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Knowledge domain and frontier trends of artificial intelligence applied in solid organ transplantation: A visualization analysis.

BACKGROUND: Solid organ transplantation (SOT) is vital for end-stage organ failure but faces challenges like organ shortage and rejection. Artificial intelligence (AI) offers potential to improve outcomes through better matching, success prediction, and automation. However, the evolution of AI in SOT research remains underexplored. This study uses bibliometric analysis to identify trends, hotspots...

A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa.

BACKGROUND: The demand for quality healthcare is rising worldwide, and nurses in South Africa are under pressure to provide care with limited resources. This demanding work environment leads to burnout and exhaustion among nurses. Understanding the specific factors leading to these issues is critical for adequately supporting nurses and informing policymakers. Currently, little is known about the ...

Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation.

Unsupervised domain adaptation (UDA) has shown impressive performance by improving the generalizability of the model to tackle the domain shift proble...

Artificial intelligence education in medical imaging: A scoping review.

BACKGROUND: The rise of Artificial intelligence (AI) is reshaping healthcare, particularly in medical imaging. In this emerging field, clinical imagin...

Development and validation of a machine learning-based predictive model for compassion fatigue in Chinese nursing interns: a cross-sectional study utilizing latent profile analysis.

BACKGROUND: Compassion fatigue is a significant issue in nursing, affecting both registered nurses and nursing students, potentially leading to burnou...

Explicitly diverse visual question generation.

Visual question generation involves the generation of meaningful questions about an image. Although we have made significant progress in automatically...

Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis.

BACKGROUND AND AIM: The potential intricacy of skull fractures as well as the complexity of underlying anatomy poses diagnostic hurdles for radiologis...

Utilizing InVEST ecosystem services model combined with deep learning and fallback bargaining for effective sediment retention in Northern Iran.

This study aimed to integrate game theory and deep learning algorithms with the InVEST Ecosystem Services Model (IESM) for Sediment Retention (SR) mod...

Exploiting passive behaviours for diverse musical playing using the parametric hand.

Creativity and style in music playing originates from constraints and imperfect interactions between instruments and players. Digital and robotic syst...

Accelerating the pace of innovation in robotics by fostering diversity and inclusive leadership.

Diverse and inclusive teams are not merely a moral imperative but also a catalyst for scientific excellence in robotics. Drawing from literature, a co...

Automated segmentation of dental restorations using deep learning: exploring data augmentation techniques.

OBJECTIVES: Deep learning has revolutionized image analysis for dentistry. Automated segmentation of dental radiographs is of great importance towards...

A multi-memory-augmented network with a curvy metric method for video anomaly detection.

Anomaly detection task in video mainly refers to identifying anomalous events that do not conform to the learned normal patterns in the inferring phas...

Domain generalization for mammographic image analysis with contrastive learning.

The deep learning technique has been shown to be effectively addressed several image analysis tasks in the computer-aided diagnosis scheme for mammogr...

Integrating microbial profiling and machine learning for inference of drowning sites: a forensic investigation in the Northwest River.

Drowning incidents present significant challenges for forensic investigators in determining the exact site of occurrence. Traditional forensic methods...

Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning.

The conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty...

High-throughput platform for label-free sorting of 3D spheroids using deep learning.

End-stage liver diseases have an increasing impact worldwide, exacerbated by the shortage of transplantable organs. Recognized as one of the promising...

Classifying forensically important flies using deep learning to support pathologists and rescue teams during forensic investigations.

Forensic entomology can help estimate the postmortem interval in criminal investigations. In particular, forensically important fly species that can b...

AI-assisted human clinical reasoning in the ICU: beyond "to err is human".

Diagnostic errors pose a significant public health challenge, affecting nearly 800,000 Americans annually, with even higher rates globally. In the ICU...

Semantic Mask Reconstruction and Category Semantic Learning for few-shot image generation.

Few-shot image generation aims at generating novel images for the unseen category when given K images from the same category. Despite significant adva...

Editorial Commentary: Anterior Cruciate Ligament Patients Must Be Provided With Both Motivation and Tools to Continue Physical Therapy, Supervised or Independently, Beyond the Early Recovery Phase.

Given the current American health insurance environment, many patients do not access physical therapy in the mid and later stages of recovery from ant...