Algorithms for Diagnosis of Diabetic Retinopathy and Diabetic Macula Edema- A Review

Potential prospects of Chinese medicine application in diabetic retinopathy

Current treatment strategies for diabetic retinopathy (DR), an eye condition that can lead to blindness, have mainly focused on proliferative DR, including vitreous injection, retinal photocoagulation, and vitrectomy. Vitreous injections mainly depend on anti-vascular endothelial growth factor therapy. In this editorial, we comment on the article by Sun et al. We focus specifically on the mechanisms of the protective effect of genipin on the retina. Genipin is a gardenia extract used in traditional Chinese medicine (TCM). In their study, the authors suggest that controlling advanced glycosylation by the intraocular injection of genipin may be a strategy for preventing retinopathy. The innovative use of a Chinese medicine extract injected into the eye to achieve a curative effect has attracted our attention. Although TCM is effective in treating DR, the topical application of DR, especially intraocular injections, is not yet feasible. Herein, we present a brief analysis of effective Chinese medicines for the treatment of DR. The effectiveness of local injections of TCM applied directly into the eyes holds promise as an effective treatment approach for DR.

Diabetic Retinopathy Leading to Blindness- A Review

Diabetic retinopathy (DR) is the most common microvascular complication of diabetes that damages the retina, leading to blindness. People with type 1 diabetes are at greater risk of developing DR than people with type 2 diabetes. Diabetic retinopathy may be divided into two primary categories: Proliferative diabetic retinopathy (PDR) and non-proliferative diabetic retinopathy (NPDR). There are multiple risk factors for the onset and progression of diabetic retinopathy, such as hypertension, obesity, smoking, duration of diabetes, and genetics. Numerous investigations have evaluated the levels of a wide range of inflammatory chemokines within DR patients' serum, vitreous, and aqueous fluids. In diabetic retinopathy, the vitreous fluid exhibited rises in angiogenic factors like platelet-derived growth factor (PDGF) or vascular endothelial growth factor (VEGF) or declines in antiangiogenic factors like pigment epithelium-derived factor (PEDF). For prevention of diabetic retinopathy, more physical activity as well as less sedentary behavior were linked to a reduced likelihood of DR. Supplementing with nutraceuticals containing vitamins (B1, B2, B6, B12, C, D, E, and l-methyl folate) and mineral (zinc) can help decrease or avoid an outbreak of DR. Only laser photocoagulation and Anti-vascular endothelial growth factor (Anti-VEGF) injections are advised as favorable therapies in severe retinopathy. When it comes to treating DR's VEGF levels, inflammation, oxidative stress, apoptosis, and angiogenesis, Traditional Chinese medicine (TCM) has an excellent future.

Artificial intelligence in screening, diagnosis, and classification of diabetic macular edema: A systematic review

We review the application of artificial intelligence (AI) techniques in the screening, diagnosis, and classification of diabetic macular edema (DME) by searching six databases- PubMed, Scopus, Web of Science, Science Direct, IEEE, and ACM- from January 1, 2005 to July 4, 2021. A total of 879 articles were extracted, and by applying inclusion and exclusion criteria, 38 articles were selected for more evaluation. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We provide an overview of the current state of various AI techniques for DME screening, diagnosis, and classification using retinal imaging modalities such as optical coherence tomography (OCT) and color fundus photography (CFP). Based on our findings, deep learning models have an extraordinary capacity to provide an accurate and efficient system for DME screening and diagnosis. Using these in the processing of modalities leads to a significant increase in sensitivity and specificity values. The use of decision support systems and applications based on AI in processing retinal images provided by OCT and CFP increases the sensitivity and specificity in DME screening and detection.

BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images

Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of ALL is strongly associated with the early diagnosis of the disease. Current practice for initial ALL diagnosis is performed through manual evaluation of stained blood smear microscopy images, which is a time-consuming and error-prone process. Deep learning-based human-centric biomedical diagnosis has recently emerged as a powerful tool for assisting physicians in making medical decisions. Therefore, numerous computer-aided diagnostic systems have been developed to autonomously identify ALL in blood images. In this study, a new Bayesian-based optimized convolutional neural network (CNN) is introduced for the detection of ALL in microscopic smear images. To promote classification performance, the architecture of the proposed CNN and its hyperparameters are customized to input data through the Bayesian optimization approach. The Bayesian optimization technique adopts an informed iterative procedure to search the hyperparameter space for the optimal set of network hyperparameters that minimizes an objective error function. The proposed CNN is trained and validated using a hybrid dataset which is formed by integrating two public ALL datasets. Data augmentation has been adopted to further supplement the hybrid image set to boost classification performance. The Bayesian search-derived optimal CNN model recorded an improved performance of image-based ALL classification on test set. The findings of this study reveal the superiority of the proposed Bayesian-optimized CNN over other optimized deep learning ALL classification models.

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.