Ocular coherence tomography and diabetic eye disease

Optical Coherence Tomography (OCT) is a high-resolution, cross-sectional imaging technique that allows detailed assessment of retinal thickness and morphologic evaluation of the neurosensory retinal layers. OCT imaging has rapidly been integrated into diagnosis and management of diabetic macular edema (DME) in routine clinical practice and clinical trials. OCT findings correlate well with other evaluation techniques for DME. Although there is a moderate correlation between OCT measured retinal thickness and visual acuity, OCT cannot replace visual acuity because there is a high degree of variability. Recent improvements in OCT technology have led to widespread interest in the use of spectral domain OCT, which is faster and has higher resolution than time domain OCT. Future research in OCT imaging will likely result in improvements in image quality. The ability to combine OCT with other various diagnostic modalities will further improve the evaluation of DME in both clinical practice and trials.     

Ocular Coherence Tomography and Diabetic Eye Disease

Optical Coherence Tomography (OCT) is a high-resolution, cross-sectional imaging technique that allows detailed assessment of retinal thickness and morphologic evaluation of the neurosensory retinal layers. OCT imaging has rapidly been integrated into diagnosis and management of diabetic macular edema (DME) in routine clinical practice and clinical trials. OCT findings correlate well with other evaluation techniques for DME. Although there is a moderate correlation between OCT measured retinal thickness and visual acuity, OCT cannot replace visual acuity because there is a high degree of variability. Recent improvements in OCT technology have led to widespread interest in the use of spectral domain OCT, which is faster and has higher resolution than time domain OCT. Future research in OCT imaging will likely result in improvements in image quality. The ability to combine OCT with other various diagnostic modalities will further improve the evaluation of DME in both clinical practice and trials.     

Scanning laser ophthalmoscopy in the retromode in diabetic macular oedema

Purpose: To determine the validity of scanning laser ophthalmoscopy in the retromode (RM-SLO) versus other imaging modalities in the diagnosis of diabetic macular oedema (DME). Methods: Two hundred and sixty-three eyes were examined. Inclusion criteria were any stage of untreated or treated diabetic retinopathy and four imaging modalities of the macula carried out on the same day: time domain optical coherence tomography (OCT), fundus autofluorescence (FAF), RM-SLO and fluorescein angiography (FA). Two masked retinal specialists independently graded all images. Agreement between RM-SLO and OCT, FA and FAF in evaluating the presence and patterns of DME was evaluated by kappa statistics, sensitivity, specificity, observed proportional agreement, and proportional agreement in positive and negative cases. Results: The agreement in evaluating the presence/absence of DME between RM-SLO and OCT, FA and FAF was good: κ?=?0.73 (confidence interval; CI, 0.64-0.83), κ?=?0.71 (CI, 0.61-0.81) and κ?=?0.73 (CI, 0.63-0.83), respectively. The agreement in evaluating cystoid pattern of DME was almost perfect between RM-SLO and OCT, RM-SLO and FA, κ?>?0.8; and good between RM-SLO and FAF, κ?>?0.7. The agreement in evaluating the presence/absence of subfoveal neuroretinal was almost perfect between RM-SLO and OCT (κ?=?0.83; 95% CI, 0.70-0.96). Subfoveal neuroretinal detachment did not show any specific pattern on FA or FAF. Sensitivity and specificity of RM-SLO in evaluating DME was 97.7% and 71.9% versus OCT, 97.4% and 68.1% versus FA and 96.1% and 73.3% versus FAF. Retinal thickness of 233?μm represented the cut-off value to define DME by RM-SLO. Conclusions: The combined use of non-invasive imaging techniques can improve the diagnostic interpretation of different aspects of DME.     

Uso en la práctica clínica,de un método de cribado automatizado de retinopatía diabética derivable mediante un sistema de inteligencia artificial de diagnóstico

Background and objectiveTo compare the diagnostic performance of an autonomous diagnostic artificial intelligence (AI) system for the diagnosis of derivable diabetic retinopathy (RDR) with manual classification.Materials and methodsPatients with type 1 and type 2 diabetes participated in a diabetic retinopathy (DR) screening program between 2011-2012. 2 images of each eye were collected. Unidentifiable retinal images were obtained, one centered on the disc and one on the fovea. The exams were classified with the autonomous AI system and manually by anonymous ophthalmologists. The results of the AI system and manual classification were compared in terms of sensitivity and specificity for the diagnosis of both (RDR) and diabetic retinopathy with decreased vision (VTDR).Results10,257 retinal inages of 5,630 eyes of 2,680 subjects were included. According to the manual classification, the prevalence of RDR was 4.14% and that of VTDR 2.57%. The AI system recorded 100% (95% CI: 97-100%) sensitivity and 81.82% (95% CI: 80 -83%) specificity for RDR, and 100% (95% CI: 95-100%) of sensitivity and 94.64% (95% CI: 94-95%) of specificity for VTDR.ConclusionsCompared to the manual classification, the autonomous diagnostic AI system registered a high sensitivity (100%) and specificity (82%) in the diagnosis of RDR and macular edema in people with diabetes. Due to its immediate diagnosis, the autonomous diagnostic AI system can increase the accessibility of RDR screening in primary care settings.     

OCT影像人工智能读片与医生读片对识别年龄相关性黄斑变性的一致性分析

目的：探讨在临床进行年龄相关性黄斑变性(ARMD)患者眼底光学相干断层扫描(OCT)图像人工智能(AI)读片的可行性。

方法：收集2019-11/2021-11 上海市静安区市北医院眼科门诊患者1 579眼OCT图像共1 579张,并收集眼科医生及AI的读片结果。通过Kappa值进行无ARMD和有ARMD分类结果的一致性分析。

结果：两名眼科医生之间在无ARMD和有ARMD读片结果的Kappa值为0.934; AI结果与眼科医生在无ARMD和有ARMD读片结果的Kappa值为0.738。AI对ARMD识别的灵敏度为73.08%,特异度为95.07%,曲线下区域面积(AUC)为0.841。

结论：AI在基于OCT图像的ARMD识别上与眼科医生有较高的一致性,适用于基层医院完成对ARMD的早期筛查和早期转诊工作。     

人工智能糖尿病视网膜病变筛查系统与眼科医师诊断结果的一致性分析

目的　探讨人工智能糖尿病视网膜病变（diabetic retinopathy,DR）筛查系统与临床医师诊断的一致性。方法　收集2019年3月至2020年5月在十堰市太和医院眼科就诊的糖尿病患者325例（650眼）的眼科影像学检查资料,包括眼底彩色照相、黄斑区OCT、荧光素眼底血管造影（FFA）,由专家组和人工智能DR筛查系统分别给出诊断,专家组参考眼底彩色照相、黄斑区OCT及FFA检查结果做出诊断,人工智能DR筛查系统仅以眼底彩色照相结果作为诊断依据。以专家组诊断结果为标准,分析人工智能DR筛查系统诊断各级DR的敏感性、特异度、漏诊率和误诊率,对二者诊断结果的一致性行Kappa检验分析。结果　与专家组诊断结果相比,人工智能诊断有无DR的敏感性为94.78%,特异度为92.11%,二者诊断结果一致性分析的Kappa值为0.82（P<0.001)。与专家组诊断结果相比,人工智能诊断有转诊意义的DR的敏感性为93.75%,特异度为96.18%,二者对DR分期诊断的一致性分析的Kappa值为0.85（P<0.001)。结论　人工智能DR筛查系统与专家组诊断结果一致性高,具有较高的准确性和敏感度,可大规模用于DR的初级筛查工作。     

Sensitivity and specificity of time-domain versus spectral-domain optical coherence tomography in diabetic macular edema

Purpose:

The purpose was to evaluate the sensitivity and specificity of measurements of central macular thickness (CMT) in diabetic macular edema using stratus time-domain and cirrus spectral-domain optical coherence tomography (OCT; Carl Zeiss Meditec, Dublin, CA).

Materials and Methods:

A total of 36 eyes from 19 patients with clinically significant diabetic macular edema (DME) were included. All participants underwent automated scanning patterns using cirrus HD-OCT and stratus OCT examinations on the same day. The sensitivity/specificity of retinal thickness measurements was calculated from published normative data. Agreement was calculated using Bland--Altman method. The receiver operating characteristic curves (ROC) and areas under the ROC were plotted.

Results:

The mean difference between the cirrus HD-OCT and stratus OCT in the central foveal zone was 49.89 μm. Bland--Altman analysis confirmed that the retinal thickness measurements had poor agreement in patients with DME. The areas under the ROC for retinal thickness measurements were 0.88 using cirrus HD-OCT and 0.94 with stratus.

Conclusions:

In patients with DME, the cirrus HD-OCT gives a higher reading than stratus OCT with poor agreement between the devices in most regions within the nine subfield zones. The sensitivity and specificity of the stratus OCT was comparable to the cirrus.     

Practical guidance for imaging biomarkers in exudative age-related macular degeneration

We provide an overview of current macular imaging techniques and identify and describe biomarkers that may be of use in the routine management of macular diseases, particularly exudative age-related macular degeneration (AMD). This perspective includes sections on macular imaging techniques including optical coherence tomography (OCT) and OCT angiography (OCTA), classification of exudative AMD, and biomarkers in structural OCT and OCTA.Fluorescein angiography remains a vital tool for assessing the activity of neovascular lesions, while indocyanine green angiography is the preferred option for choroidal vessel imaging in neovascular AMD. OCT provides a non-invasive three-dimensional visualization of retinal architecture in vivo and is useful in the diagnosis of many imaging biomarkers of AMD-related neovascular lesions, including lesion activity. OCTA is a recent advance in OCT technology that allows accurate visualization of retinal and choroidal vascular flow. OCT and OCTA have led to an updated classification of exudative AMD lesions and provide several biomarkers that help to establish a diagnosis and the disease activity status of neovascular lesions.Individualization of therapy guided by OCT and OCTA biomarkers has the potential to further improve visual outcomes in exudative AMD. Moving forwards, integration of technologically-advanced imaging equipment with AI software will help ophthalmologists to provide patients with the best possible care.     

Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research

With the advancement of retinal imaging, hyperreflective foci (HRF) on optical coherence tomography (OCT) images have gained significant attention as potential biological biomarkers for retinal neuroinflammation. However, these biomarkers, represented by HRF, present pose challenges in terms of localization, quantification, and require substantial time and resources. In recent years, the progress and utilization of artificial intelligence (AI) have provided powerful tools for the analysis of biological markers. AI technology enables use machine learning (ML), deep learning (DL) and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments. Based on ophthalmic images, AI has significant implications for early screening, diagnostic grading, treatment efficacy evaluation, treatment recommendations, and prognosis development in common ophthalmic diseases. Moreover, it will help reduce the reliance of the healthcare system on human labor, which has the potential to simplify and expedite clinical trials, enhance the reliability and professionalism of disease management, and improve the prediction of adverse events. This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration (AMD), diabetic macular edema (DME), retinal vein occlusion (RVO) and other retinal diseases and presents prospects for their utilization.     

人工智能在宁夏银川社区糖尿病视网膜病变远程筛查中的应用

目的:评估人工智能(AI)辅助诊断系统在宁夏银川社区糖尿病视网膜病变(DR)筛查中的应用效果。方法:收集2020-07/2021-07就诊于宁夏银川两个社区卫生服务中心的2型糖尿病患者1 358例2 707眼的眼底彩照,采用Eye Wisdom AI眼病辅助筛查和诊断系统自动检测分析出血、微动脉瘤以及视网膜内微血管异常等DR的特征性改变。根据其国际分期的标准对眼底彩照检测结果进行自动分级,由人工分析组进行图像判读后反馈结果,分析AI辅助筛查系统诊断DR的灵敏度、特异度、误诊率及漏诊率,比较AI与人工分析的一致性,对AI筛查系统与人工分析的结果做Kappa一致性检验。结果:与人工分析相比,AI诊断有无DR的灵敏度为91.84%,特异度为99.06%,漏诊率为8.16%,误诊率为0.94%,对于二者诊断结果的一致性分析Kappa值为0.817(P<0.001)。与人工分析相比,AI组检测无DR的灵敏度为99.06%,特异度为91.84%;检测轻度NPDR的灵敏度为85.36%,特异度为98.52%;中度NPDR的灵敏度为81.53%,特异度为98.55%;重度NPDR的灵敏度为70%...     

Variability in photocoagulation treatment of diabetic macular oedema

Purpose: To establish whether differences in the assessment of diabetic macular oedema (DME) with either optical coherence tomography (OCT) or stereoscopic biomicroscopy lead to variability in the photocoagulation treatment of DME. Methods: The differences in the assessment of DME with either OCT or stereoscopic biomicroscopy were analysed by calculating the surface areas and the overlap of retinal thickening. Photocoagulation treatment plans of retinal specialists were compared by evaluating the number and location of planned laser spots. Results: The threshold for and dosage of photocoagulation differ depending upon whether the basis of retinal thickness diagnosis is clinical observation or OCT. The overlap in laser spot location based on the assessment of DME with OCT or biomicroscopy averages 51%. Among retinal specialists, the treatment plans differed in the laser spot count by six‐ to 11‐fold. Conclusion: Diabetic macular oedema photocoagulation treatment threshold and dosage of laser spots differ depending on whether thickness assessments are based on stereoscopic slit‐lamp biomicroscopy or OCT. In addition, retinal specialists differed in the number and placement of planned laser spots even when given identical information concerning DME and treatable lesions. This variability in the photocoagulation treatment of DME could lead to differences in patient outcome and laser study results.     

Retinal thickness in diabetic retinopathy: comparison of optical coherence tomography, the retinal thickness analyzer, and fundus photography

BACKGROUND: Optical coherence tomography (OCT) and the retinal thickness analyzer (RTA) have proved their ability to measure retinal thickness in healthy subjects and diabetics reliably. In the present study, both techniques were compared for the same study population of diabetic patients, and the findings were related to macular edema shown by stereo fundus photography (SFP). METHODS: Maculas of 124 eyes from 69 patients with diabetes mellitus were examined with OCT and the RTA. Measurements of retinal thickness were compared with signs of macular edema shown by SFP. For each eye, nine different sectors were analyzed (a foveal sector, four parafoveal sectors, and four extrafoveal sectors). Thirteen eyes with a normal macula served as controls. Sensitivity and specificity of detecting clinically significant macular edema (CSME) were calculated. RESULTS: Of 111 eyes, 64 showed signs of CSME by SFP. Mean retinal thickness +/- SD of the foveal sector was 249 +/- 104 microm by RTA and 295 +/- 124 microm by OCT measurements. There was a moderate overall correlation between OCT and the RTA (r = 0.66). The correlation was best in the foveal sector (r = 0.82). Overall correlation with SFP was better for OCT (r = 0.77) than for the RTA (r = 0.62). Sensitivity of detecting CSME was consistently higher with OCT, while the RTA showed higher specificity. CONCLUSION: Both measuring techniques yielded similar results when examining eyes of patients with diabetes, although absolute values differed. OCT seems to be more suitable in the clinical screening for macular edema due to its high sensitivity (>90%) with appropriate analysis parameters. The RTA is more prone to erroneous or missing thickness readings particularly under difficult measuring conditions.     

Artificial intelligence applications in different imaging modalities for corneal topography

Interpretation of topographical maps used to detect corneal ectasias requires a high level of expertise. Several artificial intelligence (AI) technologies have attempted to interpret topographic maps. The purpose of this study is to provide a review of AI algorithms in corneal topography from the perspectives of an eye care professional, a biomedical engineer, and a data scientist. A systematic literature review using Web of Science, Pubmed, and Google Scholar was performed from 2010 to 2020 on themes regarding imaging modalities, their parameters, purpose, and conclusions and their samples and performance related to AI in corneal topography. We provide a comprehensive summary of advances in corneal imaging and its applications in AI. Combined metrics from the Dual Scheimpflug and Placido device could be a good starting point to try AI models in corneal imaging systems. The range of area under the receiving operating curve for AI in keratoconus detection and classification was from 0.87 to 1, sensitivity was from 0.89 to 1, and specificity was from 0.82 to 1. A combination of different types of AI applications to corneal ectasia diagnosis is recommended.     

糖尿病黄斑水肿中光相干断层扫描生物学标志物研究进展

糖尿病视网膜病变(DR)是糖尿病患者常见的眼部并发症,而糖尿病黄斑水肿(DME)是DR患者视力丧失的主要原因,因此DME的早期诊治有重要临床意义。光相干断层扫描(OCT)检查可实现视网膜和脉络膜各个层次的高质量成像,已广泛应用于临床,可用于DME的诊断和治疗效果的长期随访。近年来研究发现DME在OCT图像上视网膜和脉络...     

Correlation of retinal sensitivity measured with fundus-related microperimetry to visual acuity and retinal thickness in eyes with diabetic macular edema

AIM: To determine whether significant correlations exist between retinal sensitivity measured by fundus-related microperimetry and the visual acuity and the foveal thickness measured by optical coherence tomography (OCT) in eyes with diabetic macular edema (DME). METHODS: A retrospective chart review of 32 eyes with DME and 17 normal healthy eyes that had undergone fundus-related microperimetry and OCT. The macular sensitivity was measured using the recently introduced fundus-related microperimeter, Micro Perimeter 1. The mean retinal sensitivities within the central 2 degrees and 10 degrees were correlated with the best-corrected visual acuity and OCT-measured foveal retinal thickness. RESULTS: The mean sensitivities in the central 2 degrees and 10 degrees were significantly lower in patients with DME than in normal subjects (P<0.0001). The mean retinal sensitivities in the central 2 degrees and 10 degrees were inversely correlated with visual acuity (r(2)=0.623, P<0.0001; r(2)=0.581, P<0.0001) and foveal thickness (r(2)=0.581, P<0.0001; r(2)=0.551, P<0.0001). CONCLUSIONS: The mean retinal sensitivities measured with fundus-related microperimetry were significantly lower in eyes with DME than in normal eyes. Because a significant correlation of the microperimeter-determined retinal sensitivity to visual acuity and foveal thickness was observed, the retinal sensitivities obtained by fundus-related microperimetry may be another measure that can be used to assess the effects of DME.     

Bibliometric analysis of artificial intelligence and optical coherence tomography images: research hotspots and frontiers

AIM: To explore the latest application of artificial intelligence (AI) in optical coherence tomography (OCT) images, and to analyze the current research status of AI in OCT, and discuss the future research trend. METHODS: On June 1, 2023, a bibliometric analysis of the Web of Science Core Collection was performed in order to explore the utilization of AI in OCT imagery. Key parameters such as papers, countries/regions, citations, databases, organizations, keywords, journal names, and research hotspots were extracted and then visualized employing the VOSviewer and CiteSpace V bibliometric platforms. RESULTS: Fifty-five nations reported studies on AI biotechnology and its application in analyzing OCT images. The United States was the country with the largest number of published papers. Furthermore, 197 institutions worldwide provided published articles, where University of London had more publications than the rest. The reference clusters from the study could be divided into four categories: thickness and eyes, diabetic retinopathy (DR), images and segmentation, and OCT classification. CONCLUSION: The latest hot topics and future directions in this field are identified, and the dynamic evolution of AI-based OCT imaging are outlined. AI-based OCT imaging holds great potential for revolutionizing clinical care.     

Is there a correlation between structural alterations and retinal sensitivity in morphological patterns of diabetic macular edema?

Spectral domain optical coherence tomography (SDOCT) enables enhanced visualization of retinal layers and delineation of structural alterations in diabetic macular edema (DME). Microperimetry (MP) is a new technique that allows fundus-related testing of local retinal sensitivity. Combination of these two techniques would enable a structure-function correlation with insights into pathomechanism of vision loss in DME. To correlate retinal structural derangement with retinal sensitivity alterations in cases with diabetic macular edema, using SDOCT and MP. Prospective study of 34 eyes of 30 patients with DME. All patients underwent comprehensive ophthalmic examination, fluorescein angiography, microperimetry and SDOCT. Four distinct morphological patterns of DME were identified- diffuse retinal thickening (DRT), cystoid macular edema (CME), schitic retinal thickening (SRT) and neourosensory detachment (NSD) of fovea. Some retinal loci presented with a mixture of above patterns There was significant difference in retinal thickness between groups (P<0.001). Focal retinal sensitivity measurement revealed relatively preserved retinal sensitivity in areas with DRT (13.8 dB), moderately reduced sensitivity (7.9 dB) in areas with CME, and gross retinal sensitivity loss in areas with SRT (1.2 dB) and NSD (4.7 dB) (P<0.001). Analysis of regional scotoma depth demonstrated similar pattern. Retinal sensitivity showed better correlation to OCT pattern (r=-0.68, P<0.001) than retinal thickness (r=-0.44, P<0.001). Structure-function correlation allows better understanding of the pathophysiology of visual loss in different morphological types of DME. Classification of macular edema into these categories has implications on the prognosis and predictive value of treatment.     

Comparison of time-domain OCT and fundus photographic assessments of retinal thickening in eyes with diabetic macular edema

PURPOSE: To explore the correlation between optical coherence tomography (OCT) and stereoscopic fundus photographs (FP) for the assessment of retinal thickening (RT) in diabetic macular edema (DME) within a clinical trial. METHODS: OCT, FP, and best corrected visual acuity (VA) measurements were obtained in both eyes of 263 participants in a trial comparing two photocoagulation techniques for DME. Correlation coefficients (r) were calculated comparing RT measured by OCT, RT estimated from FP, and VA. Principal variables were central subfield retinal thickness (CSRT) obtained from the OCT fast macular map and DME severity assessed by a reading center using a seven-step photographic scale combining the area of thickened retina within 1 disc diameter of the foveal center and thickening at the center. RESULTS: Medians (quartiles) for retinal thickness within the center subfield by OCT at baseline increased from 236 (214, 264) microm in the lowest level of the photographic scale to 517 (455, 598) microm in the highest level (r = 0.67). However, CSRT interquartile ranges were broad and overlapping between FP scale levels, and there were many outliers. Correlations between either modality and VA were weaker (r = 0.57 for CSRT, and r = 0.47 for the FP scale). OCT appeared to be more reproducible and more sensitive to change in RT between baseline and 1 year than was FP. CONCLUSIONS: There was a moderate correlation between OCT and FP assessments of RT in patients with DME and slightly less correlation of either measure with VA. OCT and FP provide complementary information but neither is a reliable surrogate for VA.     

A new handheld fundus camera combined with visual artificial intelligence facilitates diabetic retinopathy screening

AIM: To explore the performance in diabetic retinopathy (DR) screening of artificial intelligence (AI) system by evaluating the image quality of a handheld Optomed Aurora fundus camera in comparison to traditional tabletop fundus cameras and the diagnostic accuracy of DR of the two modalities. METHODS: Overall, 630 eyes were included from three centers and screened by a handheld camera (Aurora, Optomed, Oulu, Finland) and a table-top camera. Image quality was graded by three masked and experienced ophthalmologists. The diagnostic accuracy of the handheld camera and AI system was evaluated in assessing DR lesions and referable DR. RESULTS: Under nonmydriasis status, the handheld fundus camera had better image quality in centration, clarity, and visible range (1.47, 1.48, and 1.40) than conventional tabletop cameras (1.30, 1.28, and 1.18; P<0.001). Detection of retinal hemorrhage, hard exudation, and macular edema were comparable between the two modalities, in principle, with the area under the curve of the handheld fundus camera slightly lower. The sensitivity and specificity for the detection of referable DR with the handheld camera were 82.1% (95%CI: 72.1%-92.2%) and 97.4% (95%CI: 95.4%-99.5%), respectively. The performance of AI detection of DR using the Phoebus Algorithm was satisfactory; however, Phoebus showed a high sensitivity (88.2%, 95%CI: 79.4%-97.1%) and low specificity (40.7%, 95%CI: 34.1%-47.2%) when detecting referable DR. CONCLUSION: The handheld Aurora fundus camera combined with autonomous AI system is well-suited in DR screening without mydriasis because of its high sensitivity of DR detection as well as its image quality, but its specificity needs to be improved with better modeling of the data. Use of this new system is safe and effective in the detection of referable DR in real world practice.     

Advances in retinal imaging for diabetic retinopathy and diabetic macular edema

Diabetic retinopathy and diabetic macular edema (DME) are leading causes of blindness throughout the world, and cause significant visual morbidity. Ocular imaging has played a significant role in the management of diabetic eye disease, and the advent of advanced imaging modalities will be of great value as our understanding of diabetic eye diseases increase, and the management options become increasingly varied and complex. Color fundus photography has established roles in screening for diabetic eye disease, early detection of progression, and monitoring of treatment response. Fluorescein angiography (FA) detects areas of capillary nonperfusion, as well as leakage from both microaneurysms and neovascularization. Recent advances in retinal imaging modalities complement traditional fundus photography and provide invaluable new information for clinicians. Ultra-widefield imaging, which can be used to produce both color fundus photographs and FAs, now allows unprecedented views of the posterior pole. The pathologies that are detected in the periphery of the retina have the potential to change the grading of disease severity, and may be of prognostic significance to disease progression. Studies have shown that peripheral ischemia may be related to the presence and severity of DME. Optical coherence tomography (OCT) provides structural detail of the retina, and the quantitative and qualitative features are useful in the monitoring of diabetic eye disease. A relatively recent innovation, OCT angiography, produces images of the fine blood vessels at the macula and optic disc, without the need for contrast agents. This paper will review the roles of each of these imaging modalities for diabetic eye disease.