Glaucoma,a chronic eye disease affecting millions worldwide,poses a substantial threat to eyesight and can result in permanent vision loss if left untreated.Manual identification of glaucoma is a complicated and time-...Glaucoma,a chronic eye disease affecting millions worldwide,poses a substantial threat to eyesight and can result in permanent vision loss if left untreated.Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective.To address these challenges,this research proposes a computer-aided diagnosis(CAD)approach using Artificial Intelligence(AI)techniques for binary and multiclass classification of glaucoma stages.An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network(ConvNet)models–ResNet-50,VGG-16,and InceptionV3 is utilized in this paper.This fusion technique enhances diagnostic accuracy and robustness by ensemble-averaging the predictions from individual models,leveraging their complementary strengths.The objective of this work is to assess the model’s capability for early-stage glaucoma diagnosis.Classification is performed on a dataset collected from the Harvard Dataverse repository.With the proposed technique,for Normal vs.Advanced glaucoma classification,a validation accuracy of 98.04%and testing accuracy of 98.03%is achieved,with a specificity of 100%which outperforms stateof-the-art methods.For multiclass classification,the suggested ensemble approach achieved a precision and sensitivity of 97%,specificity,and testing accuracy of 98.57%and 96.82%,respectively.The proposed E-GlauNet model has significant potential in assisting ophthalmologists in the screening and fast diagnosis of glaucoma,leading to more reliable,efficient,and timely diagnosis,particularly for early-stage detection and staging of the disease.While the proposed method demonstrates high accuracy and robustness,the study is limited by the evaluation of a single dataset.Future work will focus on external validation across diverse datasets and enhancing interpretability using explainable AI techniques.展开更多
Objective:Diabetic retinopathy(DR)screening using artificial intelligence(AI)has evolved significantly over the past decade.This study aimed to analyze research trends,developments,and patterns in AI-based fundus imag...Objective:Diabetic retinopathy(DR)screening using artificial intelligence(AI)has evolved significantly over the past decade.This study aimed to analyze research trends,developments,and patterns in AI-based fundus image DR screening from 2014 to 2024 through bibliometric analysis.Methods:The study used CiteSpace and Microsoft Excel to analyze 1,172 publications from the Web of Science Core Collection database.The analysis included publication trends over time,citation patterns,institutional collaborations,and the emergence of keywords.Results:From 2014-2022,there was a steady increase in the number of publications,reaching a peak in 2021.India(26%),China(20.05%),and the USA(9.98%)were the major contributors to research output in this field.Among the publication venues,IEEE ACCESS stood out as the leading one,with 44 articles published.The research landscape has evolved from traditional image processing techniques to deep learning approaches.In recent years,there has been a growing emphasis on multimodal AI models.The analysis identified three distinct phases in the development of AI-based DR screening:CNN-based systems(2014-2020),Vision Transformers and innovative learning paradigms(2020-2022),and large foundation models(2022-2024).Conclusions:The field has demonstrated a mature development in traditional AI approaches and is currently in the process of transitioning toward multimodal learning technologies.Future directions suggest an increased focus on the integration of telemedicine,innovative AI algorithms,and real-world implementation of these technologies in real-world settings.展开更多
Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves,blood vessels,retinal health,and the impact of diabetes on the optic nerves.Fundus disorders are a major global health concern,affec...Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves,blood vessels,retinal health,and the impact of diabetes on the optic nerves.Fundus disorders are a major global health concern,affecting millions of people worldwide due to their widespread occurrence.Fundus photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic retinopathy.As a result,accurate fundus detection is essential for early diagnosis and effective treatment,helping to prevent severe complications and improve patient outcomes.To address this need,this article introduces a Derivative Model for Fundus Detection using Deep NeuralNetworks(DMFD-DNN)to enhance diagnostic precision.Thismethod selects key features for fundus detection using the least derivative,which identifies features correlating with stored fundus images.Feature filtering relies on the minimum derivative,determined by extracting both similar and varying textures.In this research,the DNN model was integrated with the derivative model.Fundus images were segmented,features were extracted,and the DNN was iteratively trained to identify fundus regions reliably.The goal was to improve the precision of fundoscopic diagnosis by training the DNN incrementally,taking into account the least possible derivative across iterations,and using outputs from previous cycles.The hidden layer of the neural network operates on the most significant derivative,which may reduce precision across iterations.These derivatives are treated as inaccurate,and the model is subsequently trained using selective features and their corresponding extractions.The proposed model outperforms previous techniques in detecting fundus regions,achieving 94.98%accuracy and 91.57%sensitivity,with a minimal error rate of 5.43%.It significantly reduces feature extraction time to 1.462 s and minimizes computational overhead,thereby improving operational efficiency and scalability.Ultimately,the proposed model enhances diagnostic precision and reduces errors,leading to more effective fundus dysfunction diagnosis and treatment.展开更多
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 im...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.展开更多
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 ...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.展开更多
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 ...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.展开更多
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 de...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.展开更多
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 ...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.展开更多
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...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.展开更多
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.H...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.展开更多
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 searche...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.展开更多
BACKGROUND Endoscopic resection of giant gastric leiomyomas,particularly in the fundus and cardia regions,is infrequently documented and presents a significant challenge for endoscopic surgery.CASE SUMMARY Herein,a ca...BACKGROUND Endoscopic resection of giant gastric leiomyomas,particularly in the fundus and cardia regions,is infrequently documented and presents a significant challenge for endoscopic surgery.CASE SUMMARY Herein,a case of a 59-year-old woman with a giant gastric leiomyoma was reported.The patient presented to the department of hepatological surgery with a complaint of right upper abdominal pain for one month and worsening for one week.The patient was diagnosed as gastric submucosal tumor(SMT),gallstone,and cholecystitis combined with computed tomography and gastroendoscopy prior to operation.Upon admission,following a multi-disciplinary treatment discussion,it was determined that the patient would undergo a laparoscopic cholecystectomy and endoscopic resection of gastric SMT.It took 3 hours to completely resect the lesion by Endoscopic submucosal excavation and endoscopic fullthickness resection,and about 3 hours to suture the wound and take out the lesion.The lesion,ginger-shaped and measuring 8 cm×5 cm,led to transient peritonitis post-surgery.With no cardiac complications,the patient was discharged one week after surgery.CONCLUSION Endoscopic resection of a giant leiomyoma in the cardiac fundus is feasible and suitable for skilled endoscopists.展开更多
Fundus neovascularization(FNV),as a hallmark pathology in the late stages of progression of various fundus diseases from diabetic retinopathy(DR)to age-related macular degeneration(AMD),en-compasses a wide range of ag...Fundus neovascularization(FNV),as a hallmark pathology in the late stages of progression of various fundus diseases from diabetic retinopathy(DR)to age-related macular degeneration(AMD),en-compasses a wide range of age groups.FNV has now emerged as a leading cause of vision loss globally,posing a huge burden on the world's public health and healthcare systems.The mainstays of clinical treat-ment of FNV for more than a decade have included laser photocoagulation,photodynamic therapy(PDT),and inhibitors targeting vascular endothelial growth factor(VEGF).Anti-VEGF drugs have been quite successful,and a significant portion of subsequent drug development has focused on direct or indirect effects on the VEGF signaling pathway.In addition,efficient fundus drug delivery systems for precise drug release control and minimal invasiveness or non-invasiveness remain major challenges of the treatment of FNV.This review provides a brief overview of current advances in clinical care and fundamental studies for the treatment of FNV,discussing current therapeutic options by means of two main aspects,anti-VEGF and anti-inflammation.The therapeutic strategy ranges from protein/peptide drugs to gene therapy in FNV and the prospects for the application of multi-pathway therapies.展开更多
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 f...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.展开更多
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 a...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.展开更多
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 cell...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.展开更多
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 exami...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.展开更多
AIM: To compare the postoperative visual acuity among eyes with proliferative diabetic retinopathy(PDR) of different stages after pars plana vitrectomy(PPV) in type 2 diabetic patients. METHODS: A retrospective study ...AIM: To compare the postoperative visual acuity among eyes with proliferative diabetic retinopathy(PDR) of different stages after pars plana vitrectomy(PPV) in type 2 diabetic patients. METHODS: A retrospective study was conducted for PDR eyes undergoing PPV in type 2 diabetic patients. All patients were divided into three groups based on Chinese Ocular Fundus Diseases Society(COFDS) classification for PDR: Group A(primary vitreous hemorrhage), Group B(primary fibrovascular proliferation) and Group C(primary vitreous hemorrhage and/or fibrovascular proliferative combined with retinal detachment). The postoperative visual acuity and the change between postoperative and preoperative visual acuity were compared among three groups. The associated risk factors for postoperative visual acuity were analyzed in the univariate and multiple linear aggression. RESULTS: In total, 195 eyes of 195 patients were collected in this study, including 71 eyes of 71 patients in Group A, 75 eyes of 75 patients in Group B and 49 eyes of 49 patients in Group C. The eyes in Group A got better postoperative best-corrected visual acuity(BCVA) compared to the eyes in Group B and C(0.48±0.48 vs 0.89±0.63, P<0.001;0.48±0.48 vs 1.04±0.67, P<0.001;respectively). The eyes in Group A got more improvement of BCVA compared to the eyes in Group B and C(1.07±0.70 vs 0.73±0.68, P=0.004;1.07±0.70 vs 0.77±0.78, P=0.024;respectively). In the multiple linear regression analysis, primary fibro-proliferative type(β=0.194, 95%CI=0.060-0.447, P=0.01), retinal detachment type(β=0.244, 95%CI=0.132-0.579, P=0.02), baseline log MAR BCVA(β=0.192, 95%CI=0.068-0.345, P=0.004), silicone oil tamponade(β=0.272, 95%CI=0.173-0.528, P<0.001) was positively correlated with postoperative log MAR BCVA. Eyes undergoing phacovitrectomy had better postoperative BCVA(β=-0.144, 95%CI=-0.389 to-0.027, P=0.025). CONCLUSION: PDR eyes of primary vitreous hemorrhage type usually have better visual acuity prognosis compared to primary fibrovascular proliferation type and retinal detachment type. COFDS classification for PDR may have a high prognostic value for postoperative visual outcome and surgical management indications.展开更多
AIM： To report the surgical result of pars plana vitrectomy（PPV） with air tamponade for rhegmatogenous retinal detachment（RRD） by ultra-widefield fundus imaging system. METHODS： Of 25 consecutive patients（25 e...AIM： To report the surgical result of pars plana vitrectomy（PPV） with air tamponade for rhegmatogenous retinal detachment（RRD） by ultra-widefield fundus imaging system. METHODS： Of 25 consecutive patients（25 eyes） with fresh primary RRD and causative retinal break and vitreous traction were presented. All the patients underwent PPV with air tamponade. Visual acuity（VA） was examined postoperatively and images were captured by ultrawidefield scanning laser ophthalmoscope system（Optos）. RESULTS： Initial reattachment was achieved in 25 cases（100%）. The air volume was 〉60% on the postoperative day（POD） 1. The ultra-widefield images showed that the retina was reattached in all air-filled eyes postoperatively. The retinal break and laser burns in the superior were detected in 22 of 25 eyes（88%）. A missed retinal hole was found under intravitreal air bubble in 1 case（4%）. The air volume was range from 40% to 60% on POD 3. A doublelayered image was seen in 25 of 25 eyes with intravitreal gas. Retinal breaks and laser burns around were seen in the intravitreal air. On POD 7, small bubble without effect was seen in 6 cases（24%） and bubble was completely disappeared in 4 cases（16%）. Small oval bubble in the superior area was observed in 15 cases（60%）. There were no missed and new retinal breaks and no retinal detachment in all cases on the POD 14 and 1 mo and last follow-up. Air disappeared completely on a mean of 9.84 d postoperatively. The mean final postoperative bestcorrected visual acuity（BCVA） was 0.35 log MAR. Mean final postoperative BCVA improved significantly relative to mean preoperative（P〈0.05）. Final VA of 0.3 log MAR or better was seen in 13 eyes. CONCLUSION： PPV with air tamponade is an effective management for fresh RRD with superior retinal breaks. The ultra-widefield fundus imaging can detect postoperative retinal breaks in air-filled eyes. It would be a useful facility for follow-up after PPV with air tamponade. Facedown position and acquired visual rehabilitation may be shorten.展开更多
AIM： To access the 10-year fundus tessellation progression in patients with retinal vein occlusion. METHODS： The Beijing Eye Study 2001/2011 is a populationbased longitudinal study. The study participants underwent ...AIM： To access the 10-year fundus tessellation progression in patients with retinal vein occlusion. METHODS： The Beijing Eye Study 2001/2011 is a populationbased longitudinal study. The study participants underwent a detailed physical and ophthalmic examination. Degree of fundus tessellation was graded by using fundus photographs of the macula and optic disc. Progression of fundus tessellation was calculated by fundus tessellation degree of 2011 minus degree of 2001. Fundus photographs were used for assessment of retinal vein occlusion. RESULTS： The Beijing Eye Study included 4403 subjects in 2001, 3468 subjects was repeated in 2011. Assessment of retinal vein obstruction and fundus tessellation progression were available for 2462 subjects（71.0%）, with 66 subjects fulfilled the diagnosis of retinal vein occlusion. Of the 66 participants, 59 participants with unilateral branch retinal vein occlusion, 5 participants with unilateral central retinal vein occlusion, 1 participant with bilateral branch retinal vein occlusion, and 1 participant with branch retinal vein occlusion in one eye and central retinal vein occlusion in the other eye. Mean degree of peripapillary fundus tessellation progression were significantly higher in the whole retinal vein occlusion group（0.33±0.39, P〈0.001）, central retinal vein occlusion group（0.71±0.8, P=0.025） and branch retinal vein occlusion group（0.29±0.34, P=0.006） than the control group（0.20±0.26）. After adjustment for age, prevalence of tilted disc, change of best corrected visual acuity, axial length, progression of peripapillary fundus tessellation was associated with the presence of retinal vein occlusion（P=0.004; regression coefficient B, 0.094; 95%CI, 0.029, 0.158; standardized coefficient B, 0.056）. As a corollary, after adjusting for smoking duration, systolic blood pressure, anterior corneal curvature, prevalence of RVO was associated with more peripapillary fundus tessellation progression（P〈0.001; regression coefficient B： 1.257; OR： 3.517; 95%CI： 1.777, 6.958）. CONCLUSION： Peripapillary fundus tessellation progresses faster in individuals with retinal vein occlusion. This may reflect the thinning and hypoperfusion of choroid in patients with retinal vein occlusion.展开更多
基金funded by Department of Robotics and Mechatronics Engineering,Kennesaw State University,Marietta,GA 30060,USA.
文摘Glaucoma,a chronic eye disease affecting millions worldwide,poses a substantial threat to eyesight and can result in permanent vision loss if left untreated.Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective.To address these challenges,this research proposes a computer-aided diagnosis(CAD)approach using Artificial Intelligence(AI)techniques for binary and multiclass classification of glaucoma stages.An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network(ConvNet)models–ResNet-50,VGG-16,and InceptionV3 is utilized in this paper.This fusion technique enhances diagnostic accuracy and robustness by ensemble-averaging the predictions from individual models,leveraging their complementary strengths.The objective of this work is to assess the model’s capability for early-stage glaucoma diagnosis.Classification is performed on a dataset collected from the Harvard Dataverse repository.With the proposed technique,for Normal vs.Advanced glaucoma classification,a validation accuracy of 98.04%and testing accuracy of 98.03%is achieved,with a specificity of 100%which outperforms stateof-the-art methods.For multiclass classification,the suggested ensemble approach achieved a precision and sensitivity of 97%,specificity,and testing accuracy of 98.57%and 96.82%,respectively.The proposed E-GlauNet model has significant potential in assisting ophthalmologists in the screening and fast diagnosis of glaucoma,leading to more reliable,efficient,and timely diagnosis,particularly for early-stage detection and staging of the disease.While the proposed method demonstrates high accuracy and robustness,the study is limited by the evaluation of a single dataset.Future work will focus on external validation across diverse datasets and enhancing interpretability using explainable AI techniques.
基金supported by the National Natural Science Foundation of China(62402009)the Science and Technology Development Fund of Macao(0013-2024-ITP1).
文摘Objective:Diabetic retinopathy(DR)screening using artificial intelligence(AI)has evolved significantly over the past decade.This study aimed to analyze research trends,developments,and patterns in AI-based fundus image DR screening from 2014 to 2024 through bibliometric analysis.Methods:The study used CiteSpace and Microsoft Excel to analyze 1,172 publications from the Web of Science Core Collection database.The analysis included publication trends over time,citation patterns,institutional collaborations,and the emergence of keywords.Results:From 2014-2022,there was a steady increase in the number of publications,reaching a peak in 2021.India(26%),China(20.05%),and the USA(9.98%)were the major contributors to research output in this field.Among the publication venues,IEEE ACCESS stood out as the leading one,with 44 articles published.The research landscape has evolved from traditional image processing techniques to deep learning approaches.In recent years,there has been a growing emphasis on multimodal AI models.The analysis identified three distinct phases in the development of AI-based DR screening:CNN-based systems(2014-2020),Vision Transformers and innovative learning paradigms(2020-2022),and large foundation models(2022-2024).Conclusions:The field has demonstrated a mature development in traditional AI approaches and is currently in the process of transitioning toward multimodal learning technologies.Future directions suggest an increased focus on the integration of telemedicine,innovative AI algorithms,and real-world implementation of these technologies in real-world settings.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408)supported by the Researchers Supporting Project Number(MHIRSP2024005)Almaarefa University,Riyadh,Saudi Arabia.
文摘Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves,blood vessels,retinal health,and the impact of diabetes on the optic nerves.Fundus disorders are a major global health concern,affecting millions of people worldwide due to their widespread occurrence.Fundus photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic retinopathy.As a result,accurate fundus detection is essential for early diagnosis and effective treatment,helping to prevent severe complications and improve patient outcomes.To address this need,this article introduces a Derivative Model for Fundus Detection using Deep NeuralNetworks(DMFD-DNN)to enhance diagnostic precision.Thismethod selects key features for fundus detection using the least derivative,which identifies features correlating with stored fundus images.Feature filtering relies on the minimum derivative,determined by extracting both similar and varying textures.In this research,the DNN model was integrated with the derivative model.Fundus images were segmented,features were extracted,and the DNN was iteratively trained to identify fundus regions reliably.The goal was to improve the precision of fundoscopic diagnosis by training the DNN incrementally,taking into account the least possible derivative across iterations,and using outputs from previous cycles.The hidden layer of the neural network operates on the most significant derivative,which may reduce precision across iterations.These derivatives are treated as inaccurate,and the model is subsequently trained using selective features and their corresponding extractions.The proposed model outperforms previous techniques in detecting fundus regions,achieving 94.98%accuracy and 91.57%sensitivity,with a minimal error rate of 5.43%.It significantly reduces feature extraction time to 1.462 s and minimizes computational overhead,thereby improving operational efficiency and scalability.Ultimately,the proposed model enhances diagnostic precision and reduces errors,leading to more effective fundus dysfunction diagnosis and treatment.
基金supported in part by the Gusu Innovation and Entrepreneurship Leading Talents in Suzhou City,grant numbers ZXL2021425 and ZXL2022476Doctor of Innovation and Entrepreneurship Program in Jiangsu Province,grant number JSSCBS20211440+6 种基金Jiangsu Province Key R&D Program,grant number BE2019682Natural Science Foundation of Jiangsu Province,grant number BK20200214National Key R&D Program of China,grant number 2017YFB0403701National Natural Science Foundation of China,grant numbers 61605210,61675226,and 62075235Youth Innovation Promotion Association of Chinese Academy of Sciences,grant number 2019320Frontier Science Research Project of the Chinese Academy of Sciences,grant number QYZDB-SSW-JSC03Strategic Priority Research Program of the Chinese Academy of Sciences,grant number XDB02060000.
文摘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.
基金Supported by the Project of National Natural Science Foundation of China,No.82270863Major Project of Anhui Provincial University Research Program,No.2023AH040400Joint Fund for Medical Artificial Intelligence,No.MAI2023Q026.
文摘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.
基金Supported by Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties(No.SZGSP014)Sanming Project of Medicine in Shenzhen(No.SZSM202011015)Shenzhen Science and Technology Planning Project(No.KCXFZ20211020163813019).
文摘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.
文摘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.
基金This research was funded by the National Natural Science Foundation of China(Nos.71762010,62262019,62162025,61966013,12162012)the Hainan Provincial Natural Science Foundation of China(Nos.823RC488,623RC481,620RC603,621QN241,620RC602,121RC536)+1 种基金the Haikou Science and Technology Plan Project of China(No.2022-016)the Project supported by the Education Department of Hainan Province,No.Hnky2021-23.
文摘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.
基金supported by the National Natural Science Foundation of China(82020108006 and 81730025 to Chen Zhao,U2001209 to Bo Yan)the Excellent Academic Leaders of Shanghai(18XD1401000 to Chen Zhao)the Natural Science Foundation of Shanghai,China(21ZR1406600 to Weimin Tan).
文摘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.
基金supported by the Natural National Science Foundation of China(62175156)the Science and technology innovation project of Shanghai Science and Technology Commission(22S31903000)Collaborative Innovation Project of Shanghai Institute of Technology(XTCX2022-27)。
文摘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.
基金Supported by 1.3.5 Project for Disciplines of Excellence,West China Hospital,Sichuan University(No.ZYJC21025).
文摘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.
文摘BACKGROUND Endoscopic resection of giant gastric leiomyomas,particularly in the fundus and cardia regions,is infrequently documented and presents a significant challenge for endoscopic surgery.CASE SUMMARY Herein,a case of a 59-year-old woman with a giant gastric leiomyoma was reported.The patient presented to the department of hepatological surgery with a complaint of right upper abdominal pain for one month and worsening for one week.The patient was diagnosed as gastric submucosal tumor(SMT),gallstone,and cholecystitis combined with computed tomography and gastroendoscopy prior to operation.Upon admission,following a multi-disciplinary treatment discussion,it was determined that the patient would undergo a laparoscopic cholecystectomy and endoscopic resection of gastric SMT.It took 3 hours to completely resect the lesion by Endoscopic submucosal excavation and endoscopic fullthickness resection,and about 3 hours to suture the wound and take out the lesion.The lesion,ginger-shaped and measuring 8 cm×5 cm,led to transient peritonitis post-surgery.With no cardiac complications,the patient was discharged one week after surgery.CONCLUSION Endoscopic resection of a giant leiomyoma in the cardiac fundus is feasible and suitable for skilled endoscopists.
基金supported by the National Natural Science Foundation of China(52325311).
文摘Fundus neovascularization(FNV),as a hallmark pathology in the late stages of progression of various fundus diseases from diabetic retinopathy(DR)to age-related macular degeneration(AMD),en-compasses a wide range of age groups.FNV has now emerged as a leading cause of vision loss globally,posing a huge burden on the world's public health and healthcare systems.The mainstays of clinical treat-ment of FNV for more than a decade have included laser photocoagulation,photodynamic therapy(PDT),and inhibitors targeting vascular endothelial growth factor(VEGF).Anti-VEGF drugs have been quite successful,and a significant portion of subsequent drug development has focused on direct or indirect effects on the VEGF signaling pathway.In addition,efficient fundus drug delivery systems for precise drug release control and minimal invasiveness or non-invasiveness remain major challenges of the treatment of FNV.This review provides a brief overview of current advances in clinical care and fundamental studies for the treatment of FNV,discussing current therapeutic options by means of two main aspects,anti-VEGF and anti-inflammation.The therapeutic strategy ranges from protein/peptide drugs to gene therapy in FNV and the prospects for the application of multi-pathway therapies.
文摘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.
文摘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.
基金Supported by National Natural Science Foundation of China,No.31171107,No.31071011 and No.31271236
文摘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.
基金Guangdong Provincial Science and Technology Projects,China(No.2011B031800105)
文摘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.
基金Supported in part by the National Science Foundation of Liaoning Province,China(No.2020-MS-360)Shenyang Science and Technology Bureau(No.RC210267)。
文摘AIM: To compare the postoperative visual acuity among eyes with proliferative diabetic retinopathy(PDR) of different stages after pars plana vitrectomy(PPV) in type 2 diabetic patients. METHODS: A retrospective study was conducted for PDR eyes undergoing PPV in type 2 diabetic patients. All patients were divided into three groups based on Chinese Ocular Fundus Diseases Society(COFDS) classification for PDR: Group A(primary vitreous hemorrhage), Group B(primary fibrovascular proliferation) and Group C(primary vitreous hemorrhage and/or fibrovascular proliferative combined with retinal detachment). The postoperative visual acuity and the change between postoperative and preoperative visual acuity were compared among three groups. The associated risk factors for postoperative visual acuity were analyzed in the univariate and multiple linear aggression. RESULTS: In total, 195 eyes of 195 patients were collected in this study, including 71 eyes of 71 patients in Group A, 75 eyes of 75 patients in Group B and 49 eyes of 49 patients in Group C. The eyes in Group A got better postoperative best-corrected visual acuity(BCVA) compared to the eyes in Group B and C(0.48±0.48 vs 0.89±0.63, P<0.001;0.48±0.48 vs 1.04±0.67, P<0.001;respectively). The eyes in Group A got more improvement of BCVA compared to the eyes in Group B and C(1.07±0.70 vs 0.73±0.68, P=0.004;1.07±0.70 vs 0.77±0.78, P=0.024;respectively). In the multiple linear regression analysis, primary fibro-proliferative type(β=0.194, 95%CI=0.060-0.447, P=0.01), retinal detachment type(β=0.244, 95%CI=0.132-0.579, P=0.02), baseline log MAR BCVA(β=0.192, 95%CI=0.068-0.345, P=0.004), silicone oil tamponade(β=0.272, 95%CI=0.173-0.528, P<0.001) was positively correlated with postoperative log MAR BCVA. Eyes undergoing phacovitrectomy had better postoperative BCVA(β=-0.144, 95%CI=-0.389 to-0.027, P=0.025). CONCLUSION: PDR eyes of primary vitreous hemorrhage type usually have better visual acuity prognosis compared to primary fibrovascular proliferation type and retinal detachment type. COFDS classification for PDR may have a high prognostic value for postoperative visual outcome and surgical management indications.
文摘AIM： To report the surgical result of pars plana vitrectomy（PPV） with air tamponade for rhegmatogenous retinal detachment（RRD） by ultra-widefield fundus imaging system. METHODS： Of 25 consecutive patients（25 eyes） with fresh primary RRD and causative retinal break and vitreous traction were presented. All the patients underwent PPV with air tamponade. Visual acuity（VA） was examined postoperatively and images were captured by ultrawidefield scanning laser ophthalmoscope system（Optos）. RESULTS： Initial reattachment was achieved in 25 cases（100%）. The air volume was 〉60% on the postoperative day（POD） 1. The ultra-widefield images showed that the retina was reattached in all air-filled eyes postoperatively. The retinal break and laser burns in the superior were detected in 22 of 25 eyes（88%）. A missed retinal hole was found under intravitreal air bubble in 1 case（4%）. The air volume was range from 40% to 60% on POD 3. A doublelayered image was seen in 25 of 25 eyes with intravitreal gas. Retinal breaks and laser burns around were seen in the intravitreal air. On POD 7, small bubble without effect was seen in 6 cases（24%） and bubble was completely disappeared in 4 cases（16%）. Small oval bubble in the superior area was observed in 15 cases（60%）. There were no missed and new retinal breaks and no retinal detachment in all cases on the POD 14 and 1 mo and last follow-up. Air disappeared completely on a mean of 9.84 d postoperatively. The mean final postoperative bestcorrected visual acuity（BCVA） was 0.35 log MAR. Mean final postoperative BCVA improved significantly relative to mean preoperative（P〈0.05）. Final VA of 0.3 log MAR or better was seen in 13 eyes. CONCLUSION： PPV with air tamponade is an effective management for fresh RRD with superior retinal breaks. The ultra-widefield fundus imaging can detect postoperative retinal breaks in air-filled eyes. It would be a useful facility for follow-up after PPV with air tamponade. Facedown position and acquired visual rehabilitation may be shorten.
基金Supported by the National Natural Science Foundation of China(No.81570891)Beijing Natural Science Foundation of China(No.7151003)+1 种基金Beijing Municipal Administration of Hospitals’Ascent Plan(No.DFL20150201)the Capital Health Research and Development of Special(No.2016-1-2051)
文摘AIM： To access the 10-year fundus tessellation progression in patients with retinal vein occlusion. METHODS： The Beijing Eye Study 2001/2011 is a populationbased longitudinal study. The study participants underwent a detailed physical and ophthalmic examination. Degree of fundus tessellation was graded by using fundus photographs of the macula and optic disc. Progression of fundus tessellation was calculated by fundus tessellation degree of 2011 minus degree of 2001. Fundus photographs were used for assessment of retinal vein occlusion. RESULTS： The Beijing Eye Study included 4403 subjects in 2001, 3468 subjects was repeated in 2011. Assessment of retinal vein obstruction and fundus tessellation progression were available for 2462 subjects（71.0%）, with 66 subjects fulfilled the diagnosis of retinal vein occlusion. Of the 66 participants, 59 participants with unilateral branch retinal vein occlusion, 5 participants with unilateral central retinal vein occlusion, 1 participant with bilateral branch retinal vein occlusion, and 1 participant with branch retinal vein occlusion in one eye and central retinal vein occlusion in the other eye. Mean degree of peripapillary fundus tessellation progression were significantly higher in the whole retinal vein occlusion group（0.33±0.39, P〈0.001）, central retinal vein occlusion group（0.71±0.8, P=0.025） and branch retinal vein occlusion group（0.29±0.34, P=0.006） than the control group（0.20±0.26）. After adjustment for age, prevalence of tilted disc, change of best corrected visual acuity, axial length, progression of peripapillary fundus tessellation was associated with the presence of retinal vein occlusion（P=0.004; regression coefficient B, 0.094; 95%CI, 0.029, 0.158; standardized coefficient B, 0.056）. As a corollary, after adjusting for smoking duration, systolic blood pressure, anterior corneal curvature, prevalence of RVO was associated with more peripapillary fundus tessellation progression（P〈0.001; regression coefficient B： 1.257; OR： 3.517; 95%CI： 1.777, 6.958）. CONCLUSION： Peripapillary fundus tessellation progresses faster in individuals with retinal vein occlusion. This may reflect the thinning and hypoperfusion of choroid in patients with retinal vein occlusion.
