Correlation of leaking microaneurysms with retinal thickening in diabetic retinopathy

AIM: To investigate the contribution of fluorescein angiographic leaking microaneurysms (leak-MA) versus non-leaking microaneurysms (non-leak-MA) to retinal thickening in diabetic retinopathy.METHODS: A consecutive series of 38 eyes from 24 patients with diabetic retinopathy was included.Leak-MA and non-leak-MA in each eye were selected in pairs at corresponding topographic location.Leaking was defined by late phase fluorescein angiograms compared to early phase.Retinal thickness was measured with Heidelberg Spectralis OCT topographically aligned on early phase angiograms at the MA site and within a 1 mm circle.RESULTS: In all eyes,significant retinal thickening at the site of leaking compared to non-leaking microaneurysms was observed (356±69μm vs 318±56μm,P <0.001),showing a mean increase in thickness in the areas of leak-MA vs non-leak-MA of 38±39μm (95% confidence interval 2551μm,P<0.001).All 1mm area measurements also showed significant (P<0.001) thickening of the leak-MA with average thickness of leak-MA vs non-leak-MA as 351±67μmvs 319±59μm;minimum thickness 311±62μm vs 284±60μm;maximum thickness 389±78μm vs 352±66μm;and retina volume 26.4±6.0mmvs 23.6±3.7mm3,respectively.CONCLUSION: Leaking of microaneurysms on fluorescein angiography appears to cause focal thickening of retina,which can be measured with high-resolution OCT.Therefore,targeting leaking microaneursyms in diabetic retinopathy has the potential to reduce retinal thickening.

The application research on the Automatic Detection and Grading of Microaneurysms in Fundus Images of Diabetic Retinopathy by Artificial Intelligence Deep Learning Algorithms

This research focuses on the automatic detection and grading of microaneurysms in fundus images of diabetic retinopathy using artificial intelligence deep learning algorithms.By integrating multi-source fundus image data and undergoing a rigorous preprocessing workflow,a hybrid deep learning model architecture combining a modified U-Net and a residual neural network was adopted for the study.The experimental results show that the model achieved an accuracy of[X]%in microaneurysm detection,with a recall rate of[Y]%and a precision rate of[Z]%.In terms of grading diabetic retinopathy,the Cohen’s kappa coefficient for agreement with clinical grading was[K],and there were specific sensitivities and specificities for each grade.Compared with traditional methods,this model has significant advantages in processing speed and result consistency.However,it also has limitations such as insufficient data diversity,difficulties for the algorithm in detecting microaneurysms in severely hemorrhagic images,and high computational costs.The results of this research are of great significance for the early screening and clinical diagnosis decision support of diabetic retinopathy.In the future,it is necessary to further optimize the data and algorithms and promote clinical integration and telemedicine applications.

A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

Diabetic Retinopathy(DR)is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina,leading to blindness or loss of vision.Morphological and physiological retinal variations involving slowdown of blood flow in the retina,elevation of leukocyte cohesion,basement membrane dystrophy,and decline of pericyte cells,develop.As DR in its initial stage has no symptoms,early detection and automated diagnosis can prevent further visual damage.In this research,using a Deep Neural Network(DNN),segmentation methods are proposed to detect the retinal defects such as exudates,hemorrhages,microaneurysms from digital fundus images and then the conditions are classified accurately to identify the grades as mild,moderate,severe,no PDR,PDR in DR.Initially,saliency detection is applied on color images to detect maximum salient foreground objects from the background.Next,structure tensor is applied powerfully to enhance the local patterns of edge elements and intensity changes that occur on edges of the object.Finally,active contours approximation is performed using gradient descent to segment the lesions from the images.Afterwards,the output images from the proposed segmentation process are subjected to evaluate the ratio between the total contour area and the total true contour arc length to label the classes as mild,moderate,severe,No PDR and PDR.Based on the computed ratio obtained from segmented images,the severity levels were identified.Meanwhile,statistical parameters like the mean and the standard deviation of pixel intensities,mean of hue,saturation and deviation clustering,are estimated through K-means,which are computed as features from the output images of the proposed segmentation process.Using these derived feature sets as input to the classifier,the classification of DR was performed.Finally,a VGG-19 deep neural network was trained and tested using the derived feature sets from the KAGGLE fundus image dataset containing 35,126 images in total.The VGG-19 is trained with features extracted from 20,000 images and tested with features extracted from 5,000 images to achieve a sensitivity of 82%and an accuracy of 96%.The proposed system was able to label and classify DR grades automatically.

Automated Diabetic Retinopathy Detection Using Bag of Words Approach

Imaging and computer vision systems offer the ability to study quantitatively on human physiology. On contrary, manual interpretation requires tremendous amount of work, expertise and excessive processing time. This work presents an algorithm that integrates image processing and machine learning to diagnose diabetic retinopathy from retinal fundus images. This automated method classifies diabetic retinopathy (or absence thereof) based on a dataset collected from some publicly available database such as DRIDB0, DRIDB1, MESSIDOR, STARE and HRF. Our approach utilizes bag of words model with Speeded Up Robust Features and demonstrate classification over 180 fundus images containing lesions (hard exudates, soft exudates, microaneurysms, and haemorrhages) and non-lesions with an accuracy of 94.4%, precision of 94%, recall and f1-score of 94% and AUC of 95%. Thus, the proposed approach presents a path toward precise and automated diabetic retinopathy diagnosis on a massive scale.

An Effective Diagnosis of Diabetic Retinopathy with Aid of Soft Computing Approaches

DR （diabetic retinopathy） is a most probable reason of blindness in adults, but the only remedy or escape from blindness is that we have to detect DR as early. Several automated screening techniques are used to detect individual lesions in the retina. Still it takes more dependency of time and experts. To overcome those problems and also automatically detect DR in easier and faster way, we took into soft computing approaches in our proposed work. Our proposed work will discuss several amounts of soft computing algorithms, it can detect DR features （landmark and retinal lesions） in an easy manner. Processes includes are： （1） Pre-processing; （2） Optic disc localization and segmentation; （3） Localization of fovea; （4） Blood vessel segmentation; （5） Feature extraction; （6） Feature selection; Finally （7） detection of diabetic retinopathy stages （mild, moderate, severe and PDR）. Our experimental results based on Matlab simulation and it takes databases of STARE and DRIVE. Proposed effective soft computing approaches should improve the sensitivity, specificity and accuracy.

Aβ-Aggregation-Generated Blue Autofluorescence Illuminates Senile Plaques as well as Complex Blood and Vascular Pathologies in Alzheimer’s Disease

Senile plaque blue autofluorescence was discovered around 40 years ago,however,its impact on Alzheimer’s disease(AD)pathology has not been fully examined.We analyzed senile plaques with immunohistochemistry and fluorescence imaging on AD brain sections and also Aβ aggregation in vitro.In DAPI or Hoechst staining,the nuclear blue fluorescence could only be correctly assigned after subtracting the blue plaque autofluorescence.The flower-like structures wrapping dense-core blue fluorescence formed by cathepsin D staining could not be considered central-nucleated neurons with defective lysosomes since there was no nuclear staining in the plaque core when the blue autofluorescence was subtracted.Both Aβ self-oligomers and Aβ/hemoglobin heterocomplexes generated blue autofluorescence.The Aβ amyloid blue autofluorescence not only labels senile plaques but also illustrates red cell aggregation,hemolysis,cerebral amyloid angiopathy,vascular plaques,vascular adhesions,and microaneurysms.In summary,we conclude that Aβ-aggregation-generated blue autofluorescence is an excellent multi-amyloidosis marker in Alzheimer’s disease.

Improving diabetic and hypertensive retinopathy with a medical food containing L-methylfolate: a preliminary report

Background:Homocysteine and vitamin D may play a role in the development of diabetic and hypertensive retinopathy in patients with diabetes mellitus(DM)and hypertension.Supplementing food with L-methylfolate and vitamin D theoretically may improve diabetic and hypertensive retinopathy,however,the outcome of these nutritional approaches has not been fully examined.A retrospective case review was done of cases of retinopathy reversal in patients on Ocufolin^(TM) and a similar nonprescription multivitamin,Eyefolate^(TM).In this study,they were administered L-methylfolate(2.7 mg and 3.0 mg,respectively)and vitamin D3(4500 IU each).These dosages are significantly above the RDA but well below levels associated with toxicity.Case presentation:Seven patients had nonproliferative diabetic retinopathy(NPDR)and some of them had hypertension.One patient had only hypertensive retinopathy.All patients were instructed to take Ocufolin^(TM)medical food as a food supplement.Baseline genetic testing for MTHFR polymorphisms was conducted.Fundus photography was used to document the fundus condition of the enrolled eyes in 8 NPDR patients at the initial and follow-up visits.Microaneurysms(MA)and exudates were observed to be improved in some trial patients.All subjects had one or more MTHFR polymorphisms.All had diabetic retinopathy,hypertensive retinopathy,or both.MAs were resolved,and exudates were decreased in 8/8 cases after taking the medical food.Retinal edema was found in 2/8 cases and improved or resolved in both cases after taking the medical food or the supplement.The best corrected visual activity was stable or improved in 8/8 cases.Conclusion:We report a series of diabetic and hypertensive retinopathy cases with MTHFR polymorphisms and the improvement of retinal microvasculature(mainly MAs)in serial fundus photography after taking a medical food or supplement containing L-methylfolate and vitamin D.It appears that the use of nutritional supplements and medical foods containing L-methylfolate and vitamin D may be effective in facilitating the improvement of diabetic and hypertensive retinopathy.

Improved circulation in ocular ischemic syndrome after carotid artery stenting

Ocular ischemic syndrome is a chronic ischemic eye disease including a series of ischemic ocular and brain syndromes caused by carotid artery occlusion or stenosis. Because of the different degrees of ischemia, clinical manifestations of ocular ischemic syndrome are diverse, and it is difficult to diagnose in the initial stage. The main strategy to treat ocular ischemic syndrome is elimination of carotid stenosis. We presented a patient who recovered dramatically after carotid artery stenting. The pre-stenting arm-retinal circulation time of the patient＇s left eye was prolonged, and a large amount of microaneurysm appeared at the posterior polar and mid-peripheral aspects of the left retina. The post-stenting arm-retinal circulation time of the left eve decreased to 16.3 seconds, and the microaneurvsm almost disappeared.

Automatic Diabetic Retinopathy Screening via Cascaded Framework Based on Image- and Lesion-Level Features Fusion

The early detection of diabetic retinopathy is crucial for preventing blindness.However,it is time-consuming to analyze fundus images manually,especially considering the increasing amount of medical images.In this paper,we propose an automatic diabetic retinopathy screening method using color fundus images.Our approach consists of three main components:edge-guided candidate microaneurysms detection,candidates classification using mixed features,and diabetic retinopathy prediction using fused features of image level and lesion level.We divide a screening task into two sub-classification tasks:(1)verifying candidate microaneurysms by a naive Bayes classifier;(2)predicting diabetic retinopathy using a support vector machine classifier.Our approach can effectively alleviate the imbalanced class distribution problem.We evaluate our method on two public databases:Lariboisière and Messidor,resulting in an area under the curve of 0.908 on Lariboisière and 0.832 on Messidor.These scores demonstrate the advantages of our approach over the existing methods.