As inflow and outflow stenoses worsen, both flow resistance and pressure increase in the stenotic vascular access. During dialysis, when blood flow decreases, it may retrograde from the peripheral artery through the palmar arch to the arterial anastomosis site. Arterial steal syndrome (ASS) causes distal hypoperfusion, resulting in hand ischemia or extremity pain and edema. Hence, this study proposes the bilateral photoplethysmography (PPG) for ASS detection in arteriovenous fistulas. The decision-making quantizer utilizes the fractional-order feature extraction method and a non-cooperative game (NCG) framework to evaluate the ASS risk level. Bilateral asynchronous PPG signals have significant differences in the rise time and amplitude in relation to the degree of stenosis. The fractional-order self-synchronization error formulation is a feature extraction method used to quantify bilateral differences in blood flow changes between the dexter and sinister PPG signals. The NCG model as a method of decision-making is then employed to evaluate the ASS risk level. Using an acoustic Doppler measurement, the resistive (Res) index is also used to evaluate the vascular access stenosis at the arterial anastomosis site. In contrast with alternative methods including the high-sensitivity C-reactive protein level or Res index, our experimental results indicate that the proposed decision-making quantizer is more efficient in preventing ASS during hemodialysis treatment.
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Heliyon
January 2025
Key Lab of Forensic Science, Ministry of Justice, China (Academy of Forensic Science), Shanghai, 200063, China.
Testing autonomous vehicles (AVs) in hazardous scenarios is a crucial technical approach to ensure their safety. A key aspect of this process is the generation of hazard scenarios. In general, such scenarios are generated through cluster analysis of traffic accident data.
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December 2024
Apurba NSU R&D Lab, Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.
Background: Clinical Language Models (CLMs) possess the potential to reform traditional healthcare systems by aiding in clinical decision making and optimal resource utilization. They can enhance patient outcomes and help healthcare management through predictive clinical tasks. However, their real-world deployment is limited due to high computational cost at inference, in terms of both time and space complexity.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
December 2024
Mechanics and Materials Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904-0495, Japan.
Inspired by the way that digital artists zoom out of the canvas to assess the visual impact of their works, we introduce a conceptually simple yet effective metric for quantifying the clarity of digital images. This metric contrasts original images with progressively "melted" counterparts, produced by randomly flipping adjacent pixel pairs. It measures the presence of stable structures, assigning the value zero to completely uniform or random images and finite values for those with discernible patterns.
View Article and Find Full Text PDFInt J Gen Med
November 2024
Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan.
Purpose: Artificial intelligence (AI) holds great potential for revolutionizing health care by providing clinicians with data-driven insights that support more accurate and efficient clinical decisions. However, applying AI in clinical settings is often challenging due to the complexity and vastness of medical information. This perspective article explores how AI development methodologies can be adapted to support clinicians in their decision-making processes, emphasizing the importance of a hybrid approach that combines AI capabilities with clinicians' expertise.
View Article and Find Full Text PDFIEEE Trans Biomed Circuits Syst
October 2024
Cognitive navigation, a high-level and crucial function for organisms' survival in nature, enables autonomous exploration and navigation within the environment. However, most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation, capable of online decision-making through reward-based learning.
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