AIMC Topic: Humans

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Artificial intelligence-based drug repurposing with electronic health record clinical corroboration: A case for ketamine as a potential treatment for amphetamine-type stimulant use disorder.

Addiction (Abingdon, England)
BACKGROUND AND AIMS: Amphetamine-type stimulants are the second-most used illicit drugs globally, yet there are no US Food and Drug Administration (FDA)-approved treatments for amphetamine-type stimulant use disorders (ATSUD). The aim of this study w...

High-throughput point-of-care serum iron testing utilizing machine learning-assisted deep eutectic solvent fluorescence detection platform.

Journal of colloid and interface science
In this study, a high-throughput point-of-care testing (HT-POCT) system for detecting serum iron was developed using a hydrophobic deep eutectic solvent (HDES) fluorescence detection platform. This machine learning-assisted portable platform enables ...

A digital neuromorphic system for working memory based on spiking neuron-astrocyte network.

Neural networks : the official journal of the International Neural Network Society
Among various types of memory, working memory (WM) plays a crucial role in reasoning, decision-making, and behavior regulation. Neuromorphic computing is a well-established engineering approach that offers promising avenues for advancing our understa...

Stress recognition identifying relevant facial action units through explainable artificial intelligence and machine learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Facial cues and expressions constitute a component of bodily responses that provide useful information about one's stress levels. According to the Facial Action Coding System, they can be modelled consistently in terms of fu...

Towards generalizable face forgery detection via mitigating spurious correlation.

Neural networks : the official journal of the International Neural Network Society
The continuous advancement of face forgery techniques has caused a series of trust crises, posing a significant menace to information security and personal privacy. In response, deep learning is being employed to develop effective detection methods t...

Signed graph embedding via multi-order neighborhood feature fusion and contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Signed graphs have been widely applied to model real-world complex networks with positive and negative links, and signed graph embedding has become a popular topic in the field of signed graph analysis. Although various signed graph embedding methods...

Multi-compartment neuron and population encoding powered spiking neural network for deep distributional reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Inspired by the brain's information processing using binary spikes, spiking neural networks (SNNs) offer significant reductions in energy consumption and are more adept at incorporating multi-scale biological characteristics. In SNNs, spiking neurons...

Smart data-driven medical decisions through collective and individual anomaly detection in healthcare time series.

International journal of medical informatics
BACKGROUND: Anomalies in healthcare refer to deviation from the norm of unusual or unexpected patterns or activities related to patients, diseases or medical centres. Detecting these anomalies is crucial for timely interventions and efficient decisio...

GraFMRI: A graph-based fusion framework for robust multi-modal MRI reconstruction.

Magnetic resonance imaging
PURPOSE: This study introduces GraFMRI, a novel framework designed to address the challenges of reconstructing high-quality MRI images from undersampled k-space data. Traditional methods often suffer from noise amplification and loss of structural de...

Multi-Institutional Evaluation and Training of Breast Density Classification AI Algorithm Using ACR Connect and AI-LAB.

Journal of the American College of Radiology : JACR
OBJECTIVE: To demonstrate and test the capabilities of the ACR Connect and AI-LAB software platform by implementing multi-institutional artificial intelligence (AI) training and validation for breast density classification.