Primary Care

Machine learning for prediction of chronic kidney disease progression: Validation of the Klinrisk model in the CANVAS Program and CREDENCE trial.

AIM: To validate the Klinrisk machine learning model for prediction of chronic kidney disease (CKD) progression in patients with type 2 diabetes in the pooled CANVAS/CREDENCE trials.

Protocol for evaluating the fitness for purpose of an artificial intelligence product for radiology reporting in the BreastScreen New South Wales breast cancer screening programme.

INTRODUCTION: Radiologist shortages threaten the sustainability of breast cancer screening programmes. Artificial intelligence (AI) products that can interpret mammograms could mitigate this risk. While previous studies have suggested this technology has accuracy comparable to radiologists most have been limited by using 'enriched' datasets and/or not considering the interaction between the algori...

A machine learning analysis of predictors of future hypertension in a young population.

BACKGROUND: Early diagnosis of hypertension (HT) is crucial for preventing end-organ damage. This study aims to identify the risk factors for future H...

Fog-based deep learning framework for real-time pandemic screening in smart cities from multi-site tomographies.

The quick proliferation of pandemic diseases has been imposing many concerns on the international health infrastructure. To combat pandemic diseases i...

Identification of key genes and biological pathways associated with vascular aging in diabetes based on bioinformatics and machine learning.

Vascular aging exacerbates diabetes-associated vascular damage, a major cause of microvascular and macrovascular complications. This study aimed to el...

AI for interpreting screening mammograms: implications for missed cancer in double reading practices and challenging-to-locate lesions.

Although the value of adding AI as a surrogate second reader in various scenarios has been investigated, it is unknown whether implementing an AI tool...

Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats.

INTRODUCTION: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for...

Sequential safe static and dynamic screening rule for accelerating support tensor machine.

Support tensor machine (STM), as a higher-order extension of support vector machine, is adept at effectively addressing tensorial data classification ...

Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?

PURPOSE OF REVIEW: This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, all...

Construction of an antidepressant priority list based on functional, environmental, and health risks using an interpretable mixup-transformer deep learning model.

As emerging pollutants, antidepressants (AD) must be urgently investigated for risk identification and assessment. This study constructed a comprehens...

Telephone follow-up based on artificial intelligence technology among hypertension patients: Reliability study.

Artificial intelligence (AI) telephone is reliable for the follow-up and management of hypertensives. It takes less time and is equivalent to manual f...

A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscl...

Smartphone based wearable sweat glucose sensing device correlated with machine learning for real-time diabetes screening.

BACKGROUND: Diabetes is a significant health threat, with its prevalence and burden increasing worldwide indicating its challenge for global healthcar...

The development of an EU-wide nutrition and physical activity expert knowledge base to support a personalised mobile application across various EU population groups.

A healthy lifestyle comprising regular physical activity and an adequate diet is imperative for the prevention of non-communicable diseases such as hy...

Autonomous screening for laser photocoagulation in fundus images using deep learning.

BACKGROUND: Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Artificial intelligence (AI) with autonomous deep learning ...

Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme.

BACKGROUND/AIMS: National guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening re...

Smart diabetic foot ulcer scoring system.

Current assessment methods for diabetic foot ulcers (DFUs) lack objectivity and consistency, posing a significant risk to diabetes patients, including...

Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning.

Lung cancer screening (LCS) using annual computed tomography (CT) scanning significantly reduces mortality by detecting cancerous lung nodules at an e...

Predictive modelling and identification of key risk factors for stroke using machine learning.

Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk ...

Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery.

BACKGROUND: This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model...