Automatic diagnosis of diabetic retinopathy using vision transformer based on wide-field optical coherence tomography angiography

Diabetic retinopathy(DR)is one of the major causes of visual impairment in adults with diabetes.Optical coherence tomography angiography(OCTA)is nowadays widely used as the golden criterion for diagnosing DR.Recently,wide-field OCTA(WF-OCTA)provided more abundant information including that of the peripheral retinal degenerative changes and it can contribute in accurately diagnosing DR.The need for an automatic DR diagnostic system based on WF-OCTA pictures attracts more and more attention due to the large diabetic population and the prevalence of retinopathy cases.In this study,automatic diagnosis of DR using vision transformer was performed using WF-OCTA images(12 mm×12 mm single-scan)centered on the fovea as the dataset.WF-OCTA images were automatically classified into four classes:No DR,mild nonproliferative diabetic retinopathy(NPDR),moderate to severe NPDR,and proliferative diabetic retinopathy(PDR).The proposed method for detecting DR on the test set achieves accuracy of 99.55%,sensitivity of 99.49%,and specificity of 99.57%.The accuracy of the method for DR staging reaches up to 99.20%,which has been proven to be higher than that attained by classical convolutional neural network models.Results show that the automatic diagnosis of DR based on vision transformer and WF-OCTA pictures is more effective for detecting and staging DR.

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

Diabetic retinopathy(DR)is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide.Early detection and treatment can effectively delay vision decline and even blindness in patients with DR.In recent years,artificial intelligence(AI)models constructed by machine learning and deep learning(DL)algorithms have been widely used in ophthalmology research,especially in diagnosing and treating ophthalmic diseases,particularly DR.Regarding DR,AI has mainly been used in its diagnosis,grading,and lesion recognition and segmentation,and good research and application results have been achieved.This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.

Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

In recent times,Internet of Things(IoT)and Deep Learning(DL)mod-els have revolutionized the diagnostic procedures of Diabetic Retinopathy(DR)in its early stages that can save the patient from vision loss.At the same time,the recent advancements made in Machine Learning(ML)and DL models help in developing Computer Aided Diagnosis(CAD)models for DR recognition and grading.In this background,the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network(ODBN)model i.e.,NS-ODBN model for diagnosis of DR.The presented model involves Interval Neutrosophic Set(INS)technique to dis-tinguish the diseased areas in fundus image.In addition,three feature extraction techniques such as histogram features,texture features,and wavelet features are used in this study.Besides,Optimal Deep Belief Network(ODBN)model is utilized as a classification model for DR.ODBN model involves Shuffled Shepherd Optimization(SSO)algorithm to regulate the hyperparameters of DBN technique in an optimal manner.The utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model significantly.The presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation metrics.The resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.

Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

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

Algorithm of automatic identification of diabetic retinopathy foci based on ultra-widefield scanning laser ophthalmoscopy

AIM:To propose an algorithm for automatic detection of diabetic retinopathy(DR)lesions based on ultra-widefield scanning laser ophthalmoscopy(SLO).METHODS:The algorithm utilized the FasterRCNN(Faster Regions with CNN features)+ResNet50(Residua Network 50)+FPN(Feature Pyramid Networks)method for detecting hemorrhagic spots,cotton wool spots,exudates,and microaneurysms in DR ultra-widefield SLO.Subimage segmentation combined with a deeper residual network FasterRCNN+ResNet50 was employed for feature extraction to enhance intelligent learning rate.Feature fusion was carried out by the feature pyramid network FPN,which significantly improved lesion detection rates in SLO fundus images.RESULTS:By analyzing 1076 ultra-widefield SLO images provided by our hospital,with a resolution of 2600×2048 dpi,the accuracy rates for hemorrhagic spots,cotton wool spots,exudates,and microaneurysms were found to be 87.23%,83.57%,86.75%,and 54.94%,respectively.CONCLUSION:The proposed algorithm demonstrates intelligent detection of DR lesions in ultra-widefield SLO,providing significant advantages over traditional fundus color imaging intelligent diagnosis algorithms.

Risk evaluation for diabetic retinopathy in Chinese renalbiopsied type 2 diabetes mellitus patients

AIM:To investigate diabetic retinopathy(DR)prevalence in Chinese renal-biopsied type 2 diabetes mellitus(T2DM)patients with kidney dysfunction,and to further evaluate its relationship with diabetic nephropathy(DN)incidence and the risk factors for DR development in this population.METHODS:A total of 84 renal-biopsied T2DM patients were included.Fundus and imaging examinations were employed for DR diagnosis.Demographic information and clinical measures along with renal histopathology were analyzed for comparisons between the DR and non-DR groups.Risk factors on DR development were analyzed with multiple logistic regression.RESULTS:DR prevalence was 50%in total.The incidences of DN,non-diabetic renal disease(NDRD)and mixed-type pathology were 47.6%,19.0%and 33.3%in the DR group respectively,while 11.9%,83.3%and 4.8%in the non-DR group.Systolic blood pressure,ratio of urinary albumin to creatine ratio,urinary albumin,24-hours urinary protein,the incidence and severity of DN histopathology were found statistically increased in the DR group.Multiple logistic regression analysis showed histopathological DN incidence significantly increased the risk of DR development[odds ratio(OR)=21.664,95%confidential interval(CI)5.588 to 83.991,P<0.001 for DN,and OR=45.475,95%CI 6.949 to 297.611,P<0.001 for mixed-type,respectively,in reference to (NDRD)],wherein DN severity positively correlated.CONCLUSION:Renal histopathological evidence indicates DN incidence and severity increases the risk of DR development in Chinese T2DM patients inexperienced of regular fundus examinations.

TCERG1L hypermethylation is a risk factor of diabetic retinopathy in Chinese children with type 1 diabetes

●AIM:To identify the differential methylation sites(DMS)and their according genes associated with diabetic retinopathy(DR)development in type 1 diabetes(T1DM)children.●METHODS:This study consists of two surveys.A total of 40 T1DM children was included in the first survey.Because no participant has DR,retina thinning was used as a surrogate indicator for DR.The lowest 25%participants with the thinnest macular retinal thickness were included into the case group,and the others were controls.The DNA methylation status was assessed by the Illumina methylation 850K array BeadChip assay,and compared between the case and control groups.Four DMS with a potential role in diabetes were identified.The second survey included 27 T1DM children,among which four had DR.The methylation patterns of the four DMS identified by 850K were compared between participants with and without DR by pyrosequencing.●RESULTS:In the first survey,the 850K array revealed 751 sites significantly and differentially methylated in the case group comparing with the controls(|Δβ|>0.1 and Adj.P<0.05),and 328 of these were identified with a significance of Adj.P<0.01.Among these,319 CpG sites were hypermethylated and 432 were hypomethylated in the case group relative to the controls.Pyrosequencing revealed that the transcription elongation regulator 1 like(TCERG1L,cg07684215)gene was hypermethylated in the four T1DM children with DR(P=0.018),which was consistent with the result from the first survey.The methylation status of the other three DMS(cg26389052,cg25192647,and cg05413694)showed no difference(all P>0.05)between participants with and without DR.●CONCLUSION:The hypermethylation of the TCERG1L gene is a risk factor for DR development in Chinese children with T1DM.

Retinal vascular morphological characteristics in diabetic retinopathy: an artificial intelligence study using a transfer learning system to analyze ultra-wide field images

AIM:To investigate the morphological characteristics of retinal vessels in patients with different severity of diabetic retinopathy(DR)and in patients with or without diabetic macular edema(DME).METHODS:The 239 eyes of DR patients and 100 eyes of healthy individuals were recruited for the study.The severity of DR patients was graded as mild,moderate and severe non-proliferative diabetic retinopathy(NPDR)according to the international clinical diabetic retinopathy(ICDR)disease severity scale classification,and retinal vascular morphology was quantitatively analyzed in ultra-wide field images using RU-net and transfer learning methods.The presence of DME was determined by optical coherence tomography(OCT),and differences in vascular morphological characteristics were compared between patients with and without DME.RESULTS:Retinal vessel segmentation using RU-net and transfer learning system had an accuracy of 99%and a Dice metric of 0.76.Compared with the healthy group,the DR group had smaller vessel angles(33.68±3.01 vs 37.78±1.60),smaller fractal dimension(Df)values(1.33±0.05 vs 1.41±0.03),less vessel density(1.12±0.44 vs 2.09±0.36)and fewer vascular branches(206.1±88.8 vs 396.5±91.3),all P<0.001.As the severity of DR increased,Df values decreased,P=0.031.No significant difference between the DME and non-DME groups were observed in vascular morphological characteristics.CONCLUSION:In this study,an artificial intelligence retinal vessel segmentation system is used with 99%accuracy,thus providing with relatively satisfactory performance in the evaluation of quantitative vascular morphology.DR patients have a tendency of vascular occlusion and dropout.The presence of DME does not compromise the integral retinal vascular pattern.

Construction and validation of a neovascular glaucoma nomogram in patients with diabetic retinopathy after pars plana vitrectomy

BACKGROUND Neovascular glaucoma(NVG)is likely to occur after pars plana vitrectomy(PPV)for diabetic retinopathy(DR)in some patients,thus reducing the expected benefit.Understanding the risk factors for NVG occurrence and building effective risk prediction models are currently required for clinical research.AIM To develop a visual risk profile model to explore factors influencing DR after surgery.METHODS We retrospectively selected 151 patients with DR undergoing PPV.The patients were divided into the NVG(NVG occurrence)and No-NVG(No NVG occurrence)groups according to the occurrence of NVG within 6 months after surgery.Independent risk factors for postoperative NVG were screened by logistic regression.A nomogram prediction model was established using R software,and the model’s prediction accuracy was verified internally and externally,involving the receiver operator characteristic curve and correction curve.RESULTS After importing the data into a logistic regression model,we concluded that a posterior capsular defect,preoperative vascular endothelial growth factor≥302.90 pg/mL,glycosylated hemoglobin≥9.05%,aqueous fluid interleukin 6(IL-6)≥53.27 pg/mL,and aqueous fluid IL-10≥9.11 pg/mL were independent risk factors for postoperative NVG in patients with DR(P<0.05).A nomogram model was established based on the aforementioned independent risk factors,and a computer simulation repeated sampling method was used to internally and externally verify the nomogram model.The area under the curve(AUC),sensitivity,and specificity of the model were 0.962[95%confidence interval(95%CI):0.932-0.991],91.5%,and 82.3%,respectively.The AUC,sensitivity,and specificity of the external validation were 0.878(95%CI:0.746-0.982),66.7%,and 95.7%,respectively.CONCLUSION A nomogram constructed based on the risk factors for postoperative NVG in patients with DR has a high prediction accuracy.This study can help formulate relevant preventive and treatment measures.

Diabetic retinopathy identification based on multi-sourcefree domain adaptation

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.

Proteomic study of vitreous in proliferative diabetic retinopathy patients after treatment with aflibercept:a quantitative analysis based on 4D label-free technique

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.

Intravitreal conbercept injection with panretinal photocoagulation for high-risk proliferative diabetic retinopathy with vitreous hemorrhage

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.

Systemic immune-inflammation index,neutrophilto-lymphocyte ratio,and platelet-to-lymphocyte ratio in patients with type 2 diabetes at different stages of diabetic retinopathy

AIM:To investigate systemic immune-inflammation index(SII),neutrophil-to-lymphocyte ratio(NLR),and plateletto-lymphocyte ratio(PLR)levels in patients with type 2 diabetes at different stages of diabetic retinopathy(DR).METHODS:This retrospective study included 141 patients with type 2 diabetes mellitus(DM):45 without diabetic retinopathy(NDR),47 with non-proliferative diabetic retinopathy(NPDR),and 49 with proliferative diabetic retinopathy(PDR).Complete blood counts were obtained,and NLR,PLR,and SII were calculated.The study analysed the ability of inflammatory markers to predict DR using receiver operating characteristic(ROC)curves.The relationships between DR stages and SII,PLR,and NLP were assessed using multivariate logistic regression.RESULTS:The average NLR,PLR,and SII were higher in the PDR group than in the NPDR group(P=0.011,0.043,0.009,respectively);higher in the NPDR group than in the NDR group(P<0.001 for all);and higher in the PDR group than in the NDR group(P<0.001 for all).In the ROC curve analysis,the NLR,PLR,and SII were significant predictors of DR(P<0.001 for all).The highest area under the curve(AUC)was for the PLR(0.929 for PLR,0.925 for SII,and 0.821 for NLR).Multivariate regression analysis indicated that NLR,PLR,and SII were statistically significantly positive and independent predictors for the DR stages in patients with DM[odds ratio(OR)=1.122,95%confidence interval(CI):0.200–2.043,P<0.05;OR=0.038,95%CI:0.018–0.058,P<0.05;OR=0.007,95%CI:0.001–0.01,P<0.05,respectively).CONCLUSION:The NLR,PLR,and SII may be used as predictors of DR.

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

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.

HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy

AIM:To develop a classifier for traditional Chinese medicine(TCM)syndrome differentiation of diabetic retinopathy(DR),using optimized machine learning algorithms,which can provide the basis for TCM objective and intelligent syndrome differentiation.METHODS:Collated data on real-world DR cases were collected.A variety of machine learning methods were used to construct TCM syndrome classification model,and the best performance was selected as the basic model.Genetic Algorithm(GA)was used for feature selection to obtain the optimal feature combination.Harris Hawk Optimization(HHO)was used for parameter optimization,and a classification model based on feature selection and parameter optimization was constructed.The performance of the model was compared with other optimization algorithms.The models were evaluated with accuracy,precision,recall,and F1 score as indicators.RESULTS:Data on 970 cases that met screening requirements were collected.Support Vector Machine(SVM)was the best basic classification model.The accuracy rate of the model was 82.05%,the precision rate was 82.34%,the recall rate was 81.81%,and the F1 value was 81.76%.After GA screening,the optimal feature combination contained 37 feature values,which was consistent with TCM clinical practice.The model based on optimal combination and SVM(GA_SVM)had an accuracy improvement of 1.92%compared to the basic classifier.SVM model based on HHO and GA optimization(HHO_GA_SVM)had the best performance and convergence speed compared with other optimization algorithms.Compared with the basic classification model,the accuracy was improved by 3.51%.CONCLUSION:HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of DR.It provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

On implications of somatostatin in diabetic retinopathy

Somatostatin,a naturally produced neuroprotective peptide,depresses excitatory neurotransmission and exerts anti-proliferative and anti-inflammatory effects on the retina.In this review,we summarize the progress of somatostatin treatment of diabetic retinopathy through analysis of relevant studies published from February 2019 to February 2023 extracted from the PubMed and Google Scholar databases.Insufficient neuroprotection,which occurs as a consequence of declined expression or dysregulation of retinal somatostatin in the very early stages of diabetic retinopathy,triggers retinal neurovascular unit impairment and microvascular damage.Somatostatin replacement is a promising treatment for retinal neurodegeneration in diabetic retinopathy.Numerous pre-clinical and clinical trials of somatostatin analog treatment for early diabetic retinopathy have been initiated.In one such trial(EUROCONDOR),topical administration of somatostatin was found to exert neuroprotective effects in patients with pre-existing retinal neurodysfunction,but had no impact on the onset of diabetic retinopathy.Overall,we concluded that somatostatin restoration may be especially beneficial for the growing population of patients with early-stage retinopathy.In order to achieve early prevention of diabetic retinopathy initiation,and thereby salvage visual function before the appearance of moderate non-proliferative diabetic retinopathy,several issues need to be addressed.These include the needs to:a)update and standardize the retinal screening scheme to incorporate the detection of early neurodegeneration,b)identify patient subgroups who would benefit from somatostatin analog supplementation,c)elucidate the interactions of somatostatin,particularly exogenously-delivered somatostatin analogs,with other retinal peptides in the context of hyperglycemia,and d)design safe,feasible,low cost,and effective administration routes.

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

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

Quantifying peripapillary vessel density and retinal nerve fibre layer in type 1 diabetic children without clinically detectable retinopathy using OCTA

AIM:To quantify changes in radial peripapillary capillary vessel density(ppVD)and the peripapillary retinal nerve fiber layer(pRNFL)in children with type 1 diabetes without clinical diabetic retinopathy by optical coherence tomography angiography(OCTA),providing a basis for early retinopathy in children with type 1 diabetes.METHODS:This was a retrospective study.A total of 30 patients(3–14y)with type 1 diabetes without clinical diabetic retinopathy(NDR group)were included.A total of 30 age-matched healthy subjects were included as the normal control group(CON group).The HbA1c level in the last 3mo was measured once in the NDR group.The pRNFL thickness and ppVD were automatically measured,and the mean pRNFL and ppVD were calculated in the nasal,inferior,temporal,and superior quadrants.The changes in ppVD and pRNFL in the two groups were analyzed.RESULTS:Compared with CON group,the nasal and superior ppVDs decreased in the NDR group(all P<0.01).The thickness of the nasal pRNFL decreased significantly(P<0.01),while the inferior,temporal and superior pRNFLs slightly decreased but not significant in the NDR group(all P>0.05).Person and Spearman correlation analysis of ppVD and pRNFL thickness in each quadrant of the NDR group showed a positive correlation between nasal and superior(all P<0.01),while inferior and temporal had no significant correlation(all P>0.05).There was no significant correlation between the HbA1c level and ppVD and pRNFL in any quadrant(all P>0.05).There was no significant correlation between the course of diabetes mellitus and ppVD and pRNFL in any quadrant(all P>0.05).CONCLUSION:ppVD and pRNFL decrease in eyes of children with type 1 diabetes before clinically detectable retinopathy and OCTA is helpful for early monitoring.

Association of autoimmune thyroid disease with type 1 diabetes mellitus and its ultrasonic diagnosis and management

As a common hyperglycemic disease,type 1 diabetes mellitus(T1DM)is a complicated disorder that requires a lifelong insulin supply due to the immunemediated destruction of pancreaticβcells.Although it is an organ-specific autoimmune disorder,T1DM is often associated with multiple other autoimmune disorders.The most prevalent concomitant autoimmune disorder occurring in T1DM is autoimmune thyroid disease(AITD),which mainly exhibits two extremes of phenotypes:hyperthyroidism[Graves'disease(GD)]and hypothyroidism[Hashimoto's thyroiditis,(HT)].However,the presence of comorbid AITD may negatively affect metabolic management in T1DM patients and thereby may increase the risk for potential diabetes-related complications.Thus,routine screening of thyroid function has been recommended when T1DM is diagnosed.Here,first,we summarize current knowledge regarding the etiology and pathogenesis mechanisms of both diseases.Subsequently,an updated review of the association between T1DM and AITD is offered.Finally,we provide a relatively detailed review focusing on the application of thyroid ultrasonography in diagnosing and managing HT and GD,suggesting its critical role in the timely and accurate diagnosis of AITD in T1DM.

Mitigating the Prevalence of Diabetic Retinopathy in the United States: Utilization of the Chronic Care Model as a Public Health Framework

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.