Primary Care

Artificial Intelligence to Diagnose Complications of Diabetes.

Artificial intelligence (AI) is increasingly being used to diagnose complications of diabetes. Artificial intelligence is technology that enables computers and machines to simulate human intelligence and solve complicated problems. In this article, we address current and likely future applications for AI to be applied to diabetes and its complications, including pharmacoadherence to therapy, diagn...

Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique.

This paper addresses the pressing issue of diabetes, which is a widespread condition affecting a huge population worldwide. As cells become less responsive to insulin or fail to produce it adequately, blood sugar levels rise. This has the potential to cause severe health complications including kidney disease, vision impairment and heart conditions. Early diagnosis is paramount in mitigating the r...

A hybrid healthy diet recommender system based on machine learning techniques.

Obesity is a chronic disease correlated with numerous risk factors that not only negatively affect all body functions but also increase the chances of...

Machine learning-based prediction of elevated N terminal pro brain natriuretic peptide among US general population.

AIMS: Natriuretic peptide-based pre-heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screeni...

Explainable Deep Learning Approaches for Risk Screening of Periodontitis.

Several pieces of evidence have been reported regarding the association between periodontitis and systemic diseases. Despite the emphasized significan...

Towards real-time myocardial infarction diagnosis: a convergence of machine learning and ion-exchange membrane technologies leveraging miRNA signatures.

Rapid diagnosis of acute myocardial infarction (AMI) is crucial for optimal patient management. Accurate diagnosis and time of onset of an acute event...

Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri.

This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resoluti...

The application of artificial intelligence in health communication development: A scoping review.

This scoping review explores the integration of Artificial Intelligence (AI) with communication, behavioral, and social theories to enhance health beh...

vScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening.

The enthusiastic adoption of make-on-demand chemical libraries for virtual screening has highlighted the need for methods that deliver improved hit-fi...

An arterial spin labeling-based radiomics signature and machine learning for the prediction and detection of various stages of kidney damage due to diabetes.

OBJECTIVE: The aim of this study was to assess the predictive capabilities of a radiomics signature obtained from arterial spin labeling (ASL) imaging...

Application of magnetic resonance imaging and artificial intelligence algorithms in cancer screening.

In this society with a high incidence of cancer, cancer screening has become an important method to reduce the incidence and mortality of cancer. Trad...

SDC4 protein action and related key genes in nonhealing diabetic foot ulcers based on bioinformatics analysis and machine learning.

Diabetic foot ulcers (DFU) is a complication associated with diabetes characterised by high morbidity, disability, and mortality, involving chronic in...

Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device.

Detecting glucose levels is crucial for diabetes patients as it enables timely and effective management, preventing complications and promoting overal...

Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.

In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a part...

Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study.

The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting breast, lung, prostate, and colorectal cancer in b...

Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study.

BACKGROUND: Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there...

Revolutionising oral organoids with artificial intelligence.

The convergence of organoid technology and artificial intelligence (AI) is poised to revolutionise oral healthcare. Organoids - three-dimensional stru...

Enhancing systematic review efficiency in hand surgery using artificial intelligence (natural language processing) for abstract screening.

The aim of the present study was to train a natural language processing model to recognize key text elements from research abstracts related to hand s...

Entrustment and EPAs for Artificial Intelligence (AI): A Framework to Safeguard the Use of AI in Health Professions Education.

In this article, the authors propose a repurposing of the concept of entrustment to help guide the use of artificial intelligence (AI) in health profe...

Automatic AI tool for opportunistic screening of vertebral compression fractures on chest frontal radiographs: A multicenter study.

Vertebral compression fractures (VCFs) are the most common type of osteoporotic fractures, yet they are often clinically silent and undiagnosed. Chest...