Application of non-mydriatic fundus photography-assisted telemedicine in diabetic retinopathy screening

BACKGROUND Early screening and accurate staging of diabetic retinopathy(DR)can reduce blindness risk in type 2 diabetes patients.DR’s complex pathogenesis involves many factors,making ophthalmologist screening alone insufficient for prevention and treatment.Often,endocrinologists are the first to see diabetic patients and thus should screen for retinopathy for early intervention.AIM To explore the efficacy of non-mydriatic fundus photography(NMFP)-enhanced telemedicine in assessing DR and its various stages.METHODS This retrospective study incorporated findings from an analysis of 93 diabetic patients,examining both NMFP-assisted telemedicine and fundus fluorescein angiography(FFA).It focused on assessing the concordance in DR detection between these two methodologies.Additionally,receiver operating characteristic(ROC)curves were generated to determine the optimal sensitivity and specificity of NMFP-assisted telemedicine,using FFA outcomes as the standard benchmark.RESULTS In the context of DR diagnosis and staging,the kappa coefficients for NMFPassisted telemedicine and FFA were recorded at 0.775 and 0.689 respectively,indicating substantial intermethod agreement.Moreover,the NMFP-assisted telemedicine’s predictive accuracy for positive FFA outcomes,as denoted by the area under the ROC curve,was remarkably high at 0.955,within a confidence interval of 0.914 to 0.995 and a statistically significant P-value of less than 0.001.This predictive model exhibited a specificity of 100%,a sensitivity of 90.9%,and a Youden index of 0.909.CONCLUSION NMFP-assisted telemedicine represents a pragmatic,objective,and precise modality for fundus examination,particularly applicable in the context of endocrinology inpatient care and primary healthcare settings for diabetic patients.Its implementation in these scenarios is of paramount significance,enhancing the clinical accuracy in the diagnosis and therapeutic management of DR.This methodology not only streamlines patient evaluation but also contributes substantially to the optimization of clinical outcomes in DR management.

Unified deep learning model for predicting fundus fluorescein angiography image from fundus structure image

The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera imaging,single-phase FFA from scanning laser ophthalmoscopy(SLO),and three-phase FFA also from SLO.Although many deep learning models are available,a single model can only perform one or two of these prediction tasks.To accomplish three prediction tasks using a unified method,we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network.The three prediction tasks are processed as follows:data preparation,network training under FFA supervision,and FFA image prediction from fundus structure images on a test set.By comparing the FFA images predicted by our model,pix2pix,and CycleGAN,we demonstrate the remarkable progress achieved by our proposal.The high performance of our model is validated in terms of the peak signal-to-noise ratio,structural similarity index,and mean squared error.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

Development and Validation of an Automatic Ultrawide-Field Fundus Imaging Enhancement System for Facilitating Clinical Diagnosis:A Cross-Sectional Multicenter Study

In ophthalmology,the quality of fundus images is critical for accurate diagnosis,both in clinical practice and in artificial intelligence(AI)-assisted diagnostics.Despite the broad view provided by ultrawide-field(UWF)imaging,pseudocolor images may conceal critical lesions necessary for precise diagnosis.To address this,we introduce UWF-Net,a sophisticated image enhancement algorithm that takes disease characteristics into consideration.Using the Fudan University ultra-wide-field image(FDUWI)dataset,which includes 11294 Optos pseudocolor and 2415 Zeiss true-color UWF images,each of which is rigorously annotated,UWF-Net combines global style modeling with feature-level lesion enhancement.Pathological consistency loss is also applied to maintain fundus feature integrity,significantly improving image quality.Quantitative and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization(CLAHE)and structure and illumination constrained generative adversarial network(StillGAN),delivering superior retinal image quality,higher quality scores,and preserved feature details after enhancement.In disease classification tasks,images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN,demonstrating a 4.62%increase in sensitivity(SEN)and a 3.97%increase in accuracy(ACC).In a multicenter clinical setting,UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors,and yielded a significant reduction in diagnostic time((13.17±8.40)s for UWF-Net enhanced images vs(19.54±12.40)s for original images)and an increase in diagnostic accuracy(87.71%for UWF-Net enhanced images vs 80.40%for original images).Our research verifies that UWF-Net markedly improves the quality of UWF imaging,facilitating better clinical outcomes and more reliable AI-assisted disease classification.The clinical integration of UWF-Net holds great promise for enhancing diagnostic processes and patient care in ophthalmology.

Multi-class classification of pathological myopia based on fundus photography

Pathological myopia(PM)is a severe ocular disease leading to blindness.As a traditional noninvasive diagnostic method,fundus color photography(FCP)is widely used in detecting PM due to its highfidelity and precision.However,manual examination of fundus photographs for PM is time-consuming and prone to high error rates.Existing automated detection technologies have yet to study the detailed classification in diagnosing different stages of PM lesions.In this paper,we proposed an intelligent system which utilized Resnet101 technology to multi-categorically diagnose PM by classifying FCPs with different stages of lesions.The system subdivided different stages of PM into eight subcategories,aiming to enhance the precision and efficiency of the diagnostic process.It achieved an average accuracy rate of 98.86%in detection of PM,with an area under the curve(AUC)of 98.96%.For the eight subcategories of PM,the detection accuracy reached 99.63%,with an AUC of 99.98%.Compared with other widely used multi-class models such as VGG16,Vision Transformer(VIT),EfficientNet,this system demonstrates higher accuracy and AUC.This artificial intelligence system is designed to be easily integrated into existing clinical diagnostic tools,providing an efficient solution for large-scale PM screening.

Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus

AIM:To summarize the application of deep learning in detecting ophthalmic disease with ultrawide-field fundus images and analyze the advantages,limitations,and possible solutions common to all tasks.METHODS:We searched three academic databases,including PubMed,Web of Science,and Ovid,with the date of August 2022.We matched and screened according to the target keywords and publication year and retrieved a total of 4358 research papers according to the keywords,of which 23 studies were retrieved on applying deep learning in diagnosing ophthalmic disease with ultrawide-field images.RESULTS:Deep learning in ultrawide-field images can detect various ophthalmic diseases and achieve great performance,including diabetic retinopathy,glaucoma,age-related macular degeneration,retinal vein occlusions,retinal detachment,and other peripheral retinal diseases.Compared to fundus images,the ultrawide-field fundus scanning laser ophthalmoscopy enables the capture of the ocular fundus up to 200°in a single exposure,which can observe more areas of the retina.CONCLUSION:The combination of ultrawide-field fundus images and artificial intelligence will achieve great performance in diagnosing multiple ophthalmic diseases in the future.

Enhancing diabetic retinopathy screening:Non-mydriatic fundus photography-assisted telemedicine for improved clinical management

The utilization of non-mydriatic fundus photography-assisted telemedicine to screen patients with diabetes mellitus for diabetic retinopathy provides an accurate,efficient,and cost-effective method to improve early detection of disease.It has also been shown to correlate with increased participation of patients in other aspects of diabetes care.In particular,patients who undergo teleretinal imaging are more likely to meet Comprehensive Diabetes Care Healthcare Effectiveness Data and Information Set metrics,which are linked to preservation of quality-adjusted life years and additional downstream healthcare savings.

Fundus photography,fundus fluorescein angiography,and optical coherence tomography of healthy cynomolgus monkey,New Zealand rabbit,Sprague Dawley rat,and BALB/c mouse retinas

Background:A variety of experimental animal models are used in basic ophthalmological research to elucidate physiological mechanisms of vision and disease pathogenesis.The choice of animal model is based on the measurability of specific parameters or structures,the applicability of clinical measurement technologies,and the similarity to human eye function.Studies of eye pathology usually compare optical parameters between a healthy and altered state,so accurate baseline assessments are critical,but few reports have comprehensively examined the normal anatomical structures and physiological functions in these models.Methods:Three cynomolgus monkeys,six New Zealand rabbits,ten Sprague Dawley(SD)rats,and BALB/c mice were examined by fundus photography(FP),fundus fluorescein angiography(FFA),and optical coherence tomography(OCT).Results:Most retinal structures of cynomolgus monkey were anatomically similar to the corresponding human structures as revealed by FP,FFA,and OCT.New Zealand rabbits have large eyeballs,but they have large optic disc and myelinated retinal nerve fibers in their retinas,and the growth pattern of retinal vessels were also different to the human retinas.Unlike monkeys and rabbits,the retinal vessels of SD rats and BALB/c mice were widely distributed and clear.The OCT performance of them were similar with human beings except the macular.Conclusions:Monkey is a good model to study changes in retinal structure associated with fundus disease,rabbits are not suitable for studies on retinal vessel diseases and optic nerve diseases,and rats and mice are good models for retinal vascular diseases.These measures will help guide the choice of model and measurement technology and reduce the number of experimental animals required.

Comparison of three fundus inspection methods during phacoemulsification in diabetic white cataract

AIM:To investigate whether Wild Field Imaging System(WFIS SW-8000),25G endoilluminator,and intraoperative optical coherence tomography(iOCT)can perform realtime screening and diagnosing in patients with suspicious diabetic retinopathy(DR)during phacoemulsification,especially in cases of white cataract.METHODS:A cross-sectional study was carried out.A total of 204 dense diabetic cataractous eyes of 204 patients with suspected DR treated from April 2020 to March 2021 were included.Phacoemulsification combined with intraocular lens implantation was performed.Following the removal of the lens opacity,the 25G endoilluminator,fundus photography,and iOCT were performed successively.Optical coherence tomography(OCT)and/or fundus fluorescein angiography(FFA)were used to verify the fundus findings postoperatively.Intraoperative and postoperative results were compared to verify the accuracy of intraoperative diagnosis in each group.RESULTS:Intraoperative and postoperative examinations revealed 58 and 62 eyes with DR,respectively(positive rate,28.43%and 30.39%,respectively).During the phacoemulsification,WFIS SW-8000 detected 44 eyes with DR(the detection rate,70.97%);25G endo-illuminator found 56 eyes with DR(the detection rate,90.32%);iOCT found 46 eyes with DR(the detection rate,74.19%);and 58 eyes with DR were found by combining the three methods(the detection rate,93.55%).There were statistically significant differences in the diagnostic sensitivity for DR among the methods(χ^(2)=16.36,P=0.001).CONCLUSION:WFIS SW-8000,25G endo-illuminator,iOCT,and especially their combination can be used to inspect the fundus and detect DR intraoperatively;they are helpful for the timely diagnosis and treatment of DR in patients with dense cataract.

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.

Deep learning-based automated grading of visual impairment in cataract patients using fundus images

Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.

Convolutional Neural Network-Based Classificationof Multiple Retinal Diseases Using Fundus Images

Use of deep learning algorithms for the investigation and analysis of medical images has emerged as a powerful technique.The increase in retinal dis-eases is alarming as it may lead to permanent blindness if left untreated.Automa-tion of the diagnosis process of retinal diseases not only assists ophthalmologists in correct decision-making but saves time also.Several researchers have worked on automated retinal disease classification but restricted either to hand-crafted fea-ture selection or binary classification.This paper presents a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images.For this research,the data has been collected and combined from three distinct sources.The images are preprocessed for enhancing the details.Six layers of the convolutional neural network(CNN)are used for the automated feature extraction and classification of 20 retinal diseases.It is observed that the results are reliant on the number of classes.For binary classification(healthy vs.unhealthy),up to 100%accuracy has been achieved.When 16 classes are used(treating stages of a disease as a single class),93.3%accuracy,92%sensitivity and 93%specificity have been obtained respectively.For 20 classes(treating stages of the disease as separate classes),the accuracy,sensitivity and specificity have dropped to 92.4%,92%and 92%respectively.

Association between Fundus Atherosclerosis and Carotid Arterial Atherosclerosis

Objective: To investigate the correlation between fundus atherosclerosis and carotid arterial atherosclerosis. Methods: A total of 516 people undergoing physical examination in Deyang People’s Hospital between June 2020 and December 2022 were randomly selected. Fundus atherosclerosis and carotid arterial atherosclerosis were evaluated by fundus photography and carotid artery ultrasonography, respectively. Results: Among the 516 physical examination patients, 198 (38.4%) had normal fundus examination, and 318 (61.6%) had fundus arteriosclerosis. Among them, 166 cases were of grade I (32.2%), 86 cases were of grade II (16.7%), and 66 cases were of grade III (12.8%). There were 286 cases (55.4%) without carotid atherosclerosis, 201 cases (38.9%) with carotid atherosclerotic plaque, and 33 cases (6.4%) with carotid stenosis. Fundus arteriosclerosis is independently associated with carotid artery intima-media thickness, vulnerable plaques, plaque scores, and carotid artery stenosis (P Conclusion: In summary, there is a close relationship between carotid artery disease and the degree of arteriosclerosis in the eyeground. Fundus photography is a simple, non-invasive, and easily acceptable method of inspection. The results obtained from it are useful in determining the severity of carotid atherosclerosis and guiding early detection and intervention in clinical cases. This can help reduce the incidence of cardiovascular and cerebrovascular diseases.

Fundus photography,fluorescein angiography,optical coherence tomography and electroretinography of preclinical animal models of ocular diseases

The eye is an immune-privileged and sensory organ in humans and animals.Anatomical,physiological,and pathobiological features share significant similarities across divergent species(1).Each compartment of the eye has a unique structure and function.The anterior and posterior compartments of the eye contain endothelium(cornea),epithelium(cornea,ciliary body,iris),muscle(ciliary body),vitreous and neuronal(retina)tissues,which make the eye suitable to evaluate efficacy and safety of tissue specific drugs(2).

Analysis of the Efficacy of Triamcinolone Acetonide Combined with Ranibizumab in the Treatment of Fundus Diseases

Objective:To analyze the effect of triamcinolone acetonide combined with ranibizumab in patients with fundus diseases.Methods:100 patients with fundus diseases admitted from January 2018 to January 2023 were selected.The patients were separated into two groups according to the random number table method,with 50 cases in the control group(treated with ranibizumab),and 50 cases in the observation group(treated with triamcinolone acetonide combined with ranibizumab).The clinical effects of both treatment regimens were compared.Results:The time taken for symptom disappearance of the observation group was shorter than that of the control group(P<0.05).The observation group had higher naked-eye visual acuity(4.18±0.89)compared to the control group.Besides,the observation group also had lower intraocular pressure(14.19±1.33 mmHg)and retinal thickness(283.14±3.29μm),with(P<0.05)compared to the control group.Moreover,the observation group had a lower adverse reaction rate and a higher quality of life(P<0.05).Conclusion:The application of triamcinolone acetonide combined with ranibizumab treatment can quickly relieve the clinical symptoms of patients with fundus disease,improve visual acuity,intraocular pressure,and retinal thickness,with low adverse reaction rate and better prognosis and quality of life.

Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion

AIM: To investigate and compare the efficacy of two machine-learning technologies with deep-learning(DL) and support vector machine(SVM) for the detection of branch retinal vein occlusion(BRVO) using ultrawide-field fundus images. METHODS: This study included 237 images from 236 patients with BRVO with a mean±standard deviation of age 66.3±10.6 y and 229 images from 176 non-BRVO healthy subjects with a mean age of 64.9±9.4 y. Training was conducted using a deep convolutional neural network using ultrawide-field fundus images to construct the DL model. The sensitivity, specificity, positive predictive value(PPV), negative predictive value(NPV) and area under the curve(AUC) were calculated to compare the diagnostic abilities of the DL and SVM models. RESULTS: For the DL model, the sensitivity, specificity, PPV, NPV and AUC for diagnosing BRVO was 94.0%(95%CI: 93.8%-98.8%), 97.0%(95%CI: 89.7%-96.4%), 96.5%(95%CI: 94.3%-98.7%), 93.2%(95%CI: 90.5%-96.0%) and 0.976(95%CI: 0.960-0.993), respectively. In contrast, for the SVM model, these values were 80.5%(95%CI: 77.8%-87.9%), 84.3%(95%CI: 75.8%-86.1%), 83.5%(95%CI: 78.4%-88.6%), 75.2%(95%CI: 72.1%-78.3%) and 0.857(95%CI: 0.811-0.903), respectively. The DL model outperformed the SVM model in all the aforementioned parameters(P<0.001). CONCLUSION: These results indicate that the combination of the DL model and ultrawide-field fundus ophthalmoscopy may distinguish between healthy and BRVO eyes with a high level of accuracy. The proposed combination may be used for automatically diagnosing BRVO in patients residing in remote areas lacking access to an ophthalmic medical center.

Hydrogen sulfide-induced enhancement of gastric fundus smooth muscle tone is mediated by voltagedependent potassium and calcium channels in mice

AIM:To investigate the effect of hydrogen sulfide(H2S)on smooth muscle motility in the gastric fundus.METHODS:The expression of cystathionineβ-synthase(CBS)and cystathionineγ-lyase(CSE)in cultured smooth muscle cells from the gastric fundus was examined by the immunocytochemistry technique.The tension of the gastric fundus smooth muscle was recorded by an isometric force transducer under the condition of isometric contraction with each end of the smooth muscle strip tied with a silk thread.Intracellular recording was used to identify whether hydrogen sulfide affects the resting membrane potential of the gastric fundus in vitro.Cells were freshly separated from the gastric fundus of mice using a variety of enzyme digestion methods and whole-cell patch-clamp technique was used to find the effects of hydrogen sulfide on voltage-dependent potassium channel and calcium channel.Calcium imaging with fura-3AM loading was used to investigate the mechanism by which hydrogen sulfide regulates gastric fundus motility in cultured smooth muscle cells.RESULTS:We found that both CBS and CSE were expressed in the cul tured smooth muscle cel ls from the gastric fundus and that H2S increased the smooth muscle tension of the gastric fundus in mice at low concentrations.In addition,nicardipine and aminooxyacetic acid(AOAA),a CBS inhibitor,reduced the tension,whereas Nω-nitro-L-arginine methyl ester,a nonspecific nitric oxide synthase,increased the tension.The AOAA-induced relaxation was significantly recovered by H2S,and the Na HS-induced increase in tonic contraction was blocked by 5 mmol/L4-aminopyridine and 1μmol/L nicardipine.Na HS significantly depolarized the membrane potential and inhibited the voltage-dependent potassium currents.Moreover,Na HS increased L-type Ca2+currents and caused an elevation in intracellular calcium([Ca2+]i).CONCLUSION:These findings suggest that H2S may be an excitatory modulator in the gastric fundus in mice.The excitatory effect is mediated by voltagedependent potassium and L-type calcium channels.

Fundus autofluorescence in central serous chorioretinopathy: association with spectral-domain optical coherence tomography and fluorescein angiography

and FA for identifying pathological abnormalities in CSC. The characteristics of IA AF in CSC were attributable to the modification of melanin in the RPE. IR- AIM: To evaluate the correlation among changes in fundus autofluorescence (AF) measured using infrared fundus AF (IR -AF) and short-wave length fundus AF (SW -AF) with changes in spectral -domain optical coherence tomography (SD -OCT) and fluorescein angiography (FA) in central serous chorioretinopathy (CSC). METHODS: Two hundred and twenty consecutive patients with CSC were included. In addition to AF, patients were assessed by means of SD -OCT and FA. Abnormalities in images of IA -AF, SW -AF, FA were analyzed and correlated with the corresponding outer retinal alterations in SD-OCT findings. RESULTS: Eyes with abnormalities on either IR-AF or SW-AF were found in 256 eyes (58.18%), among them 256 eyes (100%) showed abnormal IR -AF, but SW-AF abnormalities were present only in 213 eyes (83.20%). The hypo-IR-AF corresponded to accumulation of subretinal liquid, collapse of retinal pigment epithelium (APE) or detachment of APE with or without RPE leakage point in the corresponding area. The hyper -IR -AF corresponded to the area with loss of the ellipsoid portion of the inner segments and sub -sensory retinal deposits or focal melanogenesis under sensory retina. The hypo-SW-AF corresponded to accumulation of subretinal liquid or atrophy of RPE. The hyper -SW -AF associated with sub -sensory retinal deposits, detachment of RPE and focal melanogenesis. CONCLUSION: IR-AF was more sensitive than SW-AF AF should be used as a common diagnostic tool for identifying pathological lesion in CSC.

Outcomes of 957 preterm neonatal fundus examinations in a Guangzhou NICU through 2008 to 2011

AIM: To take fundus examination in the preterm neonates to observe the common diseases and report the outcomes in a neonatal intensive care unit (NICU) in Guangzhou between May 2008 and May 2011. METHODS: Fundus examinations were performed with Retcam II in 957 prematures. RESULTS: There were 957 prematures in this study, including 666 males and 291 females, 2 triple births, 152 twins and 803 singletons. During the three years, 86 infants with any stage retinopathy of prematurity (ROP) (9.0%), 123 infants with retinal hemorrhage (12.9%), 10 infants with neonatal fundual jaundice (1.0%) and 3 babies with congenital choroidal coloboma (0.3%) were found. CONCLUSION: Early detection and prompt treatment of ocular disorders in neonates is important to avoid lifelong visual impairment. Examination of the eyes should be performed in the newborn period and at all well-child visits.