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How to enhance employee engagement in the AI era: An fsQCA-based study.

Work (Reading, Mass.)
BackgroundThe ongoing fourth industrial revolution, characterized by the integration of intelligent machines, presents a transformative shift in the nature of work. Unlike past industrial revolutions that reshaped job dynamics, the current reliance o...

Detecting fast-ripples on both micro- and macro-electrodes in epilepsy: A wavelet-based CNN detector.

Journal of neuroscience methods
BACKGROUND: Fast-ripples (FR) are short (∼10 ms) high-frequency oscillations (HFO) between 200 and 600 Hz that are helpful in epilepsy to identify the epileptogenic zone. Our aim is to propose a new method to detect FR that had to be efficient for in...

Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology.

Sensors (Basel, Switzerland)
Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural networ...

ChatGPT and frequently asked patient questions for upper eyelid blepharoplasty surgery.

Orbit (Amsterdam, Netherlands)
PURPOSE: Online health information seekers may access information produced by artificial intelligence language models such as ChatGPT (OpenAI). The medical field may pose a significant challenge for incorporating these applications given the training...

Deep learning-based prediction of tumor aggressiveness in RCC using multiparametric MRI: a pilot study.

International urology and nephrology
OBJECTIVE: To investigate the value of multiparametric magnetic resonance imaging (MRI) as a non-invasive method to predict the aggressiveness of renal cell carcinoma (RCC) by developing a convolutional neural network (CNN) model and fusing it with c...

Conditional generative diffusion deep learning for accelerated diffusion tensor and kurtosis imaging.

Magnetic resonance imaging
PURPOSE: The purpose of this study was to develop DiffDL, a generative diffusion probabilistic model designed to produce high-quality diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics from a reduced set of diffusion-weighted...

ECG-based machine learning model for AF identification in patients with first ischemic stroke.

International journal of stroke : official journal of the International Stroke Society
BACKGROUND: The recurrence rate of strokes associated with atrial fibrillation (AF) can be substantially reduced through the administration of oral anticoagulants. However, previous studies have not demonstrated a clear benefit from the universal app...

Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Accurate prediction of perioperative major adverse cardiovascular events (MACEs) is crucial, as it not only aids clinicians in comprehensively assessing patients' surgical risks and tailoring personalized surgical and periop...

Diagnosis of Fibrotic Interstitial Lung Diseases Based on the Combination of Label-Free Quantitative Multiphoton Fiber Histology and Machine Learning.

Laboratory investigation; a journal of technical methods and pathology
Interstitial lung disease (ILD), characterized by inflammation and fibrosis, often suffers from low diagnostic accuracy and consistency. Traditional hematoxylin and eosin (H&E) staining primarily reveals cellular inflammation with limited detail on f...