central image (5.62%). Six eyes had NV and 10 eyes had IRMAs that only existed in the 12 mm~24mm-annulus area. Conclusions: The newly developed high-speed ultra-widefield SS-OCTA can capture a 24 mm × 20 mm retinal vascular image during a single scan, which improves the accuracy of detecting the degree of retinal ischemia and detection rate of NV and IRMAs. [ABSTRACT FROM AUTHOR]

IntroductionThe study aimed to determine the effect of the scanning area used for high-speed ultra-widefield swept-source optical coherence tomography angiography (SS-OCTA) on the detection rate of diabetic retinopathy (DR) lesions.MethodsThis prospective, observational study involved diabetic patients between October 2021 and April 2022. The participants underwent a comprehensive ophthalmic examination and high-speed ultra-widefield SS-OCTA using a 24 mm × 20 mm scanning protocol. A central area denoted as “12 mm × 12 mm-central” was extracted from the 24 mm × 20 mm image, and the remaining area was denoted as “12 mm~24mm-annulus.” The rates of detection of DR lesions using the two scanning areas were recorded and compared.ResultsIn total, 172 eyes (41 eyes with diabetes mellitus without DR, 40 eyes with mild to moderate non-proliferative diabetic retinopathy (NPDR), 51 eyes with severe NPDR, and 40 eyes with proliferative diabetic retinopathy (PDR) from 101 participants were included. The detection rates of microaneurysms (MAs), intraretinal microvascular abnormalities (IRMAs), and neovascularization (NV) for the 12 mm × 12 mm central and 24 mm × 20 mm images were comparable (p > 0.05). The detection rate of NPAs for the 24 mm × 20 mm image was 64.5%, which was significantly higher than that for the 12 mm × 12 mm central image (52.3%, p < 0.05). The average ischemic index (ISI) was 15.26% for the 12 mm~24mm-annulus, which was significantly higher than that for the 12 mm × 12 mm central image (5.62%). Six eyes had NV and 10 eyes had IRMAs that only existed in the 12 mm~24mm-annulus area.ConclusionsThe newly developed high-speed ultra-widefield SS-OCTA can capture a 24 mm × 20 mm retinal vascular image during a single scan, which improves the accuracy of detecting the degree of retinal ischemia and detection rate of NV and IRMAs.

Purpose The aim of this study was to evaluate the changes in retinal vascular plexuses and choriocapillaris in type-2 diabetes mellitus (DM) patients without clinical diabetic retinopathy (DR) and to compare them with healthy controls and to identify early preclinical biomarkers for DR using optical coherence tomography angiography (OCTA). Patients and methods This is a prospective cross-sectional study that included 68 eyes (34 eyes of type-2 diabetic patients without DR and 34 eyes of healthy controls). Using OCTA, the vessel density (VD) in the superficial and deep capillary plexuses, macular thickness, foveal avascular zone (FAZ) area, and choriocapillaris flow area were measured. The OCTA morphological findings in diabetic patients were noted. In addition, the correlations between OCTA and glycosylated hemoglobin and diabetes duration were evaluated. Results There was a statistically significant decrease in the parafoveal macular thickness in the diabetic group compared with the control group (the superior–hemi parafoveal thickness was 310.94±10.84 vs. 321.71±11.2 μm, respectively, P=0.001, while the inferior–hemi parafoveal thickness was 304.71±11.04 vs. 320.82±11.25 μm, respectively, P=0.001). There was no statistically significant difference in the parafoveal and perifoveal superficial capillary plexus and DCP VD in the diabetic patients compared with the controls. In addition, there was no change in the FAZ area between the two groups, but there was a significant difference regarding the FAZ irregularity (P=0.00). Microaneurysms (100%), capillary nonperfusion (94%), capillary loop (17.6%), enlarged perifoveal intercapillary spaces (17.6%), punched-out FAZ (12%), lost spider web (6%), and capillary blind end (6%) were detected in the diabetic patients. A negative significant correlation was found between the parafoveal thickness and glycosylated hemoglobin in the DM group in the inferior–hemi thickness (r=−0.61, P=0.01), and between DM duration and VD in the DCP in the parafoveal and perifoveal areas (r=−0.55, P=0.06 and r=0.62, P=0.018, respectively). Conclusion OCTA can be used to diagnose preclinical maculopathy/retinopathy in diabetic patients using the parafoveal retinal thickness, DCP VD, FAZ irregularities, quantification of microaneurysms, and choriocapillaris flow area as biomarkers.

Diabetic Retinopathy (DR) is a degenerative disease that impacts the eyes and is a consequence of Diabetes mellitus, where high blood glucose levels induce lesions on the eye retina. Diabetic Retinopathy is regarded as the leading cause of blindness for diabetic patients, especially the working-age population in developing nations. Treatment involves sustaining the patient’s current grade of vision since the disease is irreversible. Early detection of Diabetic Retinopathy is crucial in order to sustain the patient’s vision effectively. The main issue involved with DR detection is that the manual diagnosis process is very time, money, and effort consuming and involves an ophthalmologist’s examination of eye retinal fundus images. The latter also proves to be more difficult, particularly in the early stages of the disease when disease features are less prominent in the images. Machine learning-based medical image analysis has proven competency in assessing retinal fundus images, and the utilization of deep learning algorithms has aided the early diagnosis of Diabetic Retinopathy (DR). This paper reviews and analyzes state-of-the-art deep learning methods in supervised, self-supervised, and Vision Transformer setups, proposing retinal fundus image classification and detection. For instance, referable, non-referable, and proliferative classifications of Diabetic Retinopathy are reviewed and summarized. Moreover, the paper discusses the available retinal fundus datasets for Diabetic Retinopathy that are used for tasks such as detection, classification, and segmentation. The paper also assesses research gaps in the area of DR detection/classification and addresses various challenges that need further study and investigation.

Red lesion identification at its early stage is very essential for the treatment of diabetic retinopathy to prevent loss of vision. This work proposes a red lesion detection algorithm that uses Hexagonal pattern-based features with two-level segmentation that can detect hemorrhage and microaneurysms in the fundus image. The proposed scheme initially pre-processes the fundus image followed by a two-level segmentation. The level 1 segmentation eliminates the background whereas the level 2 segmentation eliminates the blood vessels that introduce more false positives. A hexagonal pattern-based feature is extracted from the red lesion candidates which can highly differentiate the lesion from non-lesion regions. The hexagonal pattern features are then trained using the recurrent neural network and are classified to eliminate the false negatives. For the evaluation of the proposed red lesion algorithm, the datasets namely ROC challenge, e-ophtha, DiaretDB1, and Messidor are used with the metrics such as Accuracy, Recall, Precision, F1 score, Specificity, and AUC. The scheme provides an average Accuracy, Recall (Sensitivity), Precision, F1 score, Specificity, and AUC of 95.48%, 84.54%, 97.3%, 90.47%, 86.81% and 93.43% respectively. [ABSTRACT FROM AUTHOR]

Objectives: To determine the efficacy and safety of brolucizumab therapy administered on a pro re nata (PRN) basis without loading dose in treatment naïve patients with diabetic macular edema (DME) for 1 year follow-up., Methods: Patients with recent DME (<6 months) received a mandatory brolucizumab injection at inclusion and other injections could be given on a PRN basis with an 8-week interval (between injections) at minimum. Rescue therapy with other anti-VEGF was possible in case of incomplete DME resolution after the second brolucizumab with a minimum of 1-month treatment free interval between 2 injections. The primary outcome measure was the change in (BCVA) at 12 months. Secondary outcome measures included the change in central subfield thickness (CST), the change in hard exudate surface area and microaneurysms at 1 year., Results: A total of 53 patients were included. At 12 months, the mean (SD) number of injections was 2.6 (0.8) in addition to the first mandatory injection. The mean (SD) interval between 2 consecutive injections was 3.2 (1.4) months. The mean (SD) BCVA improved from 0.62 (0.1) logMAR to 0.40 (0.16) logMAR (p = 0.012). The mean CST reduced from 397.0 (47.2) µm to 224.5 (28.1) µm (p = 0.013). The hard exudate surface area decreased significantly (p = 0.012) as did microaneurysms (p = 0.02). Seven patients required at least 1 rescue therapy. No patients experienced intra-ocular inflammatory adverse events., Conclusion: Brolucizumab therapy for DME is a safe and effective modality for the treatment of recent DME and has the potential to reduce the number of injections.

Purpose: To investigate the detection of slower retinal capillary blood flow using commercial OCT angiography (OCTA) with a longer interscan time in diabetic retinopathy (DR). Design: Observational, prospective, cross-sectional study. Participants: A total of 62 eyes from 39 subjects with diabetes mellitus and 10 eyes from 9 healthy subjects. Methods: Commercial spectral domain-OCT was used to obtain 3 × 3-mm fovea-centered OCTA images of all eyes with 3 different interscan times (4.3, 5.7, and 8.6 ms). For each interscan time, OCTA imaging was performed 5 consecutive times, and a ×5 averaged image was obtained. Capillary flow density and visualization of retinal capillaries in the superficial and deep capillary plexuses (SCPs and DCPs, respectively) were compared between the 3 averaged images from the 3 different interscan times. Main Outcome Measures: Capillary flow density and visualization of foveal capillaries in 3 images with different interscan times. Results: Forty-five eyes of 34 patients were analyzed. There was no significant difference in the flow density of the SCP and DCP between the 3 images with different interscan times in all the DR stages. Some capillaries including microaneurysms that could not be observed at 4.3 ms could be observed at 5.7 or 8.6 ms. There were significantly more capillaries with difference points between the 3 images in the group with DR than in the group without DR (P

Intravitreal injection of triamcinolone acetonide (TA) is essential for clinical treatment in patients who insufficiently respond to vascular endothelial factor inhibitors for diabetic macular edema (DME). The aim of this study was to investigate microvascular changes treated with TA using optical coherence tomography angiography (OCTA). After TA in twelve eyes of eleven patients with central retinal thickness (CRT), there was a 20% or more reduction observed. Visual acuity, the number of microaneurysms, vessel density, and the foveal avascular zone (FAZ) area were compared before and at 2 months after TA. At baseline, the number of microaneurysms was 2.1 ± 1.1 in the superficial capillary plexuses (SCP) and 2.0 ± 1.1 in the deep capillary plexuses (DCP), with a significant decrease post-treatment to 1.0 ± 1.0 for SCP and 0.8 ± 0.8 for DCP (SCP; p = 0.018, DCP; p = 0.008). There was significant enlargement of the FAZ area from 0.28 ± 0.11 mm2 to 0.32 ± 0.14 mm2 (p = 0.041). There was no significant difference in the visual acuity and vessel density of SCP and DCP. Results indicated that OCTA was useful for the evaluation of qualitative and morphological retinal microcirculation and that intravitreal TA may decrease microaneurysms.