Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

The intuitive fuzzy set has found important application in decision-making and machine learning.To enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge.Retinal image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye recognition.Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images.The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between images.This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection.The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were determined.Once the peak regions were identified,the recurrence relationships among those peaks were then measured.Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image.Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE.This distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

In recent years,there has been a significant increase in the number of people suffering from eye illnesses,which should be treated as soon as possible in order to avoid blindness.Retinal Fundus images are employed for this purpose,as well as for analysing eye abnormalities and diagnosing eye illnesses.Exudates can be recognised as bright lesions in fundus pictures,which can be thefirst indicator of diabetic retinopathy.With that in mind,the purpose of this work is to create an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis(IM-EDRD)with multi-level classifications.The model uses Support Vector Machine(SVM)-based classification to separate normal and abnormal fundus images at thefirst level.The input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix(GLCM).Furthermore,the presence of Exudate and Diabetic Retinopathy(DR)in fundus images is detected using the Adaptive Neuro Fuzzy Inference System(ANFIS)classifier at the second level of classification.Exudate detection,blood vessel extraction,and Optic Disc(OD)detection are all processed to achieve suitable results.Furthermore,the second level processing comprises Morphological Component Analysis(MCA)based image enhancement and object segmentation processes,as well as feature extraction for training the ANFIS classifier,to reliably diagnose DR.Furthermore,thefindings reveal that the proposed model surpasses existing models in terms of accuracy,time efficiency,and precision rate with the lowest possible error rate.

Increased retinal venule diameter as a prognostic indicator for recurrent cerebrovascular events:a prospective observational study

Microvasculature of the retina is considered an alternative marker of cerebral vascular risk in healthy populations.However,the ability of retinal vasculature changes,specifically focusing on retinal vessel diameter,to predict the recurrence of cerebrovascular events in patients with ischemic stroke has not been determined comprehensively.While previous studies have shown a link between retinal vessel diameter and recurrent cerebrovascular events,they have not incorporated this information into a predictive model.Therefore,this study aimed to investigate the relationship between retinal vessel diameter and subsequent cerebrovascular events in patients with acute ischemic stroke.Additionally,we sought to establish a predictive model by combining retinal veessel diameter with traditional risk factors.We performed a prospective observational study of 141 patients with acute ischemic stroke who were admitted to the First Affiliated Hospital of Jinan University.All of these patients underwent digital retinal imaging within 72 hours of admission and were followed up for 3 years.We found that,after adjusting for related risk factors,patients with acute ischemic stroke with mean arteriolar diameter within 0.5-1.0 disc diameters of the disc margin(MAD_(0.5-1.0DD))of≥74.14μm and mean venular diameter within 0.5-1.0 disc diameters of the disc margin(MVD_(0.5-1.0DD))of≥83.91μm tended to experience recurrent cerebrovascular events.We established three multivariate Cox proportional hazard regression models:model 1 included traditional risk factors,model 2 added MAD_(0.5-1.0DD)to model 1,and model 3 added MVD0.5-1.0DD to model 1.Model 3 had the greatest potential to predict subsequent cerebrovascular events,followed by model 2,and finally model 1.These findings indicate that combining retinal venular or arteriolar diameter with traditional risk factors could improve the prediction of recurrent cerebrovascular events in patients with acute ischemic stroke,and that retinal imaging could be a useful and non-invasive method for identifying high-risk patients who require closer monitoring and more aggressive management.

Microstructural morphology and visual acuity outcome in eyes with epiretinal membrane before,during,and after membrane peeling in intraoperative optical coherence tomography assisted macular surgery

AIM:To measure the difference of intraoperative central macular thickness(CMT)before,during,and after membrane peeling and investigate the influence of intraoperative macular stretching on postoperative best corrected visual acuity(BCVA)outcome and postoperative CMT development.METHODS:A total of 59 eyes of 59 patients who underwent vitreoretinal surgery for epiretinal membrane was analyzed.Videos with intraoperative optical coherence tomography(OCT)were recorded.Difference of intraoperative CMT before,during,and after peeling was measured.Pre-and postoperatively obtained BCVA and spectral-domain OCT images were analyzed.RESULTS:Mean age of the patients was 70±8.13y(range 46-86y).Mean baseline BCVA was 0.49±0.27 log MAR(range 0.1-1.3).Three and six months postoperatively the mean BCVA was 0.36±0.25(P=0.01 vs baseline)and 0.38±0.35(P=0.08 vs baseline)log MAR respectively.Mean stretch of the macula during surgery was 29%from baseline(range 2%-159%).Intraoperative findings of macular stretching did not correlate with visual acuity outcome within 6mo after surgery(r=-0.06,P=0.72).However,extent of macular stretching during surgery significantly correlated with less reduction of CMT at the fovea centralis(r=-0.43,P<0.01)and 1 mm nasal and temporal from the fovea(r=-0.37,P=0.02 and r=-0.50,P<0.01 respectively)3mo postoperatively.CONCLUSION:The extent of retinal stretching during membrane peeling may predict the development of postoperative central retinal thickness,though there is no correlation with visual acuity development within the first 6mo postoperatively.

A GAN-based deep enhancer for quality enhancement of retinal images photographed by a handheld fundus camera

Objective:Due to limited imaging conditions,the quality of fundus images is often unsatisfactory,especially for images photographed by handheld fundus cameras.Here,we have developed an automated method based on combining two mirror-symmetric generative adversarial networks(GANs)for image enhancement.Methods:A total of 1047 retinal images were included.The raw images were enhanced by a GAN-based deep enhancer and another methods based on luminosity and contrast adjustment.All raw images and enhanced images were anonymously assessed and classified into 6 levels of quality classification by three experienced ophthalmologists.The quality classification and quality change of images were compared.In addition,imagedetailed reading results for the number of dubiously pathological fundi were also compared.Results:After GAN enhancement,42.9% of images increased their quality,37.5%remained stable,and 19.6%decreased.After excluding the images at the highest level(level 0)before enhancement,a large number(75.6%)of images showed an increase in quality classification,and only a minority(9.3%)showed a decrease.The GANenhanced method was superior for quality improvement over a luminosity and contrast adjustment method(P<0.001).In terms of image reading results,the consistency rate fluctuated from 86.6%to 95.6%,and for the specific disease subtypes,both discrepancy number and discrepancy rate were less than 15 and 15%,for two ophthalmologists.Conclusions:Learning the style of high-quality retinal images based on the proposed deep enhancer may be an effective way to improve the quality of retinal images photographed by handheld fundus cameras.

Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis.The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease.This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)model.The proposed GOFED-RBVSC model initially employs contrast enhancement process.Besides,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions.The ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature vectors.Finally,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the work.The performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.

The association between retinal vasculature changes and stroke:a literature review and Meta-analysis

AIM： To determine the association between retinal vasculature changes and stroke.METHODS： MEDLINE and EMBASE were searched for relevant human studies to September 2015 that investigated the association between retinal vasculature changes and the prevalence or incidence of stroke; the studies were independently examined for their qualities. Data on clinical characteristics and calculated summary odds ratios （ORs） were extracted for associations between retinal microvascular abnormalities and stroke, including stroke subtypes where possible, and adjusted for key variables. RESULTS： Nine cases were included in the study comprising 20 659 patients, 1178 of whom were stroke patients. The retinal microvascular morphological markers used were hemorrhage, microaneurysm, vessel caliber, arteriovenous nicking, and fractal dimension. OR of retinal arteriole narrowing and retinal arteriovenous nicking and stroke was 1.42 and 1.91, respectively, indicating that a small-caliber retinal arteriole and retinal arteriovenous nicking were associated with stroke. OR of retinal hemorrhage and retinal microaneurysm and stroke was 3.21 and 3.83, respectively, indicating that retinal microvascular lesions were highly associated with stroke. Results also showed that retinal fractal dimension reduction was associated with stroke （OR： 2.28 for arteriole network, OR： 1.80 for venular network）.CONCLUSION： Retinal vasculature changes have a specific relationship to stroke, which is promising evidence for the prediction of stroke using computerized retinal vessel analysis.

Multimodal imaging in acute retinal ischemia: spectral domain OCT, OCT-angiography and fundus autofluorescence

AIM： To describe retinal findings of various imaging modalities in acute retinal ischemia. METHODS： Fluorescein angiography（FA）, spectral domain optical coherence tomography（SD-OCT）, OCTangiography（OCT-A） and fundus autofluorescence（FAF） images of 13 patients（mean age 64y, range 28-86y） with acute retinal ischemia were evaluated. Six suffered from branch arterial occlusion, 2 had a central retinal artery occlusion, 2 had a combined arteriovenous occlusions, 1 patient had a retrobulbar arterial compression by an orbital haemangioma and 2 patients showed an ocular ischemic syndrome.RESULTS： All patients showed increased reflectivity and thickening of the ischemic retinal tissue. In 10 out of 13 patients SD-OCT revealed an additional highly reflective band located within or above the outer plexiform layer. Morphological characteristics were a decreasing intensity with distance from the fovea, partially segmental occurrence and manifestation limited in time. OCT-A showed a loss of flow signal in the superficial and deep capillary plexus at the affected areas. Reduced flow signal was detected underneath the regions with retinal edema. FAF showed areas of altered signal intensity at the posterior pole. The regions of decreased FAF signal corresponded to peri-venous regions. CONCLUSION： Multimodal imaging modalities in retinal ischemia yield characteristic findings and valuable diagnostic information. Conventional OCT identifies hyperreflectivity and thickening and a mid-retinal hyperreflective band is frequently observed. OCT-A examination reveals demarcation of the ischemic retinal area on the vascular level. FAF shows decreased fluorescence signal in areas of retinal edema often corresponding to peri-venous regions.

Retinal axial focusing and multi-layer imaging with a liquid crystal adaptive optics camera

With the help of adaptive optics （AO） technology, cellular level imaging of living human retina can be achieved. Aiming to reduce distressing feelings and to avoid potential drug induced diseases, we attempted to image retina with dilated pupil and froze accommodation without drugs. An optimized liquid crystal adaptive optics camera was adopted for retinal imaging. A novel eye stared system was used for stimulating accommodation and fixating imaging area. Illumination sources and imaging camera kept linkage for focusing and imaging different layers. Four subjects with diverse degree of myopia were imaged. Based on the optical properties of the human eye, the eye stared system reduced the defocus to less than the typical ocular depth of focus. In this way, the illumination light can be projected on certain retina layer precisely. Since that the defocus had been compensated by the eye stared system, the adopted 512 × 512 liquid crystal spatial light modulator （LC-SLM） corrector provided the crucial spatial fidelity to fully compensate high-order aberrations. The Strehl ratio of a subject with -8 diopter myopia was improved to 0.78, which was nearly close to diffraction-limited imaging. By finely adjusting the axial displacement of illumination sources and imaging camera, cone photoreceptors, blood vessels and nerve fiber layer were clearly imaged successfully.

NDC-IVM:An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For this purpose,some of the clustering and segmentation techniques are proposed in the existing works.But,it has some drawbacks that include ineficient,inaccurate and estimates only the affected area.In order to solve these issues,a Neighboring Differential Clustering(NDC)-Intensity V ariation Making(IVM)are proposed in this paper.The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc.This work includes three stages such as,preprocessing,clustering and segmentation.At first,the given retinal image is preprocessed by using the Gaussian Mask Updated(GMU)model for eliminating the noise and improving the quality of the image.Then,the cluster is formed by extracting the threshold and patterns with the help of NDC technique.In the segmentation stage,the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method.Here,the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identification.In experiments,the results of both existing and proposed techniques are evaluated in terms of sensitivity,specificity,accuracy,Hausdorff distance,Jaccard and dice metrics.

Determining the imaging plane of a retinal capillary layer in adaptive optical imaging

Even in the early stage,endocrine metabolism disease may lead to micro aneurysms in retinal capillaries whose diameters are less than 10 μm.However,the fundus cameras used in clinic diagnosis can only obtain images of vessels larger than 20 μm in diameter.The human retina is a thin and multiple layer tissue,and the layer of capillaries less than10 μm in diameter only exists in the inner nuclear layer.The layer thickness of capillaries less than 10 μm in diameter is about 40 μm and the distance range to rod＆cone cell surface is tens of micrometers,which varies from person to person.Therefore,determining reasonable capillary layer（CL） position in different human eyes is very difficult.In this paper,we propose a method to determine the position of retinal CL based on the rod＆cone cell layer.The public positions of CL are recognized with 15 subjects from 40 to 59 years old,and the imaging planes of CL are calculated by the effective focal length of the human eye.High resolution retinal capillary imaging results obtained from 17 subjects with a liquid crystal adaptive optics system（LCAOS） validate our method.All of the subjects＇ CLs have public positions from 127 μm to 147 μm from the rod＆cone cell layer,which is influenced by the depth of focus.

Analysis of normal human retinal vascular network architecture using multifractal geometry

AIM：To apply the multifractal analysis method as a quantitative approach to a comprehensive description of the microvascular network architecture of the normal human retina.METHODS：Fifty volunteers were enrolled in this study in the Ophthalmological Clinic of Cluj-Napoca,Romania,between January 2012 and January 2014. A set of 100 segmented and skeletonised human retinal images,corresponding to normal states of the retina were studied. An automatic unsupervised method for retinal vessel segmentation was applied before multifractal analysis. The multifractal analysis of digital retinal images was made with computer algorithms,applying the standard boxcounting method. Statistical analyses were performed using the Graph Pad In Stat software.RESULTS：The architecture of normal human retinal microvascular network was able to be described using the multifractal geometry. The average of generalized dimensions（D_q）for q=0,1,2,the width of the multifractal spectrum（Δα=α_（max）-α_（min））and the spectrum arms＇ heights difference（│Δf│）of the normal images were expressed as mean±standard deviation（SD）：for segmented versions,D_0=1.7014±0.0057; D_1=1.6507±0.0058; D_2=1.5772±0.0059; Δα=0.92441±0.0085; │Δf│= 0.1453±0.0051; for skeletonised versions,D_0=1.6303±0.0051; D_1=1.6012±0.0059; D_2=1.5531± 0.0058; Δα=0.65032±0.0162; │Δf│= 0.0238±0.0161. The average of generalized dimensions（D_q）for q=0,1,2,the width of the multifractal spectrum（Δα）and the spectrum arms＇ heights difference（│Δf│）of the segmented versions was slightly greater than the skeletonised versions.CONCLUSION：The multifractal analysis of fundus photographs may be used as a quantitative parameter for the evaluation of the complex three-dimensional structure of the retinal microvasculature as a potential marker for early detection of topological changes associated with retinal diseases.

Three-Dimensional Modeling of the Retinal Vascular Tree via Fractal Interpolation

In recent years,the three dimensional reconstruction of vascular structures in the field of medical research has been extensively developed.Several studies describe the various numerical methods to numerical modeling of vascular structures in near-reality.However,the current approaches remain too expensive in terms of storage capacity.Therefore,it is necessary to find the right balance between the relevance of information and storage space.This article adopts two sets of human retinal blood vessel data in 3D to proceed with data reduction in the first part and then via 3D fractal reconstruction,recreate them in a second part.The results show that the reduction rate obtained is between 66%and 95%as a function of the tolerance rate.Depending on the number of iterations used,the 3D blood vessel model is successful at reconstruction with an average error of 0.19 to 5.73 percent between the original picture and the reconstructed image.

Automated cone photoreceptor cell identication in confocal adaptive optics scanning laser ophthalmoscope images based on object detection

Cone photoreceptor cell identication is important for the early diagnosis of retinopathy.In this study,an object detection algorithm is used for cone cell identication in confocal adaptive optics scanning laser ophthalmoscope(AOSLO)images.An effectiveness evaluation of identication using the proposed method reveals precision,recall,and F_(1)-score of 95.8%,96.5%,and 96.1%,respectively,considering manual identication as the ground truth.Various object detection and identication results from images with different cone photoreceptor cell distributions further demonstrate the performance of the proposed method.Overall,the proposed method can accurately identify cone photoreceptor cells on confocal adaptive optics scanning laser ophthalmoscope images,being comparable to manual identication.

Retinal imaging in inherited retinal diseases

Inherited retinal diseases(IRD)are a leading cause of blindness in the working age population.The advances in ocular genetics,retinal imaging and molecular biology,have conspired to create the ideal environment for establishing treatments for IRD,with the first approved gene therapy and the commencement of multiple therapy trials.The scope of this review is to familiarize clinicians and scientists with the current landscape of retinal imaging in IRD.Herein we present in a comprehensive and concise manner the imaging findings of:(I)macular dystrophies(MD)[Stargardt disease(ABCA4),X-linked retinoschisis(RS1),Best disease(BEST1),pattern dystrophy(PRPH2),Sorsby fundus dystrophy(TIMP3),and autosomal dominant drusen(EFEMP1)],(II)cone and cone-rod dystrophies(GUCA1A,PRPH2,ABCA4 and RPGR),(III)cone dysfunction syndromes[achromatopsia(CNGA3,CNGB3,PDE6C,PDE6H,GNAT2,ATF6],blue-cone monochromatism(OPN1LW/OPN1MW array),oligocone trichromacy,bradyopsia(RGS9/R9AP)and Bornholm eye disease(OPN1LW/OPN1MW),(IV)Leber congenital amaurosis(GUCY2D,CEP290,CRB1,RDH12,RPE65,TULP1,AIPL1 and NMNAT1),(V)rod-cone dystrophies[retinitis pigmentosa,enhanced S-Cone syndrome(NR2E3),Bietti crystalline corneoretinal dystrophy(CYP4V2)],(VI)rod dysfunction syndromes(congenital stationary night blindness,fundus albipunctatus(RDH5),Oguchi disease(SAG,GRK1),and(VII)chorioretinal dystrophies[choroideremia(CHM),gyrate atrophy(OAT)].

Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

The study aimed to apply to Machine Learning(ML)researchers working in image processing and biomedical analysis who play an extensive role in compre-hending and performing on complex medical data,eventually improving patient care.Developing a novel ML algorithm specific to Diabetic Retinopathy(DR)is a chal-lenge and need of the hour.Biomedical images include several challenges,including relevant feature selection,class variations,and robust classification.Although the cur-rent research in DR has yielded favourable results,several research issues need to be explored.There is a requirement to look at novel pre-processing methods to discard irrelevant features,balance the obtained relevant features,and obtain a robust classi-fication.This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier(SKPD-PSC)method.The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in har-mony with the Platt Scale Classifier(PSC)to improve the accuracy of DR detection.First,a Steerable Filter Kernel Pre-processing(SFKP)model is applied to the Retinal Images(RI)to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians(DDG).Next,the Partial Derivative Image Localization(PDIL)model is applied to the extracted fea-tures to localize candidate features and suppress the background noise.Finally,a Platt Scale Classifier(PSC)is applied to the localized features for robust classification.For the experiments,we used the publicly available DR detection database provided by Standard Diabetic Retinopathy(SDR),called DIARETDB0.A database of 130 image samples has been collected to train and test the ML-based classifiers.Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accu-racy rate with minimum time and complexity compared to the state-of-the-art meth-ods.The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation.Compared to state-of-the-art methods,the method increases DR detection accuracy by 24%and DR detection time by 37.

A Novel Soft Clustering Method for Detection of Exudates

One of the earliest indications of diabetes consequence is Diabetic Retinopathy(DR),the main contributor to blindness worldwide.Recent studies have proposed that Exudates(EXs)are the hallmark of DR severity.The present study aims to accurately and automatically detect EXs that are difficult to detect in retinal images in the early stages.An improved Fusion of Histogram-Based Fuzzy C-Means Clustering(FHBFCM)by a New Weight Assignment Scheme(NWAS)and a set of four selected features from stages of pre-processing to evolve the detection method is proposed.The features of DR train the optimal parameter of FHBFCM for detecting EXs diseases through a stepwise enhancement method through the coarse segmentation stage.The histogram-based is applied to find the color intensity in each pixel and performed to accomplish Red,Green,and Blue(RGB)color information.This RGB color information is used as the initial cluster centers for creating the appropriate region and generating the homogeneous regions by Fuzzy C-Means(FCM).Afterward,the best expression of NWAS is used for the delicate detection stage.According to the experiment results,the proposed method successfully detects EXs on the retinal image datasets of DiaretDB0(Standard Diabetic Retinopathy Database Calibration level 0),DiaretDB1(Standard Diabetic Retinopathy Database Calibration level 1),and STARE(Structured Analysis of the Retina)with accuracy values of 96.12%,97.20%,and 93.22%,respectively.As a result,this study proposes a new approach for the early detection of EXs with competitive accuracy and the ability to outperform existing methods by improving the detection quality and perhaps significantly reducing the segmentation of false positives.

Retinal Vessel Segmentation via Adversarial Learning and Iterative Refinement

Retinal vessel segmentation is a challenging medical task owing to small size of dataset,micro blood vessels and low image contrast.To address these issues,we introduce a novel convolutional neural network in this paper,which takes the advantage of both adversarial learning and recurrent neural network.An iterative design of network with recurrent unit is performed to refine the segmentation results from input retinal image gradually.Recurrent unit preserves high-level semantic information for feature reuse,so as to output a sufficiently refined segmentation map instead of a coarse mask.Moreover,an adversarial loss is imposing the integrity and connectivity constraints on the segmented vessel regions,thus greatly reducing topology errors of segmentation.The experimental results on the DRIVE dataset show that our method achieves area under curve and sensitivity of 98.17%and 80.64%,respectively.Our method achieves superior performance in retinal vessel segmentation compared with other existing state-of-the-art methods.

Retinal Vascular Imaging Application in Women’s Reproductive Health:Clinical Implications and Future Directions

Over the past two decades,population-based studies employing semiautomatic computer-assisted programs have uncovered associations between retinal microvascular features and various systemic conditions.As the recognition of retinal imaging in cardiometabolic health grows,there is increasing evidence supporting its application in women’s health,particularly during the reproductive age.This review aims to summarize the indications of retinal imaging in women’s health and intergenerational health,where suboptimal retinal imaging has been found to mirror pathological systemic changes,such as suboptimal hemodynamic circulation,inflammation,endothelial dysfunction,oxidative stress,and hypoxia in vivo.Findings from Singapore Growing Up in Singapore Towards Healthy Outcomes and Singapore Preconception Study of Long-Term Maternal and Child Outcomes cohorts have reported serial changes in retinal conventional microvascular features(e.g.,retinal arteriolar narrowing,retinal venular widening)and retinal geometric microvascular features(e.g.,sparse fractal dimension,enlarged branching angle,and increased curvature tortuosity)during the preconception and antenatal phases.These morphological abnormalities were found to be related to female fertility,maternal antenatal health conditions,postnatal maternal cardiometabolic health,and intergenerational health in the fetus.Given the compelling evidence of the ability to detect microvascular changes through noninvasive methods at an early stage,retinal imaging holds the potential to facilitate timely interventions,mitigate the progression of complications,and prevent adverse pregnancy outcomes.Looking ahead,the convergence of artificial intelligence and advanced imaging techniques heralds a promising era in women’s health research and clinical practice.

Characterisation of human non-proliferative diabetic retinopathy using the fractal analysis

· AIM: To investigate and quantify changes in the branching patterns of the retina vascular network in diabetes using the fractal analysis method.·METHODS: This was a clinic-based prospective study of 172 participants managed at the Ophthalmological Clinic of Cluj-Napoca, Romania, between January 2012 and December 2013. A set of 172 segmented and skeletonized human retinal images, corresponding to both normal(24 images) and pathological(148 images)states of the retina were examined. An automatic unsupervised method for retinal vessel segmentation was applied before fractal analysis. The fractal analyses of the retinal digital images were performed using the fractal analysis software Image J. Statistical analyses were performed for these groups using Microsoft Office Excel2003 and Graph Pad In Stat software.·RESULTS: It was found that subtle changes in the vascular network geometry of the human retina are influenced by diabetic retinopathy(DR) and can be estimated using the fractal geometry. The average of fractal dimensions D for the normal images(segmented and skeletonized versions) is slightly lower than the corresponding values of mild non-proliferative DR(NPDR) images(segmented and skeletonized versions).The average of fractal dimensions D for the normal images(segmented and skeletonized versions) is higher than the corresponding values of moderate NPDR images(segmented and skeletonized versions). The lowestvalues were found for the corresponding values of severe NPDR images(segmented and skeletonized versions).· CONCLUSION: The fractal analysis of fundus photographs may be used for a more complete understanding of the early and basic pathophysiological mechanisms of diabetes. The architecture of the retinal microvasculature in diabetes can be quantitative quantified by means of the fractal dimension.Microvascular abnormalities on retinal imaging may elucidate early mechanistic pathways for microvascular complications and distinguish patients with DR from healthy individuals.