Diabetes

Prediction of immunotherapy response in idiopathic membranous nephropathy using deep learning-pathological and clinical factors.

BACKGROUND: Owing to individual heterogeneity, patients with idiopathic membranous nephropathy (IMN) exhibit varying sensitivities to immunotherapy. This study aimed to establish and validate a model incorporating pathological and clinical features using deep learning training to evaluate the response of patients with IMN to immunosuppressive therapy.

Protocol for a prospective, multicenter, parallel-group, open-label randomized controlled trial comparing standard care with Closed lOoP In chiLdren and yOuth with Type 1 diabetes and high-risk glycemic control: the CO-PILOT trial.

PURPOSE: Advanced hybrid closed loop (AHCL) systems have the potential to improve glycemia and reduce burden for people with type 1 diabetes (T1D). Children and youth, who are at particular risk for out-of-target glycemia, may have the most to gain from AHCL. However, no randomized controlled trial (RCT) specifically targeting this age group with very high HbA has previously been attempted. Theref...

Comparing code-free and bespoke deep learning approaches in ophthalmology.

AIM: Code-free deep learning (CFDL) allows clinicians without coding expertise to build high-quality artificial intelligence (AI) models without writi...

An Improved Clinical and Genetics-Based Prediction Model for Diabetic Foot Ulcer Healing.

The goal of this investigation was to use comprehensive prediction modeling tools and available genetic information to try to improve upon the perfor...

Performance of different machine learning algorithms in identifying undiagnosed diabetes based on nonlaboratory parameters and the influence of muscle strength: A cross-sectional study.

AIMS/INTRODUCTION: Machine learning algorithms based on the artificial neural network (ANN), support vector machine, naive Bayesian or logistic regres...

Diagnostic Accuracy of Artificial Intelligence-Based Automated Diabetic Retinopathy Screening in Real-World Settings: A Systematic Review and Meta-Analysis.

PURPOSE: To evaluate the diagnostic accuracy of artificial intelligence (AI)-based automated diabetic retinopathy (DR) screening in real-world setting...

High cell density cultivation of Corynebacterium glutamicum by deep learning-assisted medium design and the subsequent feeding strategy.

To improve the cell productivity of Corynebacterium glutamicum, its initial specific growth rate was improved by medium improvement using deep neural ...

Development of a deep learning model to distinguish the cause of optic disc atrophy using retinal fundus photography.

The differential diagnosis for optic atrophy can be challenging and requires expensive, time-consuming ancillary testing to determine the cause. While...

Appropriateness of Artificial Intelligence Chatbots in Diabetic Foot Ulcer Management.

Type 2 diabetes is a significant global health concern. It often causes diabetic foot ulcers (DFUs), which affect millions of people and increase ampu...

Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective.

Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an ...

Patients Perceptions of Artificial Intelligence in a Deep Learning-Assisted Diabetic Retinopathy Screening Event: A Real-World Assessment.

During an artificial intelligence (AI)-assisted diabetic retinopathy screening event, we performed a survey on patients´ perceptions on AI. Respondent...

A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks.

Accurate deep learning (DL) models to predict type 2 diabetes (T2D) are concerned not only with targeting the discrimination task but also with learni...

Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability.

OBJECTIVE: To construct a risk prediction model for assisted diagnosis of Diabetic Nephropathy (DN) using machine learning algorithms, and to validate...

Diabetic Foot Screening Guidelines and the Role of Artificial Intelligence: Time to Turn the Tide!

Despite medical and technological advancements, foot amputations continue to rise. Thus, the effort of diabetic foot management should be toward preve...

Integrating network pharmacology, molecular docking and simulation approaches with machine learning reveals the multi-target pharmacological mechanism of against diabetic nephropathy.

Diabetic nephropathy (DN) is one of the most feared complications of diabetes and key cause of end-stage renal disease (ESRD). has been widely used t...

Pediatric diabetes prediction using deep learning.

This study proposed a novel technique for early diabetes prediction with high accuracy. Recently, Deep Learning (DL) has been proven to be expeditious...

Machine Learning Method and Hyperspectral Imaging for Precise Determination of Glucose and Silicon Levels.

This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for ac...

Improved Glycemic Control through Robot-Assisted Remote Interview for Outpatients with Type 2 Diabetes: A Pilot Study.

: Our research group developed a robot-assisted diabetes self-management monitoring system to support Certified Diabetes Care and Education Specialist...

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data.

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare provi...

Haemorrhage diagnosis in colour fundus images using a fast-convolutional neural network based on a modified U-Net.

Retinal haemorrhage stands as an early indicator of diabetic retinopathy, necessitating accurate detection for timely diagnosis. Addressing this need,...