Primary Care

Latest AI and machine learning research in primary care for healthcare professionals.

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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...

Using artificial intelligence tools for data quality evaluation in the context of microplastic human health risk assessments.

Concerns about the negative impacts of microplastics on human health are increasing in society, whil...

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 diab...

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) an...

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 capab...

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 m...

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, preventi...

Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.

BACKGROUND: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at signif...

CT-based detection of clinically significant portal hypertension predicts post-hepatectomy outcomes in hepatocellular carcinoma.

BACKGROUND: While the CT-based method of detecting clinically significant portal hypertension (CSPH)...

PBScreen: A server for the high-throughput screening of placental barrier-permeable contaminants based on multifusion deep learning.

Contaminants capable of crossing the placental barrier (PB) adversely affect female reproduction and...

Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring.

Prolonged use of digital devices and sedentary lifestyles have led to an increase in the prevalence ...

Optimized Machine Learning for the Early Detection of Polycystic Ovary Syndrome in Women.

Polycystic ovary syndrome (PCOS) is a medical condition that impacts millions of women worldwide; ho...

High-Accuracy Intermittent Strabismus Screening via Wearable Eye-Tracking and AI-Enhanced Ocular Feature Analysis.

An effective and highly accurate strabismus screening method is expected to identify potential patie...

Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network.

BACKGROUND: Traditional diagnostic tools for scoliosis screening necessitate a substantial number of...

Improving reliability of movement assessment in Parkinson's disease using computer vision-based automated severity estimation.

BackgroundClinical assessments of motor symptoms rely on observations and subjective judgments again...

Functionally characterizing obesity-susceptibility genes using CRISPR/Cas9, in vivo imaging and deep learning.

Hundreds of loci have been robustly associated with obesity-related traits, but functional character...

Artificial intelligence for opportunistic osteoporosis screening with a Hounsfield Unit in chronic obstructive pulmonary disease patients.

INTRODUCTION: To investigate the accuracy of an artificial intelligence (AI) prototype in determinin...

Deep learning based screening model for hip diseases on plain radiographs.

INTRODUCTION: The interpretation of plain hip radiographs can vary widely among physicians. This stu...

Optimising test intervals for individuals with type 2 diabetes: A machine learning approach.

BACKGROUND: Chronic disease monitoring programs often adopt a one-size-fits-all approach that does n...

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