AIM:To report the incidence of epiretinal membrane(ERM)formation following panretinal photocoagulation(PRP)as the treatment for diabetic retinopathy(DR).METHODS:Retrospective cross-sectional study of patient charts be...AIM:To report the incidence of epiretinal membrane(ERM)formation following panretinal photocoagulation(PRP)as the treatment for diabetic retinopathy(DR).METHODS:Retrospective cross-sectional study of patient charts between January 1^(st),2010 to January 1^(st),2017 with at least 1y follow-up data.All 809 patients treated with PRP for DR were evaluated for exclusion criteria and 73 eyes remained after exclusion for confounding variables related to ERM formation such as other procedures or diseases.Outcomes were determined through medical record review and masked review of optical coherence tomography(OCT)images.Cohen’s kappa was completed to determine if there was an agreement between masked retinal specialists on OCT evaluations.Univariate logistic regression was used to determine the unadjusted odds ratio for patient and procedural characteristics on the formation of ERMs.Multiple logistic regression was then completed on select variables that met the cutoff of 0.25 for a statistically significant contribution by the Wald test with the sequential addition of clinical variables that contributed positively to the model.RESULTS:Among the 73 eyes studied,9.6%formed an ERM with an average time to formation of 1.4y.The minimum power uses during PRP was found to be statistically significant between non-ERM formers and ERM formers(P=0.044).When adjusting for all selected variables aside from minimum power used,multiple logistic regression determined that for every 10 mW increase in minimum power used during PRP,there is an increase in log odds of 1.009(SE:0.003,P=0.014).CONCLUSION:The incidence of ERM formation in 2y following treatment of DR with PRP is roughly 1 in every 10 eyes treated.展开更多
AIM:To investigate the value of optical coherence tomography angiography(OCTA)indicators in the diagnosis of diabetic retinopathy(DR),and to provide patients with diabetic nephropathy(DN)with more sensitive OCTA scree...AIM:To investigate the value of optical coherence tomography angiography(OCTA)indicators in the diagnosis of diabetic retinopathy(DR),and to provide patients with diabetic nephropathy(DN)with more sensitive OCTA screening indicators to detect concurrent DR at an early stage.METHODS:A total of 200 patients who treated in the ophthalmology department of the Seventh Affiliated Hospital,Sun Yat-sen University from 2022 to 2023 were included,including 95 first-diagnosed DR patients and 105 patients without DR,and all patients underwent OCTA examination and a collection of demographics and renal function parameters.After a quality check,automated measurements of the foveal avascular zone area,vessel density(VD),and perfusion density(PD)of both 3 mm×3 mm and 6 mm×6 mm windows were obtained.RESULTS:Using random forest and multivariate Logistic regression methods,we developed a diagnostic model for DR based on 12 variables(age,FBG,SBP,DBP,HbA1c,ALT,ALP,urea/Scr,DM duration,HUA,DN,and CMT).Adding specific OCTA parameters enhanced the efficacy of the existing diagnostic model for DR(outer vessel density in 6 mm×6 mm window,AUC=0.837 vs 0.819,P=0.03).In the study of DN patients,the parameters in the 6 mm×6 mm window improved the diagnostic efficacy of DR(inner VD;outer VD;full VD;outer PD;full PD).CONCLUSION:The outer VD in the 6 mm×6 mm window can enhance the efficacy of the traditional DR diagnostic model.Meanwhile,compared with the 3 mm×3 mm window,the microvascular parameters in the 6 mm×6 mm window focusing on DN patients can be more sensitive to diagnosing the occurrence of DR.展开更多
Diabetic corneal neuropathy and diabetic retinopathy are ocular complications occurring in the context of diabetes mellitus.Diabetic corneal neuropathy refers to the progressive damage of corneal nerves.Diabetic retin...Diabetic corneal neuropathy and diabetic retinopathy are ocular complications occurring in the context of diabetes mellitus.Diabetic corneal neuropathy refers to the progressive damage of corneal nerves.Diabetic retinopathy has traditionally been considered as damage to the retinal microvasculature.However,growing evidence suggests that diabetic retinopathy is a complex neurovascular disorder resulting from dysfunction of the neurovascular unit,which includes both the retinal vascular structures and neural tissues.Diabetic retinopathy is one of the leading causes of blindness and is frequently screened for as part of diabetic ocular screening.However,diabetic corneal neuropathy is commonly overlooked and underdiagnosed,leading to severe ocular surface impairment.Several studies have found that these two conditions tend to occur together,and they share similarities in their pathogenesis pathways,being triggered by a status of chronic hyperglycemia.This review aims to discuss the interconnection between diabetic corneal neuropathy and diabetic retinopathy,whether diabetic corneal neuropathy precedes diabetic retinopathy,as well as the relation between the stage of diabetic retinopathy and the severity of corneal neuropathy.We also endeavor to explore the relevance of a corneal screening in diabetic eyes and the possibility of using corneal nerve measurements to monitor the progression of diabetic retinopathy.展开更多
Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transforma...Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.展开更多
This editorial discusses recent advancements and ongoing challenges in diabetic retinopathy,as reviewed by Morya et al in their comprehensive analysis.In their review,Morya et al discussed the pathophysiology of diabe...This editorial discusses recent advancements and ongoing challenges in diabetic retinopathy,as reviewed by Morya et al in their comprehensive analysis.In their review,Morya et al discussed the pathophysiology of diabetic retinopathy and explored novel treatment modalities.This editorial highlights the importance of these advancements and emphasizes the need for continued research and innovation for the enhanced management of diabetic retinopathy.It also reflects upon the implications of the authors’review findings for clinical practice and future research directions,underscoring the potential of emerging therapies for improving patient outcomes and providing a deeper understanding of disease mechanisms.展开更多
Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure p...Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment.Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment.However,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level.On the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological features.To address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual representation.Specifically,our model incorporates global features for extracting high-level contextual information from retinal images.Concurrently,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR diagnosis.These global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR classification.Our model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods.展开更多
Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual image processing, and consider...Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual image processing, and considerable time investment. Fortunately, the integration of deep learning and transfer learning offers invaluable assistance to medical practitioners. This study introduces an ensemble classification framework to detect and grade diabetic retinopathy into 5 classes leveraging the concepts of transfer learning and data fusion. It utilizes three benchmark datasets on diabetic retinopathy: APTOS 2019, IDRiD, and Messidor-2. Initially, these datasets are merged, resulting in a total of 5922 fundus images. Then this fused dataset undergoes pre-processing. Firstly, the images are cropped to remove unwanted regions. Then, Contrast Limited Adaptive Histogram Equalization is applied to improve image quality and fine details. To tackle class imbalance issues, Synthetic Minority Over Sampling technique is employed. Additionally, data augmentation techniques such as flipping, rotation, and zooming are used to increase dataset diversity. The dataset is split into training, validation, and testing sets at a ratio of 70:10:20. For classification, three pre-trained CNN models, EfficientNetB2, DenseNet121, and ResNet50, are fine-tuned. After these models are trained, an ensemble model is constructed by averaging the predictions of each model. Results show that the ensemble model achieved the highest test accuracy of 96.96% in grading diabetic retinopathy into 5 classes outperforming the individual pre-trained models. Furthermore, the ensemble model’s performance is compared with previously published approaches where this model demonstrated superior result.展开更多
AIM:To summarize 10 surgical pearls for managing proliferative diabetic retinopathy(PDR)adapted from the ancient Chinese allusions and analyze the application of these pearls in a real-world fashion.METHODS:Retrospect...AIM:To summarize 10 surgical pearls for managing proliferative diabetic retinopathy(PDR)adapted from the ancient Chinese allusions and analyze the application of these pearls in a real-world fashion.METHODS:Retrospective,noncomparative,interventional study.Ten surgical pearls were summarized and adapted from the ancient Chinese philosophy.Totally 346 cases(443 eyes)that underwent pars plana vitrectomy(PPV)at our hospial from January 2016 to February 2024 were selected.Flexible combinations of these pearls were applied according to the specific condition of each patient during surgeries.The efficacy and safety were analyzed,as well as the application frequencies according to the existence of tractional retinal detachment or not.RESULTS:A total of 473 times of surgeries were performed on all the patients.According to ancient Chinese allusions,ten surgical pearls were summarized from these surgeries.All PPVs went smoothly with the application of different combinations.Finally,almost all proliferative membranes were successfully peeled except for 10 patients(11 eyes),who went through strategy No.10(minimal membranectomy)that,only necessary relaxation incisions were made with most of the proliferative membranes left on purpose.The final visual acuities were mostly improved or stable(1.92±0.83 LogMAR preoperatively vs 1.16±0.85 LogMAR postoperatively,P<0.01).Postoperative complications mainly included early inflammatory responses in the anterior chamber and nuclear sclerosis.Recurrent vitreous hemorrhage,retinal detachment,and hyphema or neovascular glaucoma occurred in 1.9%(9/473),3.2%(15/473),0.4%(2/473)and 0.4%(2/473)times of PPVs,respectively.After 12/473(2.5%)times of PPVs,retinal detachment at the macular area still existed,and multiple times of subsequent PPVs were conducted.Final retinal attachment at the macular area was realized in 98.9%eyes.Those 5 unattached eyes were with heavily reproliferated membranes and subsequent tractional retinal detachment recurrence under the oil,and three of them were scleral buckled additionally.CONCLUSION:These 10 surgical strategies and technique pearls were mostly effective and safe in the management of severe PDR patients.They were relatively easy to be memorized and applicated once the meaning of each Chinese idiom was understood.One can use different combinations flexibly according to a patient’s specific condition.展开更多
BACKGROUND Diabetic retinopathy(DR)is a major microvascular complication of diabetes mellitus,leading to significant visual impairment and blindness among adults.Current treatment options are limited,making it essenti...BACKGROUND Diabetic retinopathy(DR)is a major microvascular complication of diabetes mellitus,leading to significant visual impairment and blindness among adults.Current treatment options are limited,making it essential to explore novel therapeutic strategies.Curcumol,a sesquiterpenoid derived from traditional Chinese medicine,has shown anti-inflammatory and anti-cancer properties,but its potential role in DR remains unclear.AIM To investigate the therapeutic effects of curcumol on the progression of DR and to elucidate the underlying molecular mechanisms,particularly its impact on the fat mass and obesity-associated(FTO)protein and the long non-coding RNA(lncRNA)MAF transcription factor G antisense RNA 1(MAFG-AS1).METHODS A streptozotocin-induced mouse model of DR was established,followed by treatment with curcumol.Retinal damage and inflammation were evaluated through histological analysis and molecular assays.Human retinal vascular endothelial cells were exposed to high glucose conditions to simulate diabetic environments in vitro.Cell proliferation,migration,and inflammation markers were assessed in curcumoltreated cells.LncRNA microarray analysis identified key molecules regulated by curcumol,and further experiments were conducted to confirm the involvement of FTO and MAFG-AS1 in the progression of DR.RESULTS Curcumol treatment significantly reduced blood glucose levels and alleviated retinal damage in streptozotocininduced DR mouse models.In high-glucose-treated human retinal vascular endothelial cells,curcumol inhibited cell proliferation,migration,and inflammatory responses.LncRNA microarray analysis identified MAFG-AS1 as the most upregulated lncRNA following curcumol treatment.Mechanistically,FTO demethylated MAFG-AS1,stabilizing its expression.Rescue experiments demonstrated that the protective effects of curcumol against DR were mediated through the FTO/MAFG-AS1 signaling pathway.CONCLUSION Curcumol ameliorates the progression of DR by modulating the FTO/MAFG-AS1 axis,providing a novel therapeutic pathway for the treatment of DR.These findings suggest that curcumol-based therapies could offer a promising alternative for managing this debilitating complication of diabetes.展开更多
Objective To establish and validate a novel diabetic retinopathy(DR)risk-prediction model using a whole-exome sequencing(WES)-based machine learning(ML)method.Methods WES was performed to identify potential single nuc...Objective To establish and validate a novel diabetic retinopathy(DR)risk-prediction model using a whole-exome sequencing(WES)-based machine learning(ML)method.Methods WES was performed to identify potential single nucleotide polymorphism(SNP)or mutation sites in a DR pedigree comprising 10 members.A prediction model was established and validated in a cohort of 420 type 2 diabetic patients based on both genetic and demographic features.The contribution of each feature was assessed using Shapley Additive explanation analysis.The efficacies of the models with and without SNP were compared.Results WES revealed that seven SNPs/mutations(rs116911833 in TRIM7,1997T>C in LRBA,1643T>C in PRMT10,rs117858678 in C9orf152,rs201922794 in CLDN25,rs146694895 in SH3GLB2,and rs201407189 in FANCC)were associated with DR.Notably,the model including rs146694895 and rs201407189 achieved better performance in predicting DR(accuracy:80.2%;sensitivity:83.3%;specificity:76.7%;area under the receiver operating characteristic curve[AUC]:80.0%)than the model without these SNPs(accuracy:79.4%;sensitivity:80.3%;specificity:78.3%;AUC:79.3%).Conclusion Novel SNP sites associated with DR were identified in the DR pedigree.Inclusion of rs146694895 and rs201407189 significantly enhanced the performance of the ML-based DR prediction model.展开更多
AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to devel...AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to develop a source-free domain adaptation(SFDA)method for efficient and effective DR identification from unlabeled data.METHODS:A multi-SFDA method was proposed for DR identification.This method integrates multiple source models,which are trained from the same source domain,to generate synthetic pseudo labels for the unlabeled target domain.Besides,a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances.Validation is performed using three color fundus photograph datasets(APTOS2019,DDR,and EyePACS).RESULTS:The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks.It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains.CONCLUSION:The multi-SFDA method provides an effective approach to overcome the challenges in DR identification.The method not only addresses difficulties in data labeling and privacy issues,but also reduces the need for large amounts of labeled data required by deep learning methods,making it a practical tool for early detection and preservation of vision in diabetic patients.展开更多
Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional ...Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.展开更多
AIM:To identify different metabolites,proteins and related pathways to elucidate the causes of proliferative diabetic retinopathy(PDR)and resistance to anti-vascular endothelial growth factor(VEGF)drugs,and to provide...AIM:To identify different metabolites,proteins and related pathways to elucidate the causes of proliferative diabetic retinopathy(PDR)and resistance to anti-vascular endothelial growth factor(VEGF)drugs,and to provide biomarkers for the diagnosis and treatment of PDR.METHODS:Vitreous specimens from patients with diabetic retinopathy were collected and analyzed by Liquid Chromatography-Mass Spectrometry(LC-MS/MS)analyses based on 4D label-free technology.Statistically differentially expressed proteins(DEPs),Gene Ontology(GO),Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway representation and protein interactions were analyzed.RESULTS:A total of 12 samples were analyzed.The proteomics results showed that a total of 58 proteins were identified as DEPs,of which 47 proteins were up-regulated and 11 proteins were down-regulated.We found that C1q and tumor necrosis factor related protein 5(C1QTNF5),Clusterin(CLU),tissue inhibitor of metal protease 1(TIMP1)and signal regulatory protein alpha(SIRPα)can all be specifically regulated after aflibercept treatment.GO functional analysis showed that some DEPs are related to changes in inflammatory regulatory pathways caused by PDR.In addition,protein-protein interaction(PPI)network evaluation revealed that TIMP1 plays a central role in neural regulation.In addition,CD47/SIRPαmay become a key target to resolve anti-VEGF drug resistance in PDR.CONCLUSION:Proteomic analysis is an approach of choice to explore the molecular mechanisms of PDR.Our data show that multiple proteins are differentially changed in PDR patients after intravitreal injection of aflibercept,among which C1QTNF5,CLU,TIMP1 and SIRPαmay become targets for future treatment of PDR and resolution of anti-VEGF resistance.展开更多
AIM:To assess the clinical efficacy and safety of combining panretinal photocoagulation(PRP)with intravitreal conbercept(IVC)injections for patients with high-risk proliferative diabetic retinopathy(HR-PDR)complicated...AIM:To assess the clinical efficacy and safety of combining panretinal photocoagulation(PRP)with intravitreal conbercept(IVC)injections for patients with high-risk proliferative diabetic retinopathy(HR-PDR)complicated by mild or moderate vitreous hemorrhage(VH),with or without diabetic macular edema(DME).METHODS:Patients diagnosed with VH with/without DME secondary to HR-PDR and received PRP combined with IVC injections were recruited in this retrospective study.Upon establishing the patient’s diagnosis,an initial IVC was performed,followed by prompt administration of PRP.In cases who significant bleeding persisted and impeded the laser operation,IVC was sustained before supplementing with PRP.Following the completion of PRP,patients were meticulously monitored for a minimum of six months.Laser therapy and IVC injections were judiciously adjusted based on fundus fluorescein angiography(FFA)results.Therapeutic effect and the incidence of adverse events were observed.RESULTS:Out of 42 patients(74 eyes),29 were male and 13 were female,with a mean age of 59.17±12.74y(33-84y).The diabetic history was between 1wk and 26y,and the interval between the onset of visual symptoms and diagnosis of HR-PDR was 1wk-1y.The affected eye received 2.59±1.87(1-10)IVC injections and underwent 5.5±1.02(4-8)sessions of PRP.Of these,68 eyes received PRP following 1 IVC injection,5 eyes after 2 IVC injections,and 1 eye after 3 IVC injections.Complete absorption of VH was observed in all 74 eyes 5-50wk after initial treatment,with resolution of DME in 51 eyes 3-48wk after initial treatment.A newly developed epiretinal membrane was noted in one eye.Visual acuity significantly improved in 25 eyes.No complications such as glaucoma,retinal detachment,or endophthalmitis were reported.CONCLUSION:The study suggests that the combination of PRP with IVC injections is an effective and safe modality for treating diabetic VH in patients with HR-PDR.展开更多
BACKGROUND No study has investigated the change regularity between age and subfoveal choroidal thickness(SFCT)in proliferative diabetic retinopathy(PDR).AIM To investigate the relationship between the SFCT and age in ...BACKGROUND No study has investigated the change regularity between age and subfoveal choroidal thickness(SFCT)in proliferative diabetic retinopathy(PDR).AIM To investigate the relationship between the SFCT and age in Chinese patients with PDR.METHODS This was a cross-sectional retrospective study.The participants were hospitalized individuals with type 2 diabetes who underwent vitrectomy for PDR.Contralateral eyes that met the criteria were included in the study.All necessary laboratory tests were performed at the time of admission.Central macular thickness(CMT)and SFCT were two quantitative assessments made using enhanced depth imaging optical coherence tomography.CMT was measured automatically and SFCT was measured manually with digital calipers provided by the Heidelberg Eye Explorer software.RESULTS The final analysis included a total of 234 individuals with PDR.The average age was 55.60 years old±10.03 years old,and 57.69%of the population was male.Univariate analysis revealed a significant negative connection between age and SFCT in patients with PDR[β=-2.44,95%confidence interval(95%CI):-3.46 to-1.42;P<0.0001].In the fully adjusted model,the correlation between SFCT and age remained steady(β=-1.68,95%CI:-2.97 to-0.39;P=0.0117).Spline smoothing showed that the relationship between SFCT and age in patients with PDR was non-linear,with an inflection point at 54 years of age.CONCLUSION Our findings suggest that age is a key determinant of choroidal thickness.The non-linear link between SFCT and age in PDR patients should be taken into account.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
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
Diabetes mellitus(DM)is a chronic metabolic non-communicable disease with the ability to cause serious microvascular and macrovascular complications throughout the body,including in the eye.Diabetic retinopathy(DR),pr...Diabetes mellitus(DM)is a chronic metabolic non-communicable disease with the ability to cause serious microvascular and macrovascular complications throughout the body,including in the eye.Diabetic retinopathy(DR),present in onethird of patients with diabetes,is a vision-threatening complication caused by uncontrolled diabetes,which greatly affects the retinal blood vessels and the lightsensitive inner retina,eventually leading to blindness.Several epidemiological studies elucidate that DR can vary by age of onset,duration,types of diabetes,and ethnicity.Recent studies show that the pathogenesis of diabetic retinopathy has spread its roots beyond merely being the result of hyperglycemia.The complexity of its etiopathology and diagnosis makes therapeutic intervention challenging.This review throws light on the pathological processes behind DR,the cascade of events that follow it,as well as the available and emerging treatment options.展开更多
As the prevalence of diabetic retinopathy continues to be on the rise, the Chronic Care Model (CCM) offers a transformative, patient-focused approach for efficient diabetic retinopathy care, emphasizing the need for u...As the prevalence of diabetic retinopathy continues to be on the rise, the Chronic Care Model (CCM) offers a transformative, patient-focused approach for efficient diabetic retinopathy care, emphasizing the need for urgent and innovative strategies in the United States. The model integrates community resources, healthcare organizations, self-management support, delivery system design, decision support, and clinical information systems. Addressing challenges and solutions, the model emphasizes proactive and preventive measures, collaborative multidisciplinary care, technological integration, and overcoming resistance to change. This paper proposes the utilization of the Chronic Care Model (CCM) as a possible public health framework for comprehensive management of diabetic retinopathy in the United States. Implementing the CCM offers a comprehensive approach to diabetic retinopathy care, addressing both individual and systemic factors, essential for improving public health outcomes.展开更多
Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are...Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision loss.Traditional detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and time-consuming.Therefore,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of DR.In this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB models.This approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.展开更多
文摘AIM:To report the incidence of epiretinal membrane(ERM)formation following panretinal photocoagulation(PRP)as the treatment for diabetic retinopathy(DR).METHODS:Retrospective cross-sectional study of patient charts between January 1^(st),2010 to January 1^(st),2017 with at least 1y follow-up data.All 809 patients treated with PRP for DR were evaluated for exclusion criteria and 73 eyes remained after exclusion for confounding variables related to ERM formation such as other procedures or diseases.Outcomes were determined through medical record review and masked review of optical coherence tomography(OCT)images.Cohen’s kappa was completed to determine if there was an agreement between masked retinal specialists on OCT evaluations.Univariate logistic regression was used to determine the unadjusted odds ratio for patient and procedural characteristics on the formation of ERMs.Multiple logistic regression was then completed on select variables that met the cutoff of 0.25 for a statistically significant contribution by the Wald test with the sequential addition of clinical variables that contributed positively to the model.RESULTS:Among the 73 eyes studied,9.6%formed an ERM with an average time to formation of 1.4y.The minimum power uses during PRP was found to be statistically significant between non-ERM formers and ERM formers(P=0.044).When adjusting for all selected variables aside from minimum power used,multiple logistic regression determined that for every 10 mW increase in minimum power used during PRP,there is an increase in log odds of 1.009(SE:0.003,P=0.014).CONCLUSION:The incidence of ERM formation in 2y following treatment of DR with PRP is roughly 1 in every 10 eyes treated.
文摘AIM:To investigate the value of optical coherence tomography angiography(OCTA)indicators in the diagnosis of diabetic retinopathy(DR),and to provide patients with diabetic nephropathy(DN)with more sensitive OCTA screening indicators to detect concurrent DR at an early stage.METHODS:A total of 200 patients who treated in the ophthalmology department of the Seventh Affiliated Hospital,Sun Yat-sen University from 2022 to 2023 were included,including 95 first-diagnosed DR patients and 105 patients without DR,and all patients underwent OCTA examination and a collection of demographics and renal function parameters.After a quality check,automated measurements of the foveal avascular zone area,vessel density(VD),and perfusion density(PD)of both 3 mm×3 mm and 6 mm×6 mm windows were obtained.RESULTS:Using random forest and multivariate Logistic regression methods,we developed a diagnostic model for DR based on 12 variables(age,FBG,SBP,DBP,HbA1c,ALT,ALP,urea/Scr,DM duration,HUA,DN,and CMT).Adding specific OCTA parameters enhanced the efficacy of the existing diagnostic model for DR(outer vessel density in 6 mm×6 mm window,AUC=0.837 vs 0.819,P=0.03).In the study of DN patients,the parameters in the 6 mm×6 mm window improved the diagnostic efficacy of DR(inner VD;outer VD;full VD;outer PD;full PD).CONCLUSION:The outer VD in the 6 mm×6 mm window can enhance the efficacy of the traditional DR diagnostic model.Meanwhile,compared with the 3 mm×3 mm window,the microvascular parameters in the 6 mm×6 mm window focusing on DN patients can be more sensitive to diagnosing the occurrence of DR.
文摘Diabetic corneal neuropathy and diabetic retinopathy are ocular complications occurring in the context of diabetes mellitus.Diabetic corneal neuropathy refers to the progressive damage of corneal nerves.Diabetic retinopathy has traditionally been considered as damage to the retinal microvasculature.However,growing evidence suggests that diabetic retinopathy is a complex neurovascular disorder resulting from dysfunction of the neurovascular unit,which includes both the retinal vascular structures and neural tissues.Diabetic retinopathy is one of the leading causes of blindness and is frequently screened for as part of diabetic ocular screening.However,diabetic corneal neuropathy is commonly overlooked and underdiagnosed,leading to severe ocular surface impairment.Several studies have found that these two conditions tend to occur together,and they share similarities in their pathogenesis pathways,being triggered by a status of chronic hyperglycemia.This review aims to discuss the interconnection between diabetic corneal neuropathy and diabetic retinopathy,whether diabetic corneal neuropathy precedes diabetic retinopathy,as well as the relation between the stage of diabetic retinopathy and the severity of corneal neuropathy.We also endeavor to explore the relevance of a corneal screening in diabetic eyes and the possibility of using corneal nerve measurements to monitor the progression of diabetic retinopathy.
文摘Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.
文摘This editorial discusses recent advancements and ongoing challenges in diabetic retinopathy,as reviewed by Morya et al in their comprehensive analysis.In their review,Morya et al discussed the pathophysiology of diabetic retinopathy and explored novel treatment modalities.This editorial highlights the importance of these advancements and emphasizes the need for continued research and innovation for the enhanced management of diabetic retinopathy.It also reflects upon the implications of the authors’review findings for clinical practice and future research directions,underscoring the potential of emerging therapies for improving patient outcomes and providing a deeper understanding of disease mechanisms.
基金supported by the National Natural Science Foundation of China(No.62376287)the International Science and Technology Innovation Joint Base of Machine Vision and Medical Image Processing in Hunan Province(2021CB1013)the Natural Science Foundation of Hunan Province(Nos.2022JJ30762,2023JJ70016).
文摘Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment.Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment.However,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level.On the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological features.To address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual representation.Specifically,our model incorporates global features for extracting high-level contextual information from retinal images.Concurrently,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR diagnosis.These global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR classification.Our model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods.
文摘Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual image processing, and considerable time investment. Fortunately, the integration of deep learning and transfer learning offers invaluable assistance to medical practitioners. This study introduces an ensemble classification framework to detect and grade diabetic retinopathy into 5 classes leveraging the concepts of transfer learning and data fusion. It utilizes three benchmark datasets on diabetic retinopathy: APTOS 2019, IDRiD, and Messidor-2. Initially, these datasets are merged, resulting in a total of 5922 fundus images. Then this fused dataset undergoes pre-processing. Firstly, the images are cropped to remove unwanted regions. Then, Contrast Limited Adaptive Histogram Equalization is applied to improve image quality and fine details. To tackle class imbalance issues, Synthetic Minority Over Sampling technique is employed. Additionally, data augmentation techniques such as flipping, rotation, and zooming are used to increase dataset diversity. The dataset is split into training, validation, and testing sets at a ratio of 70:10:20. For classification, three pre-trained CNN models, EfficientNetB2, DenseNet121, and ResNet50, are fine-tuned. After these models are trained, an ensemble model is constructed by averaging the predictions of each model. Results show that the ensemble model achieved the highest test accuracy of 96.96% in grading diabetic retinopathy into 5 classes outperforming the individual pre-trained models. Furthermore, the ensemble model’s performance is compared with previously published approaches where this model demonstrated superior result.
文摘AIM:To summarize 10 surgical pearls for managing proliferative diabetic retinopathy(PDR)adapted from the ancient Chinese allusions and analyze the application of these pearls in a real-world fashion.METHODS:Retrospective,noncomparative,interventional study.Ten surgical pearls were summarized and adapted from the ancient Chinese philosophy.Totally 346 cases(443 eyes)that underwent pars plana vitrectomy(PPV)at our hospial from January 2016 to February 2024 were selected.Flexible combinations of these pearls were applied according to the specific condition of each patient during surgeries.The efficacy and safety were analyzed,as well as the application frequencies according to the existence of tractional retinal detachment or not.RESULTS:A total of 473 times of surgeries were performed on all the patients.According to ancient Chinese allusions,ten surgical pearls were summarized from these surgeries.All PPVs went smoothly with the application of different combinations.Finally,almost all proliferative membranes were successfully peeled except for 10 patients(11 eyes),who went through strategy No.10(minimal membranectomy)that,only necessary relaxation incisions were made with most of the proliferative membranes left on purpose.The final visual acuities were mostly improved or stable(1.92±0.83 LogMAR preoperatively vs 1.16±0.85 LogMAR postoperatively,P<0.01).Postoperative complications mainly included early inflammatory responses in the anterior chamber and nuclear sclerosis.Recurrent vitreous hemorrhage,retinal detachment,and hyphema or neovascular glaucoma occurred in 1.9%(9/473),3.2%(15/473),0.4%(2/473)and 0.4%(2/473)times of PPVs,respectively.After 12/473(2.5%)times of PPVs,retinal detachment at the macular area still existed,and multiple times of subsequent PPVs were conducted.Final retinal attachment at the macular area was realized in 98.9%eyes.Those 5 unattached eyes were with heavily reproliferated membranes and subsequent tractional retinal detachment recurrence under the oil,and three of them were scleral buckled additionally.CONCLUSION:These 10 surgical strategies and technique pearls were mostly effective and safe in the management of severe PDR patients.They were relatively easy to be memorized and applicated once the meaning of each Chinese idiom was understood.One can use different combinations flexibly according to a patient’s specific condition.
文摘BACKGROUND Diabetic retinopathy(DR)is a major microvascular complication of diabetes mellitus,leading to significant visual impairment and blindness among adults.Current treatment options are limited,making it essential to explore novel therapeutic strategies.Curcumol,a sesquiterpenoid derived from traditional Chinese medicine,has shown anti-inflammatory and anti-cancer properties,but its potential role in DR remains unclear.AIM To investigate the therapeutic effects of curcumol on the progression of DR and to elucidate the underlying molecular mechanisms,particularly its impact on the fat mass and obesity-associated(FTO)protein and the long non-coding RNA(lncRNA)MAF transcription factor G antisense RNA 1(MAFG-AS1).METHODS A streptozotocin-induced mouse model of DR was established,followed by treatment with curcumol.Retinal damage and inflammation were evaluated through histological analysis and molecular assays.Human retinal vascular endothelial cells were exposed to high glucose conditions to simulate diabetic environments in vitro.Cell proliferation,migration,and inflammation markers were assessed in curcumoltreated cells.LncRNA microarray analysis identified key molecules regulated by curcumol,and further experiments were conducted to confirm the involvement of FTO and MAFG-AS1 in the progression of DR.RESULTS Curcumol treatment significantly reduced blood glucose levels and alleviated retinal damage in streptozotocininduced DR mouse models.In high-glucose-treated human retinal vascular endothelial cells,curcumol inhibited cell proliferation,migration,and inflammatory responses.LncRNA microarray analysis identified MAFG-AS1 as the most upregulated lncRNA following curcumol treatment.Mechanistically,FTO demethylated MAFG-AS1,stabilizing its expression.Rescue experiments demonstrated that the protective effects of curcumol against DR were mediated through the FTO/MAFG-AS1 signaling pathway.CONCLUSION Curcumol ameliorates the progression of DR by modulating the FTO/MAFG-AS1 axis,providing a novel therapeutic pathway for the treatment of DR.These findings suggest that curcumol-based therapies could offer a promising alternative for managing this debilitating complication of diabetes.
基金supported by the National Natural Science Foundation of China[Grant No.62206185]。
文摘Objective To establish and validate a novel diabetic retinopathy(DR)risk-prediction model using a whole-exome sequencing(WES)-based machine learning(ML)method.Methods WES was performed to identify potential single nucleotide polymorphism(SNP)or mutation sites in a DR pedigree comprising 10 members.A prediction model was established and validated in a cohort of 420 type 2 diabetic patients based on both genetic and demographic features.The contribution of each feature was assessed using Shapley Additive explanation analysis.The efficacies of the models with and without SNP were compared.Results WES revealed that seven SNPs/mutations(rs116911833 in TRIM7,1997T>C in LRBA,1643T>C in PRMT10,rs117858678 in C9orf152,rs201922794 in CLDN25,rs146694895 in SH3GLB2,and rs201407189 in FANCC)were associated with DR.Notably,the model including rs146694895 and rs201407189 achieved better performance in predicting DR(accuracy:80.2%;sensitivity:83.3%;specificity:76.7%;area under the receiver operating characteristic curve[AUC]:80.0%)than the model without these SNPs(accuracy:79.4%;sensitivity:80.3%;specificity:78.3%;AUC:79.3%).Conclusion Novel SNP sites associated with DR were identified in the DR pedigree.Inclusion of rs146694895 and rs201407189 significantly enhanced the performance of the ML-based DR prediction model.
基金Supported by the Fund for Shanxi“1331 Project”and Supported by Fundamental Research Program of Shanxi Province(No.202203021211006)the Key Research,Development Program of Shanxi Province(No.201903D311009)+4 种基金the Key Research Program of Taiyuan University(No.21TYKZ01)the Open Fund of Shanxi Province Key Laboratory of Ophthalmology(No.2023SXKLOS04)Shenzhen Fund for Guangdong Provincial High-Level Clinical Key Specialties(No.SZGSP014)Sanming Project of Medicine in Shenzhen(No.SZSM202311012)Shenzhen Science and Technology Planning Project(No.KCXFZ20211020163813019).
文摘AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to develop a source-free domain adaptation(SFDA)method for efficient and effective DR identification from unlabeled data.METHODS:A multi-SFDA method was proposed for DR identification.This method integrates multiple source models,which are trained from the same source domain,to generate synthetic pseudo labels for the unlabeled target domain.Besides,a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances.Validation is performed using three color fundus photograph datasets(APTOS2019,DDR,and EyePACS).RESULTS:The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks.It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains.CONCLUSION:The multi-SFDA method provides an effective approach to overcome the challenges in DR identification.The method not only addresses difficulties in data labeling and privacy issues,but also reduces the need for large amounts of labeled data required by deep learning methods,making it a practical tool for early detection and preservation of vision in diabetic patients.
基金supported in part by the Research on the Application of Multimodal Artificial Intelligence in Diagnosis and Treatment of Type 2 Diabetes under Grant No.2020SK50910in part by the Hunan Provincial Natural Science Foundation of China under Grant 2023JJ60020.
文摘Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.
基金Supported by Tianjin Key Medical Discipline Specialty Construction Project(No.TJYXZDXK-016A)Henan Provincial Department of Science and Technology(No.LHGJ20200802).
文摘AIM:To identify different metabolites,proteins and related pathways to elucidate the causes of proliferative diabetic retinopathy(PDR)and resistance to anti-vascular endothelial growth factor(VEGF)drugs,and to provide biomarkers for the diagnosis and treatment of PDR.METHODS:Vitreous specimens from patients with diabetic retinopathy were collected and analyzed by Liquid Chromatography-Mass Spectrometry(LC-MS/MS)analyses based on 4D label-free technology.Statistically differentially expressed proteins(DEPs),Gene Ontology(GO),Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway representation and protein interactions were analyzed.RESULTS:A total of 12 samples were analyzed.The proteomics results showed that a total of 58 proteins were identified as DEPs,of which 47 proteins were up-regulated and 11 proteins were down-regulated.We found that C1q and tumor necrosis factor related protein 5(C1QTNF5),Clusterin(CLU),tissue inhibitor of metal protease 1(TIMP1)and signal regulatory protein alpha(SIRPα)can all be specifically regulated after aflibercept treatment.GO functional analysis showed that some DEPs are related to changes in inflammatory regulatory pathways caused by PDR.In addition,protein-protein interaction(PPI)network evaluation revealed that TIMP1 plays a central role in neural regulation.In addition,CD47/SIRPαmay become a key target to resolve anti-VEGF drug resistance in PDR.CONCLUSION:Proteomic analysis is an approach of choice to explore the molecular mechanisms of PDR.Our data show that multiple proteins are differentially changed in PDR patients after intravitreal injection of aflibercept,among which C1QTNF5,CLU,TIMP1 and SIRPαmay become targets for future treatment of PDR and resolution of anti-VEGF resistance.
基金Supported by Research Grants from the fund of Suzhou Kowloon Hospital(No.SZJL202106).
文摘AIM:To assess the clinical efficacy and safety of combining panretinal photocoagulation(PRP)with intravitreal conbercept(IVC)injections for patients with high-risk proliferative diabetic retinopathy(HR-PDR)complicated by mild or moderate vitreous hemorrhage(VH),with or without diabetic macular edema(DME).METHODS:Patients diagnosed with VH with/without DME secondary to HR-PDR and received PRP combined with IVC injections were recruited in this retrospective study.Upon establishing the patient’s diagnosis,an initial IVC was performed,followed by prompt administration of PRP.In cases who significant bleeding persisted and impeded the laser operation,IVC was sustained before supplementing with PRP.Following the completion of PRP,patients were meticulously monitored for a minimum of six months.Laser therapy and IVC injections were judiciously adjusted based on fundus fluorescein angiography(FFA)results.Therapeutic effect and the incidence of adverse events were observed.RESULTS:Out of 42 patients(74 eyes),29 were male and 13 were female,with a mean age of 59.17±12.74y(33-84y).The diabetic history was between 1wk and 26y,and the interval between the onset of visual symptoms and diagnosis of HR-PDR was 1wk-1y.The affected eye received 2.59±1.87(1-10)IVC injections and underwent 5.5±1.02(4-8)sessions of PRP.Of these,68 eyes received PRP following 1 IVC injection,5 eyes after 2 IVC injections,and 1 eye after 3 IVC injections.Complete absorption of VH was observed in all 74 eyes 5-50wk after initial treatment,with resolution of DME in 51 eyes 3-48wk after initial treatment.A newly developed epiretinal membrane was noted in one eye.Visual acuity significantly improved in 25 eyes.No complications such as glaucoma,retinal detachment,or endophthalmitis were reported.CONCLUSION:The study suggests that the combination of PRP with IVC injections is an effective and safe modality for treating diabetic VH in patients with HR-PDR.
基金Supported by the 1.3.5 Project for Disciplines of Excellence,West China Hospital,Sichuan University,No.ZYJC21025.
文摘BACKGROUND No study has investigated the change regularity between age and subfoveal choroidal thickness(SFCT)in proliferative diabetic retinopathy(PDR).AIM To investigate the relationship between the SFCT and age in Chinese patients with PDR.METHODS This was a cross-sectional retrospective study.The participants were hospitalized individuals with type 2 diabetes who underwent vitrectomy for PDR.Contralateral eyes that met the criteria were included in the study.All necessary laboratory tests were performed at the time of admission.Central macular thickness(CMT)and SFCT were two quantitative assessments made using enhanced depth imaging optical coherence tomography.CMT was measured automatically and SFCT was measured manually with digital calipers provided by the Heidelberg Eye Explorer software.RESULTS The final analysis included a total of 234 individuals with PDR.The average age was 55.60 years old±10.03 years old,and 57.69%of the population was male.Univariate analysis revealed a significant negative connection between age and SFCT in patients with PDR[β=-2.44,95%confidence interval(95%CI):-3.46 to-1.42;P<0.0001].In the fully adjusted model,the correlation between SFCT and age remained steady(β=-1.68,95%CI:-2.97 to-0.39;P=0.0117).Spline smoothing showed that the relationship between SFCT and age in patients with PDR was non-linear,with an inflection point at 54 years of age.CONCLUSION Our findings suggest that age is a key determinant of choroidal thickness.The non-linear link between SFCT and age in PDR patients should be taken into account.
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 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.
文摘Diabetes mellitus(DM)is a chronic metabolic non-communicable disease with the ability to cause serious microvascular and macrovascular complications throughout the body,including in the eye.Diabetic retinopathy(DR),present in onethird of patients with diabetes,is a vision-threatening complication caused by uncontrolled diabetes,which greatly affects the retinal blood vessels and the lightsensitive inner retina,eventually leading to blindness.Several epidemiological studies elucidate that DR can vary by age of onset,duration,types of diabetes,and ethnicity.Recent studies show that the pathogenesis of diabetic retinopathy has spread its roots beyond merely being the result of hyperglycemia.The complexity of its etiopathology and diagnosis makes therapeutic intervention challenging.This review throws light on the pathological processes behind DR,the cascade of events that follow it,as well as the available and emerging treatment options.
文摘As the prevalence of diabetic retinopathy continues to be on the rise, the Chronic Care Model (CCM) offers a transformative, patient-focused approach for efficient diabetic retinopathy care, emphasizing the need for urgent and innovative strategies in the United States. The model integrates community resources, healthcare organizations, self-management support, delivery system design, decision support, and clinical information systems. Addressing challenges and solutions, the model emphasizes proactive and preventive measures, collaborative multidisciplinary care, technological integration, and overcoming resistance to change. This paper proposes the utilization of the Chronic Care Model (CCM) as a possible public health framework for comprehensive management of diabetic retinopathy in the United States. Implementing the CCM offers a comprehensive approach to diabetic retinopathy care, addressing both individual and systemic factors, essential for improving public health outcomes.
文摘Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision loss.Traditional detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and time-consuming.Therefore,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of DR.In this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB models.This approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.
