Latest AI and machine learning research in care of terminally ill / palliative care for healthcare professionals.
BACKGROUND: Multimorbidity has become a major global public health challenge. However, existing research primarily emphasizes the identification of disease patterns at the population level and lacks the capacity to provide predictive insights into individual future pattern membership. Bridging this gap is crucial for personalized prevention and management. OBJECTIVE: This study aims to propose an ...
BACKGROUND: Assessing radiographic bone condition is important for periodontal diagnosis. The accuracy of radiographic interpretation depends highly on a clinician's experience and knowledge. This study aimed to develop a deep learning-based online platform that aids clinicians in diagnosing periodontitis based on periapical radiographs and to evaluate the platform's usability using a validated su...
BACKGROUND: The purpose of this study was to create a risk score for mortality within 3 years of elective aortobifemoral artery bypass for aortoiliac ...
The integration of new technologies such as artificial intelligence (AI) into healthcare has initiated profound changes in clinical practice, reshapin...
BackgroundStatistical analysis plans are critical regulatory documents that define the statistical methodology, objectives, and data-handling procedur...
OBJECTIVES: To develop recommendations to inform development and integration of predictive digital health and artificial intelligence tools in primary...
Automated detection of complex animal behavior remains a challenge in neuroscience. Developments in computer vision have greatly advanced automated be...
PURPOSE: DirectDensity enables tube voltage-independent, density-calibrated computed tomography (CT) images for treatment planning and is therefore in...
The integration of wearable sensors and IoT technology provides new technical means for sports activity monitoring. However, existing solutions still ...
It is anticipated that the next generation of wireless networks will incorporate artificial intelligence (AI) to a significant degree at all network l...
Current preprocessing workflows for untargeted metabolomics using liquid chromatography-high resolution mass spectrometry (LC-HRMS) are time-consuming...
Electromyography (EMG) signals are widely applied in prosthetic control, rehabilitation training, and human-machine interaction. This places stringent...
Accurate prediction of kelp origin is essential for effective quality and safety management. To this end, we integrated the stable isotope ratios and ...
OBJECTIVE: Magnetic Resonance Elastography (MRE) is a non-invasive imaging technique for mapping biomechanical properties of in vivo tissue, including...
INTRODUCTION: Adverse drug events (ADEs) are a leading cause of preventable patient harm in hospitals. Because they are often recorded only in clinica...
Digital bioassays are emerging as a pivotal technology in disease prevention and diagnostics due to their single-molecule level detection capability. ...
CONTEXT: Ovarian stimulation is a key step in medically assisted reproduction (MAR), whereby supraphysiological doses of FSH extend the "FSH window" a...
Accurate prediction of athlete performance is a challenges issue of significance in sports science and analytics and has application in training desig...
Precise spatiotemporal manipulation of particles in complex microfluidic channel networks (MCNs) underlies numerous advanced applications, but remains...
RNA structures are essential for understanding their biological functions and developing RNA-targeted therapeutics. However, accurate RNA structure pr...