Diabetic retinopathy algorithms

In the realm of precision medicine, the potential of deep learning is progressively harnessed to facilitate intricate clinical decision-making, especially when navigating multifaceted datasets encompassing Omics, Clinical, image, device, social, and environmental dimensions. This study accentuates the criticality of image data, given its instrumental role in detecting and classifying vision-threatening diabetic retinopathy (VTDR) - a predominant global contributor to vision impairment. The timely identification of VTDR is a linchpin for efficacious interventions and the mitigation of vision loss...

This review addresses the complexities of type 1 diabetes (T1D) and its associated complications, with a particular focus on diabetic retinopathy (DR). This review outlines the progression from non-proliferative to proliferative diabetic retinopathy and diabetic macular edema, highlighting the role of dysglycemia in the pathogenesis of these conditions. A significant portion of this review is devoted to technological advances in diabetes management, particularly the use of hybrid closed-loop systems (HCLSs) and to the potential of open-source HCLSs, which could be easily adapted to different patients' needs using big data analytics and machine learning...

OBJECTIVES: To investigate a comprehensive proteomic profile of the tear fluid in patients with diabetic retinopathy (DR) and further define non-invasive biomarkers. METHODS: A cross-sectional, multicentre study that includes 46 patients with DR, 28 patients with diabetes mellitus (DM), and 30 healthy controls (HC). Tear samples were collected with Schirmer strips. As for the discovery set, data-independent acquisition mass spectrometry was used to characterize the tear proteomic profile...

IMPORTANCE: Machine learning (ML) algorithms have the potential to identify eyes with early diabetic retinopathy (DR) at increased risk for disease progression. OBJECTIVE: To create and validate automated ML models (autoML) for DR progression from ultra-widefield (UWF) retinal images. DESIGN, SETTING AND PARTICIPANTS: Deidentified UWF images with mild or moderate nonproliferative DR (NPDR) with 3 years of longitudinal follow-up retinal imaging or evidence of progression within 3 years were used to develop automated ML models for predicting DR progression in UWF images...

Many individuals with diabetes on multiple daily insulin injections therapy use carbohydrate ratios (CRs) and correction factors (CFs) to determine mealtime and correction insulin boluses. The CRs and CFs vary over time due to physiological changes in individuals' response to insulin. Errors in insulin dosing can lead to life-threatening abnormal glucose levels, increasing the risk of retinopathy, neuropathy, and nephropathy. Here, we present a novel learning algorithm that uses Q-learning to track optimal CRs and uses nearest-neighbors based Q-learning to track optimal CFs...

AIMS: With growing concerns over complications in diabetes sufferers, this study sought to develop an interpretable machine learning model to offer enhanced diagnostic and treatment recommendations. METHODS: We assessed coronary heart disease, diabetic nephropathy, diabetic retinopathy, and fatty liver disease using logistic regression, decision tree, random forest, and CatBoost algorithms. The SHAP algorithm was employed to elucidate the model's predictions, offering a more in-depth understanding of influential features...

Diabetes is a prevalent global concern, with an estimated 12% of the global adult population affected by 2045. Diabetic retinopathy (DR), a sight-threatening complication, has spurred diverse screening approaches worldwide due to advances in DR knowledge, rapid technological developments in retinal imaging and variations in healthcare resources.Many high income countries have fully implemented or are on the verge of completing a national Diabetic Eye Screening Programme (DESP). Although there have been some improvements in DR screening in Africa, Asia, and American countries further progress is needed...

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer's, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation...

Artificial intelligence (AI) was first introduced in 1956, and effectively represents the fourth industrial revolution in human history. Over time, this medium has evolved to be the preferred method of medical imagery interpretation. Today, the implementation of AI in the medical field as a whole, and the ophthalmological field in particular, is diverse and includes diagnose, follow-up and monitoring of the progression of ocular diseases. For example, AI algorithms can identify ectasia, and pre-clinical signs of keratoconus, using images and information computed from various corneal maps...

BACKGROUND: Studies have demonstrated that 50% to 80% of patients do not receive an International Classification of Diseases (ICD) code assigned to their medical encounter or condition. For these patients, their clinical information is mostly recorded as unstructured free-text narrative data in the medical record without standardized coding or extraction of structured data elements. Leumit Health Services (LHS) in collaboration with the Israeli Ministry of Health (MoH) conducted this study using electronic medical records (EMRs) to systematically extract meaningful clinical information about people with diabetes from the unstructured free-text notes...

OBJECTIVE: To examine the therapeutic mechanism of astragaloside IV (AS-IV) in the management of retinal ganglion cell (RGC) injury induced by high glucose (HG), a comprehensive approach involving the integration of network pharmacology and conducting in vitro and in vivo experiments was utilized. METHODS: A rat model of diabetic retinopathy (DR) injury was created by administering streptozotocin through intraperitoneal injection. Additionally, a model of RGC injury induced by HG was established using a glucose concentration of 0...

Intracranial arterial stenosis (ICAS) is characterized by the pathological narrowing or occlusion of the inner lumen of intracranial blood vessels. However, the retina can indirectly react to cerebrovascular disease. Therefore, retinal fundus images (RFI) serve as valuable noninvasive and easily accessible screening tools for early detection and diagnosis of ICAS. This paper introduces an adversarial learning-based domain adaptation algorithm (ALDA) specifically designed for ICAS detection in multi-source datasets...

We report on the prediction performance of artificial intelligence components embedded into a telehealth platform underlying a newly established eye screening service connecting metropolitan-based ophthalmologists to patients in remote indigenous communities in Northern Territory and Queensland. Two AI-based components embedded into the telehealth platform were evaluated on retinal images collected from 328 unique patients: an image quality alert system and a diabetic retinopathy detection system. Compared to ophthalmologists, at an individual image level, the image quality detection algorithm was correct 72% of the time, and 85% accurate at a patient level...

PURPOSE: A number of algorithms have been developed to calculate screening intervals for diabetic retinopathy on the basis of individual risk factors. However, these approaches have not considered proliferative diabetic retinopathy (PDR) and diabetic macular oedema (DME) as separate end points and death as competing risk. METHODS: A multi-state survival model with death as competing risk was used to predict the screening interval for diabetic retinopathy based on information about all 2446 patients from a well-defined population who had started treatment for either PDR or DME during 25 years...

(1) Background: Diabetes mellitus (DM) is a growing challenge, both for patients and physicians, in order to control the impact on health and prevent complications. Millions of patients with diabetes require medical attention, which generates problems regarding the limited time for screening but also addressability difficulties for consultation and management. As a result, screening programs for vision-threatening complications due to DM have to be more efficient in the future in order to cope with such a great healthcare burden...

BACKGROUND AND OBJECTIVE: Choroidal vascularity index (CVI) measures the ratio of blood vessels in the choroid to the total choroidal area. We aimed to compare CVI between young Black and White patients without a history of ocular or systemic disease. PATIENTS AND METHODS: We used a previously validated algorithm for shadow compensation and choroidal vessel binarization to measure CVI across the Early Treatment of Diabetic Retinopathy Study grid. RESULTS: Black patients had a lower CVI ( ß = -0...

Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome...

Identifying diabetic patients at risk of developing foot ulcers, as one of the most significant complications of diabetes, is a crucial healthcare concern. This study aimed to develop an associative classification model (CBA) using the Apriori algorithm to predict diabetic foot ulcers (DFU). This retrospective cohort study included 666 patients with type 2 diabetes referred to Shahid Beheshti Hospital in Iran between April 2020 and August 2022, of which 279 (42%) had DFU. Data on 29 specific baseline features were collected, which were preprocessed by discretizing numerical variables based on medical cutoffs...

In this manuscript micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) with Inception-V3 transfer learning optimized with equilibrium optimization algorithm (MA-RTCNN-Inception V3-EOA) is proposed. The proposed research work contains four phases: (1) pre-processing, (2) segmentation, (3) post-processing, and (4) classification. At first, guided box filtering for contrast enhancement and background exclusion of input image. The proposed MA-RTCNN-Inception V3-EOA based classification framework is implemented in MATLAB using several performances evaluating metrics like precision, sensitivity, f-measure, specificity, accuracy, classification error rate, and Matthews's correlation coefficient and RoC analysis...

SIGNIFICANCE: The assessment of biomarkers in the eye is rapidly gaining traction for the screening, diagnosis, and monitoring of ocular and neurological diseases. Targeted ocular spectroscopy is a technology that enables concurrent imaging of the eye fundus and analysis of high-quality spectra from a targeted region within the imaged area. This provides structural, compositional, and functional information of specific regions of the eye fundus from a non-invasive approach to ocular biomarker detection...