Detection Of Diabetic Macular Edema Research Articles

Macular vascular and photoreceptor changes for diabetic macular edema at early stage

This study was intended to investigate the macular vascular and photoreceptor changes for diabetic macular edema (DME) at the early stage. A total of 255 eyes of 134 diabetes mellitus patients were enrolled and underwent an ophthalmological and systemic evaluation in this cross-sectional study. Early DME was characterized by central subfoveal thickness (CST) value between 250 and 325 μm, intact ellipsoid zone, and an external limiting membrane. While non-DME was characterized by CST < 250 μm with normal retinal morphology and structure. Foveal avascular zone (FAZ) area ≤ 0.3 mm2 (P < 0.001, OR = 0.41, 95% CI 0.26–0.67 in the multivariate analysis) and HbA1c level ≤ 8% (P = 0.005, OR = 0.37, 95% CI 0.19–0.74 in multivariate analysis) were significantly associated with a higher risk of early DME. Meanwhile, no significant differences exist in cone parameters between non-DME and early DME eyes. Compared with non-DME eyes, vessel diameter, vessel wall thickness, wall-to-lumen ratio, the cross-sectional area of the vascular wall in the upper side were significantly decreased in the early DME eyes (P = 0.001, P < 0.001, P = 0.005, P = 0.003 respectively). This study suggested a vasospasm or vasoconstriction with limited further photoreceptor impairment at the early stage of DME formation. CST ≥ 250 μm and FAZ ≤ 0.3 mm2 may be the indicator for early DME detection.

Implementation of Artificial Intelligence-Based Diabetic Retinopathy Screening in a Tertiary Care Hospital in Quebec: Prospective Validation Study.

Diabetic retinopathy (DR) affects about 25% of people with diabetes in Canada. Early detection of DR is essential for preventing vision loss. We evaluated the real-world performance of an artificial intelligence (AI) system that analyzes fundus images for DR screening in a Quebec tertiary care center. We prospectively recruited adult patients with diabetes at the Centre hospitalier de l'Université de Montréal (CHUM) in Montreal, Quebec, Canada. Patients underwent dual-pathway screening: first by the Computer Assisted Retinal Analysis (CARA) AI system (index test), then by standard ophthalmological examination (reference standard). We measured the AI system's sensitivity and specificity for detecting referable disease at the patient level, along with its performance for detecting any retinopathy and diabetic macular edema (DME) at the eye level, and potential cost savings. This study included 115 patients. CARA demonstrated a sensitivity of 87.5% (95% CI 71.9-95.0) and specificity of 66.2% (95% CI 54.3-76.3) for detecting referable disease at the patient level. For any retinopathy detection at the eye level, CARA showed 88.2% sensitivity (95% CI 76.6-94.5) and 71.4% specificity (95% CI 63.7-78.1). For DME detection, CARA had 100% sensitivity (95% CI 64.6-100) and 81.9% specificity (95% CI 75.6-86.8). Potential yearly savings from implementing CARA at the CHUM were estimated at CAD $245,635 (US $177,643.23, as of July 26, 2024) considering 5000 patients with diabetes. Our study indicates that integrating a semiautomated AI system for DR screening demonstrates high sensitivity for detecting referable disease in a real-world setting. This system has the potential to improve screening efficiency and reduce costs at the CHUM, but more work is needed to validate it.

Cost-effectiveness of incorporating self-imaging optical coherence tomography into fundus photography-based diabetic retinopathy screening

Diabetic macular edema (DME) has emerged as the foremost cause of vision loss in the population with diabetes. Early detection of DME is paramount, yet the prevailing screening, relying on two-dimensional and labor-intensive fundus photography (FP), results in frequent unwarranted referrals and overlooked diagnoses. Self-imaging optical coherence tomography (SI-OCT), offering fully automated, three-dimensional macular imaging, holds the potential to enhance DR screening. We conducted an observational study within a cohort of 1822 participants with diabetes, who received comprehensive assessments, including visual acuity testing, FP, and SI-OCT examinations. We compared the performance of three screening strategies: the conventional FP-based strategy, a combination strategy of FP and SI-OCT, and a simulated combination strategy of FP and manual SD-OCT. Additionally, we undertook a cost-effectiveness analysis utilizing Markov models to evaluate the costs and benefits of the three strategies for referable DR. We found that the FP + SI-OCT strategy demonstrated superior sensitivity (87.69% vs 61.53%) and specificity (98.29% vs 92.47%) in detecting DME when compared to the FP-based strategy. Importantly, the FP + SI-OCT strategy outperformed the FP-based strategy, with an incremental cost-effectiveness ratio (ICER) of $8016 per quality-adjusted life year (QALY), while the FP + SD-OCT strategy was less cost-effective, with an ICER of $45,754/QALY. Our results were robust to extensive sensitivity analyses, with the FP + SI-OCT strategy standing as the dominant choice in 69.36% of simulations conducted at the current willingness-to-pay threshold. In summary, incorporating SI-OCT into FP-based screening offers substantial enhancements in sensitivity, specificity for detecting DME, and most notably, cost-effectiveness for DR screening.

Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables

ObjectiveDiabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario.MethodsIn this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People’s Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People’s Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction.ResultsThe risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively.ConclusionThe MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.

Clinical Features for Detecting Diabetic Macular Edema using Artificial Intelligence

Background:Vision is a valuable part of life: influencing our perception of the world and of memories. Diabetes, and more specifically, Diabetic Retinopathy (DR) can affect our vision, taking away sight potentially permanently if left untreated. Currently, Diabetic Retinopathy is the leading cause for adult blindness and will continue to rise with increasing prevalence of adult diabetes. Diabetic Macular Edema (DME), a complication of DR, is diagnosed by ophthalmologists using optical coherence tomography (OCT); however, the sheer amount of DME-related imaging creates a time strain on ophthalmologists, creating a demand to further optimize the image reading process. In this study, we hypothesize that increasing the rate and ease of diagnosing DME by introducing artificial intelligence-based methods in primary medical clinics will increase the long-term preservation of ocular health in diabetic patients.&#x0D; Methods:Due to the nature of our retrospective cohort study, consent was not acquired and images were also de-identified. We categorized 676 patient files by HbA1c, non-proliferative diabetic retinopathy (NPDR) severity, and proliferative diabetic retinopathy (PDR). Retinal OCT images were annotated to identify central macular edema, a common feature of DME. Retinal fundus images were also annotated to identify microaneurysms and hemorrhages, two additional features commonly used for detecting either DR or DME.&#x0D; Results:A lesion features dataset was prepared to train our AI model. OCT and fundus imaging features were extracted and combined to train the AI model for DME detection. From annotations of the in-house Macular thickness dataset, it was seen that 167 patients had DME from the total 389 diabetic retinopathy patients.&#x0D; Conclusion:We will continue to prepare more datasets like the macular thickness dataset for our AI. We predict that after our AI receives substantial training with the datasets, the AI will potentially demonstrate some capability of diagnosing DME, supporting its use in medical diagnostics.

Detection and Classification of Diabetic Macular Edema with a Desktop-Based Code-Free Machine Learning Tool.

To evaluate the effectiveness of the Lobe application, a machine learning (ML) tool that can be used on a personal computer without requiring coding expertise, in the recognition and classification of diabetic macular edema (DME) in spectral-domain optical coherence tomography (SD-OCT) scans. A total of 695 cross-sectional SD-OCT images from 336 patients with DME and 200 OCT images of 200 healthy controls were included. Images with DME were classified into three main types: diffuse retinal edema (DRE), cystoid macular edema (CME), and cystoid macular degeneration (CMD). To develop the ML model, we used the desktop-based code-free Lobe application, which includes a pre-trained ResNet-50 V2 convolutional neural network and is available free of charge. The performance of the trained model in recognizing and classifying DME was evaluated with 41 DRE, 28 CMD, 70 CME, and 40 normal SD-OCT images that were not used in the training. The developed model showed 99.28% sensitivity and 100% specificity for class-independent detection of DME. Sensitivity and specificity by labels were 87.80% and 98.57% for DRE, 96.43% and 99.29% for CME, and 95.71% and 95.41% for CMD, respectively. To our knowledge, this is the first evaluation of the effectiveness of Lobe with ophthalmological images, and the results indicate that it can be used with high efficiency in the recognition and classification of DME from SD-OCT images by ophthalmologists without coding expertise.

Comparative Analysis of Pre-trained ResNet and DenseNet Models for the Detection of Diabetic Macular Edema

A major setback in Deep Learning (DL) is that a huge amount of data is essential to render the trained model more generalizable. Constructing a higher-order model based on insufficient data has a detrimental effect on testing performance. Transfer Learning (TL) involves less training data than conventional DL approaches and offers superior decision support. Healthcare datasets of reasonable sizes are generally inappropriate for training DL models. A promising solution to the issue would be to use TL methods for the classification of medical image datasets. This paper aims at the training and evaluation of six variants of pre-trained ResNet and three variants of pre-trained DenseNet models for Diabetic Macular Edema (DME) classification employing a public retinal Optical Coherence Tomography (OCT) image dataset. Among the ResNet implementations, ResNet101V2 has delivered the highest accuracy value of 95%. And among the DenseNet implementations, DenseNet201 has yielded an exceptional classification accuracy of 99%. As all three DenseNet versions, along with the ResNet101V2 version, have produced noticeably good results (accuracy values greater than 95%), they can be used to screen the retinal OCT images of DME patients.

DME-DeepLabV3+: a lightweight model for diabetic macular edema extraction based on DeepLabV3+ architecture.

Diabetic macular edema (DME) is a major cause of vision impairment in the patients with diabetes. Optical Coherence Tomography (OCT) is an important ophthalmic imaging method, which can enable early detection of DME. However, it is difficult to achieve high-efficiency and high-precision extraction of DME in OCT images because the sources of OCT images are diverse and the quality of OCT images is not stable. Thus, it is still required to design a model to improve the accuracy of DME extraction in OCT images. A lightweight model (DME-DeepLabV3+) was proposed for DME extraction using a DeepLabV3+ architecture. In this model, MobileNetV2 model was used as the backbone for extracting low-level features of DME. The improved ASPP with sawtooth wave-like dilation rate was used for extracting high-level features of DME. Then, the decoder was used to fuse and refine low-level and high-level features of DME. Finally, 1711 OCT images were collected from the Kermany dataset and the Affiliated Eye Hospital. 1369, 171, and 171 OCT images were randomly selected for training, validation, and testing, respectively. In ablation experiment, the proposed DME-DeepLabV3+ model was compared against DeepLabV3+ model with different setting to evaluate the effects of MobileNetV2 and improved ASPP on DME extraction. DME-DeepLabV3+ had better extraction performance, especially in small-scale macular edema regions. The extraction results of DME-DeepLabV3+ were close to ground truth. In comparative experiment, the proposed DME-DeepLabV3+ model was compared against other models, including FCN, UNet, PSPNet, ICNet, and DANet, to evaluate DME extraction performance. DME-DeepLabV3+ model had better DME extraction performance than other models as shown by greater pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1), and mean Intersection over Union (MIoU), which were 98.71%, 95.23%, 91.19%, 91.12%, 91.15%, and 91.18%, respectively. DME-DeepLabV3+ model is suitable for DME extraction in OCT images and can assist the ophthalmologists in the management of ocular diseases.

Generating OCT B-Scan DME images using optimized Generative Adversarial Networks (GANs)

Diabetic Macular Edema (DME) represents a significant visual impairment among individuals with diabetes, leading to a dramatic reduction in visual acuity and potentially resulting in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that produces high-resolution retinal images, plays a vital role in the clinical assessment of this condition. Physicians typically rely on OCT B-Scan images to evaluate DME severity. However, manual interpretation of these images is susceptible to errors, which can lead to detrimental consequences, such as misdiagnosis and improper treatment strategies. Hence, there is a critical need for more reliable diagnostic methods. This study aims to address this gap by proposing an automated model based on Generative Adversarial Networks (GANs) to generate OCT B-Scan images of DME. The model synthesizes images from patients' baseline OCT B-Scan images, which could potentially enhance the robustness of DME detection systems. We employ five distinct GANs in this study: Deep Convolutional GAN, Conditional GAN, CycleGAN, StyleGAN2, and StyleGAN3, drawing comparisons across their performance. Subsequently, the hyperparameters of the best-performing GAN are fine-tuned using Particle Swarm Optimization (PSO) to produce more realistic OCT images. This comparative analysis not only serves to improve the detection of DME severity using OCT images but also provides insights into the appropriate choice of GANs for the effective generation of realistic OCT images from the baseline OCT datasets.

Convolution neural network model for predicting various lesion-based diseases in diabetic macula edema in optical coherence tomography images

Diabetic Macular Edema (DME), a rare eye disease primarily found in diabetic patients, is due to the formation of fluid in the extra-cellular space of the macular area in the retina. In the earlier days, it was detected through fundus images that provided less accuracy and were difficult for early detection. Optical Coherence Tomography (OCT) is widely adopted to overcome such issues. It is an advanced imaging modality that provides a better view of the retinal structure. However, the medical professionals manually carried out the detection of DME from the OCT images. Advancement in machine learning algorithms has enabled easy processing of OCT images for DME detection. However, the machine learning algorithm provided less accuracy as it was limited to 2-dimensional datasets and their parameters. The lesions in the images were detected using the 3-dimensional structure of the OCT images. The deep learning algorithms consisted of various layers that increased the algorithm's efficiency and provided the necessary features and parameters that helped in the earlier detection of DME. It is also important to detect the types of DME like hemorrhages, Microaneurysms, and exudate. In this paper, a novel lesion-based CNN algorithm is proposed for the efficient detection of the lesions that help in better prediction. The proposed model is compared with other deep learning models, and the results show that the LCNN model provides improved accuracy than normal deep learning models like ResNet, VGG16, and Inception. The accuracy of deep learning models like AlexNet and Inception is increased to 96%.

Deep learning–based detection of diabetic macular edema using optical coherence tomography and fundus images

Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The performances of these algorithms vary and often create doubt regarding their clinical utility. In resource-constrained health-care systems, these algorithms may play an important role in determining referral and treatment. The survey provides a diversified overview of macular edema detection methods, including cutting-edge research, with the objective of providing pertinent information to research groups, health-care professionals, and diabetic patients about the applications of deep learning in retinal image detection and classification process. Electronic databases such as PubMed, IEEE Explore, BioMed, and Google Scholar were searched from inception to March 31, 2022, and the reference lists of published papers were also searched. The study followed the preferred reporting items for systematic review and meta-analysis (PRISMA) reporting guidelines. Examination of various deep learning models and their exhibition regarding precision, epochs, their capacity to detect anomalies for less training data, concepts, and challenges that go deep into the applications were analyzed. A total of 53 studies were included that evaluated the performance of deep learning models in a total of 1,414,169°CT volumes, B-scans, patients, and 472,328 fundus images. The overall area under the receiver operating characteristic curve (AUROC) was 0.9727. The overall sensitivity for detecting DME using OCT images was 96% (95% confidence interval [CI]: 0.94–0.98). The overall sensitivity for detecting DME using fundus images was 94% (95% CI: 0.90–0.96).

Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks

Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person's growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate.

An intelligent approach for detection and grading of diabetic retinopathy and diabetic macular edema using retinal images

ABSTRACT Early detection of diabetic retinopathy (DR) and diabetic macular edema (DME) is difficult due to the presence of abnormalities like micro-aneurysms in the fundus images which causes vision loss for diabetic patients. In this paper, we detect DR and DME in an earlier stage by performing preprocessing to enhance the quality of the input image, which includes three steps: noise filtering, artefact removal and contrast enhancement. Second, blood vessel segmentation is performed, using Improved Mask-Regional Convolutional Neural Networks (Mask RCNN), which increases the accuracy and precision rate of DR and DME detection. Finally, feature extraction and classification using VGG-16 is performed, which extracts structural features, colour and orientation features. Based on the extracted features, the proposed VGG-16 classifies the image into three classes: normal, DR and DME. After detecting DR and DME, we calculate the severity level of the disease using conditional entropy which classifies the severity into mild, moderate and severe. The proposed work is evaluated by two different datasets: IDRiD and MESSIDOR. The performance of the DR and DME detection is evaluated in terms of accuracy (80.7%), sensitivity (93.67%), specificity (93.67%), F1 score (94.61%), ROC curve and AUC curve compared to existing works.

DIABETIC MACULAR EDEMA CLASSIFICATION WITH OCT IMAGES USING GENERATIVE ADVERSARIAL NETWORK AND ACTIVE CONTOUR MODEL

The major reason for blindness is diabetic macular edema (DME) and hence detection of DME at early stage using optical coherence tomography (OCT) is commonly employed for diagnosing retinal diseases. An accurate disease identification and classification poses a challenging task due to the difficulty in differentiating the abnormal and healthy regions. To overcome these issues and to accurately classify the DME, an effective DME classification approach named antlion spider monkey optimization-based generative adversarial network (ALSMO-based GAN) is proposed in this research for segmenting the retinal layers and to classify the DME more accurately. With the generator and the discriminator components of GAN, the DME is effectively classified so that the devised ALSMO algorithm can be used to train the process of GAN. The inspiration of the foraging and the hunting behavior enable the optimization to increase the rate of convergence and to achieve global optimal solution by reducing the local optima. With the segmented retinal layer, the classification process is progressed through the extraction of relevant features from the retinal layers. The performance of the developed method is verified using measures like accuracy, sensitivity, and specificity which attained values of 92.5%, 98%, and 92.3%, respectively.

Deep learning algorithms for detection of diabetic macular edema in OCT images: A systematic review and meta-analysis.

Artificial intelligence (AI) can detect diabetic macular edema (DME) from optical coherence tomography (OCT) images. We aimed to evaluate the performance of deep learning neural networks in DME detection. Embase, Pubmed, the Cochrane Library, and IEEE Xplore were searched up to August 14, 2021. We included studies using deep learning algorithms to detect DME from OCT images. Two reviewers extracted the data independently, and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the risk of bias. The study is reported according to Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA). Ninteen studies involving 41005 subjects were included. The pooled sensitivity and specificity were 96.0% (95% confidence interval (CI): 93.9% to 97.3%) and 99.3% (95% CI: 98.2% to 99.7%), respectively. Subgroup analyses found that data set selection, sample size of training set and the choice of OCT devices contributed to the heterogeneity (all P < 0.05). While there was no association between the diagnostic accuracy and transfer learning adoption or image management (all P > 0.05). Deep learning methods, particularly the convolutional neural networks (CNNs) could effectively detect clinically significant DME, which can provide referral suggestions to the patients.

Detection of DME by Classification and Segmentation Using OCT Images

Optical Coherence Tomography (OCT) is a developing medical scanning technique proposing non- protruding scanning with high resolution for biological tissues. It is extensively employed in optics to accomplish investigative scanning of the eye, especially the retinal layers. Various medical research works are conducted to evaluate the usage of Optical Coherence Tomography to detect diseases like DME. The current study provides an innovative, completely automated algorithm for disease detection such as DME through OCT scanning. We performed the classification and segmentation for the detection of DME. The algorithm used employed HOG descriptors as feature vectors for SVM based classifier. Cross-validation was performed on the SD-OCT data sets comprised of volumetric images obtained from 20 people. Out of 10 were normal, while 10 were patients of diabetic macular edema (DME). Our classifier effectively detected 100% of cases of DME while about 70% cases of healthy individuals. The development of such a notable technique is extremely important for detecting retinal diseases such as DME.

Diabetic Macular Edema Screened by Handheld Smartphone-based Retinal Camera and Artificial Intelligence.

Our aim was to assess the tomographic presence of diabetic macular edema in type 2 diabetes patients screened for diabetic retinopathy with color fundus photographs and an artificial intelligence algorithm. Color fundus photographs obtained with a low-cost smartphone-based handheld retinal camera were analyzed by the algorithm; patients with suspected macular lesions underwent ocular coherence tomography. A total of 366 patients were screened; diabetic macular edema was suspected in 34 and confirmed in 29 individuals, with average age 60.5 ± 10.9 years and glycated hemoglobin 9.8 ± 2.4%; use of insulin, statins, and aspirin were reported in 44.8%, 37.9%, and 34.5% of individuals, respectively; systemic blood hypertension, dyslipidemia, abdominal obesity, chronic kidney disease, and risk for diabetic foot ulcers were present in 100%, 58.6%, 62.1%, 48.3%, and 27.5% of individuals, respectively. Proliferative diabetic retinopathy was present in 31% of patients with macular edema; severity level was associated with albuminuria (p = 0.028). Eyes with macular edema had average central macular thickness 329.89 ± 80.98 mmu; intraretinal cysts, sub retinal fluid, hyper-reflective foci, epiretinal membrane, and vitreomacular traction were found in 87.2%, 6.4%, 85.1%, 10.6%, and 6.4% of eyes, respectively. Diabetic retinopathy screening overwhelms health systems and is typically based on color fundus photographs, with high false-positive rates for the detection of diabetic macular edema. The present, semi-automated strategy comprising artificial intelligence algorithms integrated with smartphone-based retinal cameras could improve screening in low-resource settings with limited availability of ocular coherence tomography, allowing increased access rates and ultimately contributing to tackle preventable blindness.

Selfie fundus imaging for diabetic retinopathy screening.

BackgroundRegular screening for retinopathy and timely intervention reduces blindness from diabetes by 90%. Screening is currently dependent on the interpretation of images captured by trained technicians. Inherent barriers of accessibility and affordability with this approach impede widespread success of retinopathy screening programs. Herein, we report our observations on the potential of a novel approach, Selfie Fundus Imaging (SFI), to enhance diabetic retinopathy screening.MethodsThe study was undertaken over a two-month period during COVID 19 lockdown. 60 diabetic patients participated in the study. Retinal images were captured using three different approaches, handheld smartphone-based photographs captured by patients themselves after a short video-assisted training session (SFI group), and smartphone-based photographs captured by a trained technician and photographs taken on desktop conventional digital fundus camera (Gold standard). Sensitivity and kappa statistics was determined for retinopathy and macular oedema grading.FindingsMean age of the study participants was 52.4 years ± 9.8 years and 78% were men. Of 120 images captured using SFI, 90% were centred-gradable, 8% were decentred-gradable and 2% were ungradable. 82% patients captured the image within a minute (majority by 31–45 s). The sensitivity of SFI to detect diabetic retinopathy was 88.39%. Agreement between SFI grading and standard fundus photograph grading was 85.86% with substantial kappa (0.77). For the detection of diabetic macular oedema, the agreement between SFI images and standard images was 93.67, with almost perfect kappa (0.91).ConclusionFundus images were captured by patients using SFI without major difficulty and were comparable to images taken by trained specialist. With greater penetrance, advances, and availability of mobile photographic technology, we believe that SFI would positively impact the success of diabetic retinopathy screening programs by breaking the barriers of availability, accessibility, and affordability. SFI could ensure continuation of screening schedules for diabetic retinopathy, even in the face a highly contagious pandemic.

DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis

Diabetic macular edema (DME) is the most common cause of irreversible vision loss in diabetes patients. Early diagnosis of DME is necessary for effective treatment of the disease. Visual detection of DME in retinal screening images by ophthalmologists is a time-consuming process. Recently, many computer-aided diagnosis systems have been developed to assist doctors by detecting DME automatically. In this paper, a new deep feature transfer-based stacked autoencoder neural network system is proposed for the automatic diagnosis of DME in fundus images. The proposed system integrates the power of pretrained convolutional neural networks as automatic feature extractors with the power of stacked autoencoders in feature selection and classification. Moreover, the system enables extracting a large set of features from a small input dataset using four standard pretrained deep networks: ResNet-50, SqueezeNet, Inception-v3, and GoogLeNet. The most informative features are then selected by a stacked autoencoder neural network. The stacked network is trained in a semi-supervised manner and is used for the classification of DME. It is found that the introduced system achieves a maximum classification accuracy of 96.8%, sensitivity of 97.5%, and specificity of 95.5%. The proposed system shows a superior performance over the original pretrained network classifiers and state-of-the-art findings.

Deep Learning Algorithms for Detection of Diabetic Macular Edema in OCT Images: A Systematic Review and Meta-Analysis

Purpose: Artificial intelligence (AI) can detect diabetic macular edema (DME) from optical coherence tomography (OCT) images. We aimed to evaluate the performance of deep learning neural networks in DME detection. Methods: Embase, Pubmed, the Cochrane Library, and IEEE Xplore were searched up to November 14, 2020. We included studies used deep learning algorithms to detect DME from OCT images. Two reviewers extracted the data independently, and QUDAS-2 tool was applied to assess the risk of bias. Results: Eighteen studies involving 41005 subjects were included. The pooled sensitivity and specificity was 96.0% (95% confidence interval (CI): 93.9% to 97.3%) and 99.3% (95% CI: 98.2% to 99.7%), respectively. The pooled diagnostic odds (DOR) was 3491.0 (95% CI: 1117.2 to 10908.7). The positive likelihood ratio (PLR) and negative likelihood (NLR) was 142.2 (95% CI: 54.2 to 373.5) and 0.04 (95% CI: 0.03 to 0.06), respectively. Subgroup analyses found that data set selection, sample size of training set and the choice of OCT devices contributed to the heterogeneity(all P 0.05). Conclusions: Deep learning methods could effectively detect clinically significant DME. Funding Statement: This study was supported by The Capital Health Research and Development of Special (2020-1-2052); Science & Technology Project of Beijing Municipal Science & Technology Commission (Z201100005520045, Z181100001818003); the Beijing Municipal Administration of Hospitals’ Ascent Plan (DFL20150201). Declaration of Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.