Primary Care

Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.

The escalating demand for chronic disease management has presented substantial challenges to traditional methods. However, the emergence of Internet of Things (IoT) and artificial intelligence (AI) technologies offers a potential resolution by facilitating more precise chronic disease management through data-driven strategies. This review concentrates on the utilization of IoT mobile sensing devic...

Plantar Thermogram Analysis Using Deep Learning for Diabetic Foot Risk Classification.

AIMS: Thermography is a noninvasive method to identify patients at risk of diabetic foot ulcers. In this study, we employed thermography and deep learning to stratify patients with diabetes at risk of developing foot ulcers. METHODS: We prospectively recorded clinical data and plantar thermograms for adult patients with diabetes who underwent diabetic foot screening. A total of 153 thermal images ...

An extensive experimental analysis for heart disease prediction using artificial intelligence techniques.

The heart is an important organ that plays a crucial role in maintaining life. Unfortunately, heart disease is one of the major causes of mortality gl...

Intracranial stenosis prediction using a small set of risk factors in the Tromsø Study.

Intracranial atherosclerotic stenosis (ICAS) refers to a narrowing of intracranial arteries due to plaque buildup on the inside of the vessel walls re...

Machine learning or traditional statistical methods for predictive modelling in perioperative medicine: A narrative review.

Prediction of outcomes in perioperative medicine is key to decision-making and various prediction models have been created to help quantify and commun...

Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.

Chronic wounds, which take over four weeks to heal, are a major global health issue linked to conditions such as diabetes, venous insufficiency, arter...

The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations.

Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges....

Impact of pectoral muscle removal on deep-learning-based breast cancer risk prediction.

State-of-the-art breast cancer risk (BCR) prediction models have been originally trained on mammograms with pectoral muscle (PM) included. This study ...

Predicting diabetes self-management education engagement: machine learning algorithms and models.

INTRODUCTION: Diabetes self-management education (DSME) is endorsed by the American Diabetes Association (ADA) as an essential component of diabetes m...

Leveraging OGTT derived metabolic features to detect Binge-eating disorder in individuals with high weight: a "seek out" machine learning approach.

Binge eating disorder (BED) carries a 6 times higher risk for obesity and accounts for roughly 30% of type 2 diabetes cases. Timely identification of ...

Diabetic peripheral neuropathy detection of type 2 diabetes using machine learning from TCM features: a cross-sectional study.

AIMS: Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus. Early identification of individuals at high risk of D...

Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases.

Retinal images provide a non-invasive and accessible means to directly visualize human blood vessels and nerve fibers. Growing studies have investigat...

Artificial Intelligence for Diabetic Foot Screening Based on Digital Image Analysis: A Systematic Review.

INTRODUCTION: Early detection of diabetic foot complications is essential for effective management and prevention of complications. Artificial intelli...

Identification of potent phytochemicals against Magnaporthe oryzae through machine learning aided-virtual screening and molecular dynamics simulation approach.

Magnaporthe oryzae stands as a notorious fungal pathogen responsible for causing devastating blast disease in cereals, leading to substantial reductio...

Predicting cell properties with AI from 3D imaging flow cytometer data.

Predicting the properties of tissues or organisms from the genomics data is widely accepted by the medical community. Here we ask a question: can we p...

A recursive embedding and clustering technique for unraveling asymptomatic kidney disease using laboratory data and machine learning.

Traditional methods for diagnosing chronic kidney disease (CKD) via laboratory data may not be capable of identifying early kidney disease. Kidney bio...

Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis.

OBJECTIVE: Blood pressure fluctuations during dialysis, including intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN), are common c...

Robot-Assisted Approach to Diabetes Care Consultations: Enhancing Patient Engagement and Identifying Therapeutic Issues.

: Diabetes is a rapidly increasing global health challenge compounded by a critical shortage of diabetes care and education specialists. Robot-assiste...

Artificial intelligence and science of patient input: a perspective from people with multiple sclerosis.

Artificial intelligence (AI) can play a vital role in achieving a shift towards predictive, preventive, and personalized medicine, provided we are gui...

Machine-learning tool for classifying pulmonary hypertension via expert reader-provided CT features: An educational resource for non-dedicated radiologists.

PURPOSE: Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the European Society of Cardiology/European Respiratory...