Latest AI and machine learning research in primary care for healthcare professionals.
There are no prospective clinical studies evaluating artificial intelligence implementation for glaucoma detection in real-world settings. We developed an automated retinal photography and AI-based screening system and prospectively assessed its accuracy, feasibility, and acceptability in Australian general practice (GP) clinics. Adults aged 50 years or older were recruited during routine GP visit...
This paper presents a novel framework for accurate exercise posture recognition and health indicator prediction based on improved convolutional neural networks. We propose a multi-scale feature fusion architecture incorporating spatiotemporal attention mechanisms to enhance key point detection precision while maintaining computational efficiency. The system achieves superior posture recognition pe...
Postoperative pneumonia, a prevalent complication arising from lower limb fracture surgery, can significantly prolong hospitalization periods and elev...
Cancer-associated fibroblasts promote tumor progression through growth facilitation, invasion, and immune evasion. This study investigated the impact ...
BACKGROUND: Dipeptidyl peptidase-4 (DPP4) is considered a crucial enzyme in type 2 diabetes (T2D) treatment, targeted by inhibitors due to its role in...
BACKGROUND: Intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN) are major complications of maintenance hemodialysis (MHD) that sign...
Traditional methods for measuring body composition in CT scans rely on labor-intensive manual delineation, which is time-consuming and imprecise. This...
BACKGROUND: The integration of machine learning (ML) algorithms enables the detection of diffusion abnormalities-related respiratory changes in indivi...
Heart transplantation (HTx) remains the definitive treatment for patients with end-stage heart disease. Despite the number of HTx performed annually i...
Monitoring biomarkers offers insights for early disease (e.g., cancer, chronic diseases) screening, treatment guidance and response evaluation. To tac...
The use of artificial intelligence (AI) in pediatric and adolescent medicine offers numerous possibilities, particularly in the prevention of chronic ...
The worldwide prevalence of overweight and obesity has increased rapidly in the last decades. This rise has led to a surge in comorbidities such as ty...
BackgroundMild cognitive impairment (MCI) is a risk factor for dementia, and early screening is crucial for patient prognosis.ObjectiveTo construct an...
Non-Hispanic white (White) populations are overrepresented in medical studies. Potential healthcare disparities can happen when machine learning model...
The drylands vesper mouse (Calomys musculinus) is the primary host for Junin mammarenavirus (JUNV), the etiological agent of Argentine hemorrhagic fev...
BACKGROUND: Building machine learning models that are interpretable, explainable, and fair is critical for their trustworthiness in clinical practice....
Machine learning (ML) models for screening carcinogenic chemicals are critical for the sound management of chemicals. Previous models were built on sm...
Randomized controlled trials (RCTs) have demonstrated benefits of marine omega-3 polyunsaturated fatty acids (omega-3 FA) supplementation for the prev...
Drug-induced cardiotoxicity poses a significant risk to human health, and reliable predictive models are needed for safety assessment. In this study, ...
IMPORTANCE: Deep learning predictions of retinal nerve fiber layer (RNFL) thickness derived from optic disc photographs may help to determine risk for...