AIMC Topic: Humans

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Clinical Performance Evaluation of an Artificial Intelligence-Based Tool for Predicting the Presence of Obstructive Coronary Artery Disease: Protocol for a Cohort Observational Study.

JMIR research protocols
BACKGROUND: A significant number of individuals undergoing coronary computed tomography angiography (CCTA) for suspected (CAD) have nonobstructive or no CAD. There is a need for clinically proven models that can predict the pretest probability of sta...

Deep learning-based artefact reduction in low-dose dental cone beam computed tomography with high-attenuation materials.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
This paper examines the current challenges in computed tomography (CT), with a critical exploration of existing methodologies from a mathematical perspective. Specifically, it aims to identify research directions to enhance image quality in low-dose,...

Deep Learning-based Gait Recognition and Evaluation of the Wounded.

Disaster medicine and public health preparedness
OBJECTIVES: Remote injury assessment during natural disasters poses major challenges for healthcare providers due to the inaccessibility of disaster sites. This study aimed to explore the feasibility of using artificial intelligence (AI) techniques f...

3D electroacoustic tomography image enhancement using deep learning with the SAM-Med3D encoder.

Physics in medicine and biology
To overcome the limitations of electroacoustic tomography (EAT) in clinical settings-particularly the artifacts and distortions caused by limited-angle data acquisition-and enable accurate, efficient visualization of electric field distributions for ...

Machine learning-powered plasmonic pattern recognition: etch-suppressed gold nanorods for multiplex urinary analysis of catecholamine neurotransmitters.

Analytical methods : advancing methods and applications
Simultaneous monitoring of catecholamine neurotransmitters (CNTs)-including epinephrine (Epi), norepinephrine (NE), levodopa (L-DOPA), and dopamine (DA)-is essential for the accurate diagnosis and effective management of various neurological disorder...

Introduction of sub-band augmentation with machine learning to develop an insomnia classification model using single-channel EEG signals.

Physiological measurement
. Biological signals can be used to record sleep activities and can be used to identify sleep disorders. Insomnia is a sleep disorder that can be detected using supervised learning models developed using biological signal analysis. The baseline insom...

A graph neural network-based approach for predicting SARS-CoV-2-human protein interactions from multiview data.

PloS one
The COVID-19 pandemic has demanded urgent and accelerated action toward developing effective therapeutic strategies. Drug repurposing models (in silico) are in high demand and require accurate and reliable molecular interaction data. While experiment...

Measurement model of credit risk for unlisted agricultural enterprises.

PloS one
This paper aims to measure credit risks of unlisted agricultural enterprises by using the KMV model integrating a CNN-BiLSTM neural network. Initially, the expected default frequencies (EDF) for each listed agricultural enterprise are computed using ...

Predicting stock returns using machine learning combined with data envelopment analysis and automatic feature engineering: A case study on the Vietnamese stock market.

PloS one
In financial markets, predicting stock returns is an essential task for investors. This paper is one of the first studies using business efficiency scores calculated from data envelopment analysis to predict stock returns. In the meantime, this is al...

Equitable AI: Exploring the role of gender in poverty estimation models using geospatial data.

PloS one
Household surveys have been the foundation for poverty measurement in developing countries for the past half-century, but the spatial and temporal gaps in these survey data often limit how well anti-poverty programs can be targeted, monitored, or eva...