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ABSTRACT Purpose: Diabetes mellitus is a leading cause of impaired vision. The objective of this study was to evaluate the feasibility of use of portable retinograph and remote analysis of images along with a virtual questionnaire for screening for diabetic retinopathy in basic health units in the city of Ribeirão Preto/SP during the Covid-19 pandemic. Methods: Standard Covid-19 protocol was followed during the screening. Blood pressure and capillary blood glucose were measured. Demographic and social data were collected through a standardized online questionnaire via smartphone. After pupillary dilation, fundal images were obtained with portable retinographs by trained ophthalmology residents. Two standardized 45° images were acquired: one posterior segment and another nasal to the optic nerve. Diabetic retinopathy was classified according to the Early Treatment Diabetic Retinopathy Study. Results: A total of 350 patients (64% female; 45% aged 55-70 years; 55% Caucasian) were evaluated. For 40.5% of patients, the campaign was the first opportunity for retinal evaluation; 47.56% had diabetes mellitus for >10 years. On repeat analysis of images stored in a cloud-based repository by retinal specialist, a 7.8% difference was observed in the Early Treatment Diabetic Retinopathy Study diabetic retinopathy classification, compared to the screening findings. Mild diabetic retinopathy was observed in 12.23%, moderate diabetic retinopathy in 6.31%, and proliferative diabetic retinopathy in 2.58% patients. Macular edema was present in 4.58% patients. Diabetic retinopathy was not detected in 72.78% patients. Conclusion: Use of portable retinographs together with telemedicine can provide efficient alternative to traditional methods for screening and diagnosis of diabetic retinopathy.
RESUMO Objetivo: A diabetes mellitus é considerada uma epidemia global e causa de baixa visual em países em desenvolvimento. Este estudo foi realizado com o objetivo de avaliar a viabilidade do retinógrafo portátil e análise remota de imagens associada a questionário virtual para o rastreio de retinopatia diabética em Unidades Básicas de Saúde da cidade de Ribeirão Preto/SP durante a pandemia de Covid-19. Métodos: Trezentos e sessenta pacientes compareceram a campanha. O acolhimento foi realizado na Unidade Básica de Saúde pela equipe de enfermagem, respeitando medidas de prevenção do Covid-19 Os realizou-se aferição da pressão arterial e glicemia capilar seguida de dilatação. Dados demográficos e sociais foram coletados através de questionário on-line padronizado via smartphone e realizou-se a triagem da retinopatia diabética através da obtenção de imagens com retinógrafos portáteis realizados por residentes de oftalmologia previamente treinados, com a aquisição de 2 imagens padronizadas de 45º: uma do segmento posterior e outra nasal ao nervo óptico. Resultados: Trezentos e sessenta pacientes foram atendidos durante a campanha. Dez pacientes (1,02%) foram excluídos devido à opacidade de meios e impossibilidade de obtenção de imagens de fundo de olho. Foram avaliados 350 pacientes, 64% do sexo feminino, 45% entre 55 e 70 anos e 55% brancos. A Campanha foi a primeira avaliação de retina para 40,5% dos pacientes e 47,56% apresentavam diabetes mellitus há mais de 10 anos. Na análise comparativa da classificação da retinopatia diabética segundo Early Treatment Diabetic Retinopathy Study (triagem X Nuvem) observou-se uma diferença de 7,8% nos resultados. Retinopatia diabética leve foi observada em 12,23%, moderada em 6,31%, proliferativa em 2,58%; edema macular presente em 4,58% e ausência de retinopatia diabética em 72,78% dos pacientes. Conclusão: A utilização de retinógrafos portáteis juntamente a telemedicina, para o rastreamento da retinopatia diabética pode ser considerada uma alternativa eficiente para triagem e diagnóstico da retinopatia diabética dentro ou fora do cenário pandêmico, auxiliando na prevenção de perda visual pelo diabetes.
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OBJECTIVE: Clinical guidelines recommend annual eye examinations to detect diabetic retinopathy (DR) in patients with diabetes. However, timely DR detection remains a problem in medically underserved and under-resourced settings in the United States. Machine learning that identifies patients with latent/undiagnosed DR could help to address this problem. MATERIALS AND METHODS: Using electronic health record data from 40 631 unique diabetic patients seen at Los Angeles County Department of Health Services healthcare facilities between January 1, 2015 and December 31, 2017, we compared ten machine learning environments, including five classifier models, for assessing the presence or absence of DR. We also used data from a distinct set of 9300 diabetic patients seen between January 1, 2018 and December 31, 2018 as an external validation set. RESULTS: Following feature subset selection, the classifier with the best AUC on the external validation set was a deep neural network using majority class undersampling, with an AUC of 0.8, the sensitivity of 72.17%, and specificity of 74.2%. DISCUSSION: A deep neural network produced the best AUCs and sensitivity results on the test set and external validation set. Models are intended to be used to screen guideline noncompliant diabetic patients in an urban safety-net setting. CONCLUSION: Machine learning on diabetic patients' routinely collected clinical data could help clinicians in safety-net settings to identify and target unscreened diabetic patients who potentially have undiagnosed DR.
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In recent years, Artificial Intelligence (AI) has proven its relevance for medical decision support. However, the "black-box" nature of successful AI algorithms still holds back their wide-spread deployment. In this paper, we describe an eXplanatory Artificial Intelligence (XAI) that reaches the same level of performance as black-box AI, for the task of classifying Diabetic Retinopathy (DR) severity using Color Fundus Photography (CFP). This algorithm, called ExplAIn, learns to segment and categorize lesions in images; the final image-level classification directly derives from these multivariate lesion segmentations. The novelty of this explanatory framework is that it is trained from end to end, with image supervision only, just like black-box AI algorithms: the concepts of lesions and lesion categories emerge by themselves. For improved lesion localization, foreground/background separation is trained through self-supervision, in such a way that occluding foreground pixels transforms the input image into a healthy-looking image. The advantage of such an architecture is that automatic diagnoses can be explained simply by an image and/or a few sentences. ExplAIn is evaluated at the image level and at the pixel level on various CFP image datasets. We expect this new framework, which jointly offers high classification performance and explainability, to facilitate AI deployment.
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Diabetes Mellitus , Retinopatia Diabética , Algoritmos , Inteligência Artificial , Retinopatia Diabética/diagnóstico por imagem , Humanos , Programas de Rastreamento , FotografaçãoRESUMO
Diabetic retinopathy (DR) is one of the most common causes of visual impairment and blindness in diabetic patients.It is particularly important to set up simpler,safer,non-invasive and highly effective methods for diagnosis as well as monitoring DR.A variety of new fundus imaging techniques show great advantages in early diagnosis,treatment and monitoring of DR in recent years,The main characteristics of wide-field scanning laser imaging system is achieving a large range of retinal image in a single photograph and without mydriasis.It provides several options for color images,FFA and FAF,which satisfy to detect the retina,choroid and vascular structure.Multi spectral fundus imaging system is suitable for DR screening,because it is able to recognize the typical characteristics of DR,such as microaneurysms,hemorrhage and exudation,and is non-invasive and convenient.OCT angiography is a quantitative examination that provides foveal avascular zone area,macular blood flow density,which provides strong evidence for DR diagnosis.The improvement of these new techniques will help us to build up a personalized evaluation system of DR.
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Objective To study the efficiency and accuracy of artificial intelligence (AI) system based on fundus photograph in diabetic retinopathy(DR)screening,and evaluate the clinical application value of AI system. Methods A diagnostic trial was adopted. Total of 13683 color fundus photos were collected in Zhaoqing Gaoyao People's Hospital from March,2017 to November,2018. The AI system for DR (ZOC-DR-V1) was established,based on transfer learning + NASNet algorithm,by training 4465 precisely labeled fundus images (2510 normal,and 1955 with any stage of DR). One thousand confirmed fundus images (300 normal and 700 with any stage of DR),diagnosed by AI ( AI group ) and doctors ( 3 ophthalmologist doctors and 3 endocrinologist doctors ) ( doctor group ) , respectively. Ophthalmologist group and endocrinologist group were both composed of primary,intermediate and senior physicians. The mean reading time of each image and the total time of 1000 images were recorded. The accuracy and efficiency of AI system and doctor groups were compared. The reading process was divided into two stages. The diagnostic coincidence rate and the average reading time of each group between the two parts were calculated and compared. This study protocol was approved by Ethic Committee of Zhongshan Ophthalmic Center, Sun Yat-sen University (No. 2017KYPJ104). Results After training,the diagnostic coincidence rate of AI system (ZOC-DR-V1) in test set was 94. 7％,AUC was 0. 994. In this "man-machine to war",the diagnostic coincidence rate of primary,intermediate and senior endocrinologist was 94. 0％,91. 4％ and 93. 4％;the diagnostic coincidence rate of primary,intermediate and senior ophthalmologist was 92. 7％,94. 4％ and 95. 6％;the diagnostic coincidence rate of AI system was 95. 2％. There was no difference in the diagnostic coincidence rate between AI system and senior ophthalmologist ( P = 0. 749 ) . The mean reading time of each image of primary, intermediate and senior endocrinologists was (4. 63±1. 87),(3. 74±3. 47) and (5. 71±3. 47) seconds,and the total time of 1000 images of primary,intermediate and senior endocrinologists was 1. 29,1. 04 and 1. 58 hours;the mean reading time of each image of primary,intermediate and senior ophthalmologists was ( 7. 25 ± 6. 58 ) , ( 5. 18 ± 5. 01 ) and ( 5. 18 ± 3. 47 ) seconds,and the total time of 1000 images of primary,intermediate and senior endocrinologists was 2. 02,1. 44 and 1. 44 hours;the mean and total time of AI system was (1. 62±0. 67) seconds and 0. 45 hours,and the reading time of AI system was significantly shorter than that of the doctor groups (all at P=0. 000). The diagnostic coincidence rates between previous and posterior part of primary endocrinologist, primary and intermediate ophthalmologist were significantly different (χ2=11. 986,6. 517,10. 896;all at P<0. 05),and the mean reading time in the posterior part was significantly shorter than that in the previous part of intermediate and senior endocrinologist and primary ophthalmologist (t=4. 175,8. 189,5. 160;all at P<0. 01). While the reading time of AI system remained stable throughout the process(χ2=3. 151,P=0. 103;t=0. 038,P=0. 970). Conclusions The ophthalmic AI system based on fundus images has a good diagnostic efficiency,and its diagnostic coincidence rate can compare with senior ophthalmologist,providing a new method and platform for large-scale DR screening.
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Objective To propose a multi-scale convolutional neural network ( CNN) based lesions detection method of fundus image,and evaluate its application in diabetic retinopathy ( DR) assisted diagnosis. Methods A multi-scale CNN based on lesions detection method of fundus image was proposed. Compared with the existing detection methods,the problem of poor robustness based on threshold segmentation and morphological segmentation was overcome. The idea of multi-scale grids detection without relying on manual pixel-by-pixel labeling was adopted in this algorithm,and the detection performance of small lesions was significantly improved. In addition, multiple DR lesions with high accuracy could be detected by the proposed loss function under the condition of weak labels and small data sets. Results At the level of lesions,the sensitivity and specificity of hard exudation lesions detection were 92. 17％ and 97. 17％,respectively. Compared with single-scale method,the sensitivity and accuracy of multi-scale method proposed in this paper increased by 7. 41％ and 5. 02％,respectively,and compared with other algorithm using the same public dataset IDRiD, the specificity of this algorithm increased by 55. 82％. This method could effectively detect the lesions in fundus images,and could give the basic range of the lesions. The average detection time of fundus images with a large number of lesions was 1. 59 seconds. Conclusions The DR lesions in the fundus image can be quickly and reliably identified,the location information of the lesions can be marked,and the influence of subjective factors can be reduced by using this algorithm, and it can be used to assist the clinician to conduct more effectively.
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Objective To develop a remote diabetic retinopathy (DR) screening system and to evaluate the effectiveness of the screening system in community.Methods A cross-sectional study was carried out under the informed consent of subjects in Peking University People's Hospital and Beijing Xicheng District Desheng Community Health Service Center from June 2015 to December 2016.A remote DR screening system was established in Peking University People's Hospital and Beijing Xicheng District Desheng Community Health Service Center during June 2015 to December 2016.Based on non-mydriatic digital eye fundus camera photography and the internet transmission technology,anterior ocular segment and fundus images of 2 473 eyes from 1 355 community subjects with type 2 diabetes mellitus were transmitted from Beijing Xicheng District Desheng Community Health Service Center to the reading center of Peking University People's Hospital,and the results were provided to the subjects after analysis,including visual examination,diagnosis and follow-up rate of the subjects,the agreement between remote screening system and conventional screening method was analyzed and compared.Results The visual acuities of the 2 473 eyes of 1 355 subjects were obtained by trained community physician,and the visual acuity was ≤0.05 in 103 eyes (4.2％),＞0.05-0.3 in 780 eyes (31.5％),＞0.3 in 1 590 eyes (64.3％).A good consistency was found in the diagnosis and grading of DR (Kappa value =0.895) and in diagnosis of macular disorder (Kappa value =0.763)between the remote screening system and conventional screening method.In addition,the diagnosis results of retinal photocoagulation were consistent between the two methods (Kappa value =1.000).The mean duration of the remote screening system for one subject was 10 minutes,which was shorter than 23 minutes of conventional screening method.The follow-up rate of remote screening system was 75.2％.Conclusions There is a high consistency in the DR diagnosis and evaluation between the remote non-mydriatic screening system and conventional screening method.The screening program with follow-up requests has a satisfying follow-up rate,which could meet the demand of DR screening.
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Objective To observe the characteristics of magnetic resonance angiography of(MRA) ophthalmic artery in patients with diabetic retinopathy (DR).Methods A total of 36 eyes of DR patients (DR group) diagnosed by clinical examination were included in the study.Among them,there were 42 eyes in 21 males and 15 eyes in 15 females.The average age was 55.2 years old.The average duration of diabetes was 7.56 years.All eyes were examined by MRA and fluorescein fundus angiography (FFA) in the ophthalmic artery.At the same time,24 eyes of 12 patients were examined by computed tomography angiography (CTA).Twenty-two healthy volunteers with age and gender matching were selected as the control group.Among them,there were 13 males and 9 females.All patients underwent MRA examination of the ophthalmic artery;at the same time,5 eyes of 5 eyes were examined by CTA.MRA and CTA were classified into three grades according to the development condition and shape change of the ophthalmic artery.0 grade:no abnormality;grade Ⅰ:mild lesion;grade Ⅱ:obvious lesion.According to the results of FFA examination,it was divided into no obvious disease stage,background stage,proliferative stage,proliferative stage.The morphological features of the MRA of the ophthalmic artery in the DR group and the control group were compared.The relationship between the MRA grading of the ophthalmic artery and the FFA staging was observed in the DR group.The consistency analysis between the MRA and CTA grades of the ophthalmic artery in the DR group was performed by Kappa test;the relationship between the MRA grade and the FFA staging of the ophthalmic artery was analyzed by Spearson correlation analysis of the same data with two-way ordered attributes.Results In 44 eyes of the control group,the MRA in grade 0 and Ⅰ of the ophthalmic artery were 41 and 3 eyes,respectively;all eyes of the CTA examination of the ophthalmic artery were grade 0.In 72 eyes of the DR group,the MRA in grade 0,Ⅰ,and Ⅱ of the ophthalmic artery were 28,28,and 16 eyes,respectively.Among the 24 eyes examined by CTA,there were 13,6,and 5 eyes in grade 0,Ⅰ,and Ⅱ,respectively.In the DR group,the classification of MRA and CTA of the ophthalmic artery was highly consistent (Kappa value =0.86).There were significant differences in the number of eyes with different grades of MRA in the DR group and the control group (Z=-5.74,P=0.000).In 72 eyes of the DR group,there were 8,12,22,and 30 eyes in no obvious disease stage,background stage,proliferative stage,and proliferative stage,respectively.Correlation analysis showed that there was a significant correlation between MRA grading and FFA staging in the DR group (r=0.405,P=0.000).Conclusions MRA can show the changes of ophthalmic artery morphology and reflect DR staging to a certain extent.It has a high consistency with FFA vascular changes.
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Objective To compare the imaging characteristics and detection of various types of lesions in diabetic retinopathy (DR) with colorful laser scanning fundus imaging (MSLI) and traditional color fundus photography (CFP).Methods Prospective case series observational study.A total of 38 eyes of 38 patients with DR diagnosed by clinical examination were included in the study.Among them,21 were male and 17 were female;the mean age was 62.6± 11.2 years;the average duration of diabetes was 14.3±7.5 years.All the patients were performed CFP,MSLI,frequency domain optical coherence tomography (SD-OCT),fluorescein angiography (FFA) examination.Using the Helielberg Spectralis HRA+OCT MSLI inspection,one scan simultaneously obtained 488 nm blue reflection (BR),515 nm green light reflection (GR),820 nm infrared light reflection (IR),and multicolor image (MC).The detection of traditional CFP and MC on microaneurysm (MA),hard exudation (HEX),cotton plaque (CWS),intraretinal hemorrhage (IRH),intraretinal microvascular abnormality (IRMA),venous bead (VB),venous ring (VL),macular edema (DME),macular anterior membrane (MEM) and laser photocoagulation (LB) were comparatively observed.The results of FFA examination were used as the diagnostic criteria for lesions.SD-OCT was used to determine the location and depth of lesions and the diagnostic reference for DME and MEM.Results The numbers of eyes with MA (x2=10.460),DME (x2=4.006),MEM (x2=4.444) was significantly higher in MC than that of traditional CFP.But the number of eyes with IRH (Z2=0.103),CWS (x2=1.515),HEX (x2=0.227),IRMA (x2=0.051),VB (x2=0.001),VL (x2=0.149),VH (x2=0.693) and LB (x2=0.720) were not statistically significant between two methods (P＞0.05).The imaging quality of MSLI mode is obviously better than that of traditional CFP.Among them,GR imaging shows the best structural changes of superficial retina in MA,CWS,HEX,MEM,etc.IR imaging shows clear depth in deep retina such as LB.DME was green on MC and the weak low-reflection dark area was visible on the IR image,which were consistent with the DME range indicated by the SD-OCT examination.Conclusions Compared with the traditional CFP,the MSLI can clearly show the DR lesion.The number of checkouts is high on MA,DME and MEM by MC image.
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Optical coherence tomography angiography (OCTA) base on OCT with an algorithm that can image a high-resolution picture of retinal circulation. OCTA has allowed quantifying the characteristic lesions of diabetic retinopathy (DR) in early stage, such as fovea avascular zone, retinal vascular density and the counts of retinal microaneurysm. In addition, OCTA can objectively evaluate the progression and prognosis of DR in late stage through imaging involved retinal neovascularization. Understanding OCT angiography features of DR lesions with different course of the disease may provide reference value for the diagnosis and treatment of DR.
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Diabetic retinopathy (DR) is one of the microvascular complications of diabetes mellitus (DM).Like other macrovascular complications of DM,the development and progression of DR is influenced by a variety of systemic and local factors.It is essential to understand the importance of multidisciplinary collaboration.Systemic risk fators such as hyperglycemia,hypertension,dyslipidemia and diabetic nephropathy should be treated before effective DR management can be implemented.Through multidisciplinary collaboration,we can prevent the development of DR,slow the progression of DR,and improve the safety of perioperative care.Thereby enhancing the level of prevention and control of DM complications,including DR.
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Objective To investigate the distribution patterns of diabetic macular edema (DME) based on optical coherence tomography (OCT),and explore its correlation with diabetic retinopathy (DR) stages and systemic factors.Methods A total of 135 patients (242 eyes) with type 2 diabetes were included in this retrospective study.There were 75 males (138 eyes) and 60 females (104 eyes),the ages were from 29 to 83 years,with an average age of (5 8.8± 11.1) years.The general information such as height,weight,smoking history and blood glucose [such as glycosylated hemoglobin (HbA1 c)],blood pressure,blood lipid,24 hours urine protein and other examinations were collected.The diagnosis of DR and DME were made,and the staging of DR and typing of DME were performed based on fundus color imaging and OCT.DR were divided into mild nonproliferative DR (NPDR),moderate NPDR,severe NPDR and proliferative DR (PDR).DME were categorizedinto 4 types including sponge-like retinal swelling (SME),cystoid macular edema (CME),serous retinal detachment (SRD) and posterior hyaloid traction (PHT).The correlation between DME types and DR staging were analyzed by x2 test and Fisher exact test.Multivariate logistic regression analysis was used to analyze the correlation between DME types and systemic factors.Results In 242 DR eyes the proportions of mild,moderate,severe NPDR and PDR were 30.99％,32.64％,23.14％ and 13.23％,respectively.There were 199 eyes (82.23％) with DME.There were statistically significant differences in the proportion of DME in different stages of DR (x2=21.077,P＜0.01).In the 199 eyes with DME,There were 165 eyes (68.18％) of SME,22 eyes (9.09％) of CME,7 eyes (2.89％) of SRD and 5 eyes (2.07％) of PHT.The distribution of DME patterns in different stages of DR was statistically significant (x2=156.273,P＜0.01).Logistic regression analysis showed that the duration of diabetes,HbA1c and macroalbummuria were independent risk factors for DME [odds ratio (OR)=1.090,1.510,4.123;P＜0.05],and were also independent for SME (OR=1.092,1.445,3.942;P＜0.05);HbA1c wasanindependentriskfactorforSRD(OR=2.337,P＜0.05).Canclusions There are differences in the distribution of different DME types in each stage of DR.The duration of diabetes,HbA 1c and macroalbuminuria were independent risk factors for DME and SME,and macroalbuminuria and HbA1c for CME and SRD.
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Objective To observe the morphological changes of macular capillary in type 2 diabetic mellitus (DM) patients without clinical features of diabetic retinopathy (DR) by optical coherence tomography angiography (OCTA). Methods This is a prospective clinical case-control study. Forty-three eyes of 22 patients with DM without clinical features of DR (case group) and 40 control eyes of 20 age-and sex-matched healthy physical examination subjects (control group) were enrolled in this study. All subjects underwent OCTA examination with mode of retinal blood flow imaging, macular 3 mm×3 mm and 6 mm×6 mm area, signal strength>45. Foveal avascular zone (FAZ) area, foveal capillary density, parafovea capillary non-perfusion, and micro-aneurysm in shallow capillary vessel layer were evaluated. Results In case group, the mean FAZ area was (0.397±0.141) mm2 and the mean foveal capillary density was (44.6±0.62)%. In control group, the mean FAZ area was (0.253±0.112) mm2 and the mean foveal capillary density was (48.6±0.58)%. FAZ area of eyes in case group was larger than that in control group (t=1.017, P0.05). The spider web-like FAZ and normal foveolar avascular zone were observed in eyes of control group. The parafovea capillary non-perfusion, abnormal foveolar avascular zone, micro-aneurysm and tortuosity of vessels were observed in eyes of case group. Parafovea capillary non-perfusion (χ2=4.542), micro-aneurysms (χ2=5.183) were seen more often in case group than control group (P<0.05). Conclusion Type 2 DM patients have abnormal retinal vascular microcirculation before DR using OCTA, including larger FAZ area, parafovea capillary non-perfusion, abnormal foveolar avascular zone, micro-aneurysm and tortuosity of vessels.
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Objective To observe the ocular fundus features and consistency of classification of diabetic retinopathy (DR) by ultra-wide-field fluorescein angiography (UWFA) and the simulated early treatment diabetic retinopathy study (ETDRS) 7 standard field (7SF) imaging. Methods This is a retrospective clinical description study. Ninety-six eyes of 55 DR patients were included. The ages ranged from 25 to 73 years, with a mean age of (41.34±15.07) years. UWFA examination (British Optos 200Tx imaging system) using the protocol for obtaining 7SF images as described in the ETDRS, 7 circular regions with a range of 30 degrees are spliced as 7SF templates to determine the observation range. This template was then overlaid on the UWFA image to identify the potential viewable area of 7SF. And the visualized area of the retina, retinal non-perfusion (NP) area, retinal neovascularization (NV) area, and pan-retinal photocoagulation (PRP) area of UWFA and 7SF were quantified by a retinal specialist. Results UWFA imaging and 7SF imaging have a high degree of consistency in judging DR classification (kappa=0.851, P=0.000). The retinal visual area, NP area, NV area and PRP area of the UWFA imaging were 3.16, 3.38, 2.22 and 3.15 times more comparing with the simulated 7SF imaging (t=213.430, 45.013, 22.644, 142.665;P=0.000, 0.000, 0.003, 0.000). The lesions of 8 eyes were found outside the range of simulated 7SF imaging, including peripheral NP in 5 eyes, NV areas in 3 eyes, respectively. Conclusion UWFA imaging and simulated 7SF imaging are consistent to judge DR classification, but UWFA can find more peripheral retinal lesions.
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PURPOSE: We assessed the suitability of a low-cost, handheld, nonmydriatic retinograph, namely the Horus DEC 200, for diabetic retinopathy (DR) diagnosis. Two factors were considered: ease of image acquisition and image quality. METHODS: One operator acquired fundus photographs from 54 patients using the Horus and AFC-330, a more expensive, nonportable retinograph. Satisfaction surveys were filled out by patients. Then, two retinologists subjectively assessed image quality and graded DR severity in one eye of each patient. Objective image quality indices also were computed. RESULTS: During image acquisitions, patients had difficulty locating the fixation target inside the Horus: by default, 53.7% of them had to fixate external points with the contralateral eye, as opposed to none of them using the AFC-330 (P < 0.0001). This issue impacted the duration of image acquisitions. Images obtained by the Horus were of significantly lower quality according to the experts (P = 0.0002 and P = 0.0004) and to the objective criterion (P < 0.0001). As a result, up to 20.4% of eyes were inadequate for interpretation, as opposed to 9.3% using the AFC-330. However, no significant difference was found in terms of DR severity according to both experts (P = 0.557 and P = 0.156). CONCLUSIONS: The Horus can be used to screen DR, but at the cost of longer examination times and higher proportions of patients referred to an ophthalmologist due to inadequate image quality. TRANSLATIONAL RELEVANCE: The Horus is adequate to screen DR, for instance in primary care centers or in mobile imaging units.
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Chinese Guideline of Diabetic Retinopathy was developed by the Chinese Ocular Fundus Society and Chinese Ophthalmological Society.It is the first prevention and intervention guideline document of diabetic retinopathy (DR) in China.Clinical pathways and strategies are clearly identified and described in this document for DR screening,referral,intervention,systematic management and patient education.The new DR stage classification combines the first Chinese DR classification since 1985 and the updated international classification of DR.This guideline is based on Chinese health care system,but also reflects the tradition and innovation,and reaches international practice standard.Learning and practice the guideline will promote the prevention and reduce the occurrence and development of DR in China.
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There are many topics in clinical studies of diabetic retinopathy (DR).The current hot topics include the relationship between DR and systemic diseases,major factors for initiation and progression of DR,early DR screening strategies,DR prevention strategies and how to improve the therapeutic effects of DR.However,due to the complexity of DR pathogenesis,multiple risk factors,long cycle of DR prevention and control,it is difficult to exclude all the confounding factors in the DR clinical research.From the long-term perspective,delaying the occurrence and progression of DR and establishing an efficient and practical prevention and control system is the focus of the future DR research in China.
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OBJECTIVES: Diabetic Retinopathy screening services aim to reduce the risk of sight loss amongst patients with diabetes. The rising incidence of diabetes in England and the operational need to ensure the accuracy and timeliness of screening lists led to a pilot study of electronic extraction of data from primary care. This study aimed to evaluate the effectiveness of updating the single collated list of patients eligible for diabetic eye screening using extracts from electronic patient records in primary care. SETTING AND METHODS: The Gloucestershire Diabetic Eye Screening Programme (GDESP) provides screening for 85 General Practices in the county. Of these, 54 using Egton Medical Information Systems (EMIS) practice management system software agreed to participate in this study. The screening list held in 2009 by the Gloucestershire DESP of 14,209 patients known to have diabetes was audited against a list created with automatic extraction from General Practice records of patients marked with the diabetes Read Code C10. Those subsequently screened and referred to the Hospital Eye service were followed up. RESULTS: The Gloucestershire DESP manual list covering the 54 EMIS practices comprised 14,771 people with diabetes. The audit process identified an additional 709 (4.8%) patients coded C10, including 23 diagnosed more than 5 years ago, and 20 patients under the age of 20 who were diagnosed more than a year ago. CONCLUSION: Automatic extraction of data from General Practice identified 709 patients coded as having diabetes not previously known to the Gloucestershire DESP.
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Retinopatia Diabética/diagnóstico , Retinopatia Diabética/epidemiologia , Retinopatia Diabética/terapia , Registros Eletrônicos de Saúde/estatística & dados numéricos , Inglaterra/epidemiologia , Medicina Geral , Humanos , Incidência , Programas de Rastreamento/estatística & dados numéricos , Encaminhamento e ConsultaRESUMO
Objective To explore the related risk factors for diabetic retinopathy(DR)in type 2diabetes.Methods The clinical data of 412 type 2 diabetes patients,diagnosed between 2003 and 2010,were analyzed retrospectively.The diagnosis of DR and proliferative diabetic retinopathy(PDR)was confirmed by ophthalmoloseopy and fundus fluorescein angiography.Glycated hemoglobin Alc,glucose,insulin,and C-peptide of fasting plasma,and 1,2 and 3 hours postprandial plasma were measured.According to the above-mentioned data,get the fluctuation of glucose,insulin and C-peptide of 1,2 and 3hour postprandial plasma.Results The morbidity of DR and PDR increased following the longer disease duration.Age,diabetic duration,body mass index(BMI),hypertension grade,HbAlC,fasting plasma insulin and C-peptide,2 and 3 hours postprandial plasma glucose,1 and 2 hours postprandial plasma insulin,1,2 and 3 hour postprandial plasma C-peptide,1,2 and 3 hours postprandial plasma glucose,insulin and C-peptide fluctuation are different statistically among non-DR group,non-PDR group and PDR group(P＜0.05).3 hours postprandial plasma glucose and fasting plasma insulin were risk factors of DR (P＜0.05).Conclusions Postprandial plasma glucose and fasting plasma insulin were risk factors of DR.Nevertheless,postprandial insulin,fasting and postprandial C-peptide,postprandial plasma glucose,insulin and C-peptide fluctuation were useful for DR diagnosis.
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The ocular fundus changes and the damage of visual function were various at different stages of diabetic retinopathy (DR). To get hold of timing and different therapic method correctly of early diagnosis, whole body treatment, laser photocoagulation and vitreous-retina surgery and adopting targeted interventions could help patients receiving the most reasonable and effective treatment at different stages,both of them are keys to reduce the damage of visual function.
