Diabetes

Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software.

: To describe the development and validation of an artificial intelligence-based, deep learning algorithm (DeepDR) for the detection of diabetic retinopathy (DR) in retinal fundus photographs. : Five hundred fundus images, which had detailed labelling of DR lesions, were transmitted to be analysed, including localization of the optic disk and macular, vessel segmentation, detection of lesions, and...

Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System.

OBJECTIVE: To determine if natural language processing (NLP) improves detection of nonsevere hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of future severe hypoglycemia (SH).

On using electronic health records to improve optimal treatment rules in randomized trials.

Individualized treatment rules (ITRs) tailor medical treatments according to patient-specific characteristics in order to optimize patient outcomes. D...

Inspection of visible components in urine based on deep learning.

PURPOSE: Urinary particles are particularly important parameters in clinical urinalysis, especially for the diagnosis of nephropathy. Therefore, it is...

Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China.

Retinal fundus photography provides a non-invasive approach for identifying early microcirculatory alterations of chronic diseases prior to the onset ...

Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview.

Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, econo...

Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.

BACKGROUND: Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysi...

Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data.

Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis sys...

Variability in Plus Disease Identified Using a Deep Learning-Based Retinopathy of Prematurity Severity Scale.

PURPOSE: Retinopathy of prematurity is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective, which leads to treatmen...

DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images.

Diabetic retinopathy (DR) grading is crucial in determining the adequate treatment and follow up of patient, but the screening process can be tiresome...

Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records.

OBJECTIVE: We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use ...

Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study.

BACKGROUND: Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopa...

Artificial Intelligence: The Future for Diabetes Care.

Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pandemic, can reform the approach to diagnosis and man...

Artificial Intelligence and Digital Tools: Future of Diabetes Care.

Diabetes mellitus has become a global threat, especially in the emerging economies. In the United States, there are about 24 million people with diabe...

Application of deep learning image assessment software VeriSee™ for diabetic retinopathy screening.

PURPOSE: To develop a deep learning image assessment software VeriSee™ and to validate its accuracy in grading the severity of diabetic retinopathy (D...

Microaneurysms detection in color fundus images using machine learning based on directional local contrast.

BACKGROUND: As one of the major complications of diabetes, diabetic retinopathy (DR) is a leading cause of visual impairment and blindness due to dela...

Ensemble Deep Learning for Diabetic Retinopathy Detection Using Optical Coherence Tomography Angiography.

PURPOSE: To evaluate the role of ensemble learning techniques with deep learning in classifying diabetic retinopathy (DR) in optical coherence tomogra...

Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke.

Convolutional Neural Networks (CNNs) have become a prominent method of AI implementation in medical classification tasks. Grading Diabetic Retinopathy...

Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems.

PURPOSE: We sought to assess the performance of deep learning approaches for differentiating nonglaucomatous optic neuropathy with disc pallor (NGON) ...

Limits of trust in medical AI.

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrate...