Non-proliferative diabetic retinopathy symptoms detection and classification using neural network

DiaNet v2 deep learning based method for diabetes diagnosis using retinal images

Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential. Current diagnostic tests like fasting plasma glucose (FPG), oral glucose tolerance tests (OGTT), random plasma glucose (RPG), and hemoglobin A1c (HbA1c) have limitations, leading to misclassifications and discomfort for patients. The aim of this study is to enhance diabetes diagnosis accuracy by developing an improved predictive model using retinal images from the Qatari population, addressing the limitations of current diagnostic methods. This study explores an alternative approach involving retinal images, building upon the DiaNet model, the first deep learning model for diabetes detection based solely on retinal images. The newly proposed DiaNet v2 model is developed using a large dataset from Qatar Biobank (QBB) and Hamad Medical Corporation (HMC) covering wide range of pathologies in the the retinal images. Utilizing the most extensive collection of retinal images from the 5545 participants (2540 diabetic patients and 3005 control), DiaNet v2 is developed for diabetes diagnosis. DiaNet v2 achieves an impressive accuracy of over 92%, 93% sensitivity, and 91% specificity in distinguishing diabetic patients from the control group. Given the high prevalence of diabetes and the limitations of existing diagnostic methods in clinical setup, this study proposes an innovative solution. By leveraging a comprehensive retinal image dataset and applying advanced deep learning techniques, DiaNet v2 demonstrates a remarkable accuracy in diabetes diagnosis. This approach has the potential to revolutionize diabetes detection, providing a more accessible, non-invasive and accurate method for early intervention and treatment planning, particularly in regions with high diabetes rates like MENA.

Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods

The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients. This makes it pertinent to develop ways to automate the detection process of DR. A computer aided diagnosis system has the potential to significantly reduce the burden currently placed on the ophthalmologists. Hence, this review paper is presented with the aim of summarizing, classifying, and analyzing all the recent development on automated DR detection using fundus images from 2015 up to this date. Such work offers an unprecedentedly thorough review of all the recent works on DR, which will potentially increase the understanding of all the recent studies on automated DR detection, particularly on those that deploys machine learning algorithms. Firstly, in this paper, a comprehensive state-of-the-art review of the methods that have been introduced in the detection of DR is presented, with a focus on machine learning models such as convolutional neural networks (CNN) and artificial neural networks (ANN) and various hybrid models. Each AI will then be classified according to its type (e.g. CNN, ANN, SVM), its specific task(s) in performing DR detection. In particular, the models that deploy CNN will be further analyzed and classified according to some important properties of the respective CNN architectures of each model. A total of 150 research articles related to the aforementioned areas that were published in the recent 5 years have been utilized in this review to provide a comprehensive overview of the latest developments in the detection of DR.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-022-10185-6.

Maslinic Acid Protects against Streptozotocin-Induced Diabetic Retinopathy by Activating Nrf2 and Suppressing NF-κB

This study tested the protective effect of maslinic acid (MA) against diabetic retinopathy (DR) in rats with type 1 diabetes mellitus (T1DM) and investigated possible mechanisms of action. DM was introduced by streptozotocin (STZ) (65 mg/kg, i.p.). Control and STZ (T1DM) were divided into 2 subgroups, which received either the vehicle or MA (80 mg/kg). Serum, pancreases, and retinas were collected for further use. MA significantly reduced fasting glucose levels in the control and T1DM rats but enhanced fasting insulin levels and partially increased the size of the islets of Langerhans and the number of β-cells in T1DM rats. In addition, MA significantly improved the retina structure by preventing the reduction in the area between the inner and outer limiting membranes (ILM and OLM, respectively) and increasing the number of cells forming the ganglion cell layer (GCL), inner nuclear layer (INL), and outer nuclear layer (ONL). Associated with these effects, MA significantly reduced the total levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6), as well as the nuclear levels of NF-κB p65, mRNA levels of Bax, and protein levels of cleaved caspase-3 in the retinas of T1DM rats. However, MA significantly lowered levels of reactive oxygen species (ROS) and malondialdehyde (MDA) but significantly increased the nuclear levels of Nrf2, protein levels of Bcl2, and total levels of superoxide dismutase (SOD) and reduced glutathione (GSH) in the retinas of the control and T1DM rats. In conclusion, MA prevents DR by antioxidant potential mediated by the activation of Nrf2.

Long-Term Oral Administration of Salidroside Alleviates Diabetic Retinopathy in db/db Mice

Diabetic retinopathy (DR), a microvascular complication of diabetes mellitus, is the leading cause of vision loss in the working-age population worldwide. Unfortunately, current clinical treatments cannot completely prevent the occurrence and development of DR. Salidroside (Sal) is a medicinal supplement that has antioxidative and cytoprotective properties. This study aimed to investigate the therapeutic effect of Sal on DR. Briefly, Sal treatment was applied to wide-type mice and db/db mice (a widely used diabetic mice) at 25 mg/kg by oral gavage once daily from 8 weeks to 20 weeks. Mice's bodyweight, blood glucose, total cholesterol, triglyceride, high density lipoprotein and low density lipoprotein were recorded and analyzed. Retinal trypsin digestion and evans blue dye assay were used to detect retinal microvessel changes and function. Retinal glutathione and malondialdehyde content measurements were applied to assess retinal oxidative stress. Full-length transcriptome analysis was performed to explore the underlying mechanisms of Sal protection. Our results found that Sal treatment could successfully relieve blood glucose and blood lipid abnormalities, and reduce retinal oxidative stress level in diabetic mice. Also, Sal treatment repaired the abnormal transcriptome caused by diabetes, alleviated the microvascular lesion of the fundus in diabetic mice, and protected retinal normal barrier function. This study enriches the indications of Sal in the treatment of diabetic diseases, providing practical research ideas for the comprehensive preventions and treatments of DR.

Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review

Objectives

Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients’ (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases.

Materials and Methods

We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions.

Results

We identified 35 eligible studies and classified in three groups: psychological disorder (n = 22), eye diseases (n = 6), and others (n = 7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications.

Conclusion

More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

OBJECTIVES

This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to "classical" knowledge-based ones, and to consider the issues raised and their possible solutions.

METHODS

We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies.

RESULTS

We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data.

CONCLUSIONS

Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.