Diabetes

Near-optimal insulin treatment for diabetes patients: A machine learning approach.

Blood glycemic control is crucial for minimizing severe side effects in diabetes mellitus. Currently, two opposing treatment approaches exist: in formulaic methods, insulin care is calculated by parameter-based computation (i.e., correction factor, insulin-to-carb ratio, and absorption duration), which are fixed by the medical team based on the history of a tested patient blood glucose levels (BGL...

Prediction of response to anti-vascular endothelial growth factor treatment in diabetic macular oedema using an optical coherence tomography-based machine learning method.

PURPOSE: To predict the anti-vascular endothelial growth factor (VEGF) therapeutic response of diabetic macular oedema (DME) patients from optical coherence tomography (OCT) at the initiation stage of treatment using a machine learning-based self-explainable system.

Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists.

Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advance...

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type...

Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa.

Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and h...

Diabetic Retinopathy Screening with Automated Retinal Image Analysis in a Primary Care Setting Improves Adherence to Ophthalmic Care.

PURPOSE: Retinal screening examinations can prevent vision loss resulting from diabetes but are costly and highly underused. We hypothesized that arti...

Concept attribution: Explaining CNN decisions to physicians.

Deep learning explainability is often reached by gradient-based approaches that attribute the network output to perturbations of the input pixels. How...

Use of Machine Learning and Hybrid Closed Loop Insulin Delivery at Diabetes Camps.

Internet of things-inspired healthcare system for urine-based diabetes prediction.

Healthcare industry is the leading domain that has been revolutionized by the incorporation of Internet of Things (IoT) technology resulting in smart ...

Improving blood glucose level predictability using machine learning.

This study was designed to improve blood glucose level predictability and future hypoglycemic and hyperglycemic event alerts through a novel patient-s...

Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology.

With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligenc...

Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study.

BACKGROUND: Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been de...

Artificial Intelligence in Decision Support Systems for Type 1 Diabetes.

Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin del...

An innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms.

Current methods of evaluating the degree of diabetic retinopathy are highly subjective and have no quantitative standard. To objectively evaluate the ...

An artificial intelligence decision support system for the management of type 1 diabetes.

Type 1 diabetes (T1D) is characterized by pancreatic beta cell dysfunction and insulin depletion. Over 40% of people with T1D manage their glucose thr...

Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect.

Despite the rapid development of science and technology in healthcare, diabetes remains an incurable lifelong illness. Diabetes education aiming to im...

Forecasting tuberculosis using diabetes-related google trends data.

Online activity-based data can be used to aid infectious disease forecasting. Our aim was to exploit the converging nature of the tuberculosis (TB) an...

Artificial intelligence and network pharmacology based investigation of pharmacological mechanism and substance basis of Xiaokewan in treating diabetes.

Xiaokewan is a typical Traditional Chinese medicine (TCM) for diabetes and contains various natural chemicals, such as lignans, flavonoids, saponins, ...

Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches.

In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in ...

Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study.

To identify the most important factors that impact brain volume, while accounting for potential collinearity, we used a data-driven machine-learning a...