AIMC Topic: Humans

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Advancing healthcare through mobile collaboration: a survey of intelligent nursing robots research.

Frontiers in public health
Mobile collaborative intelligent nursing robots have gained significant attention in the healthcare sector as an innovative solution to address the challenges posed by the increasing aging population and limited medical resources. This article provid...

Altered Blood Oxygen Level-Dependent Signal Stability in the Brain of Patients with Major Depressive Disorder Undergoing Resting-State Functional Magnetic Resonance Imaging.

Neuropsychobiology
INTRODUCTION: Major depressive disorder (MDD) is a common, relapse-prone psychiatric disorder with unknown pathogenesis. Previous studies on resting-state functional magnetic resonance imaging of MDD have mostly focused on the spontaneous activity of...

Deep learning-enhanced automated mitochondrial segmentation in FIB-SEM images using an entropy-weighted ensemble approach.

PloS one
Mitochondria are intracellular organelles that act as powerhouses by breaking down nutrition molecules to produce adenosine triphosphate (ATP) as cellular fuel. They have their own genetic material called mitochondrial DNA. Alterations in mitochondri...

Uncovering the Probiotic Supplement Landscape: Market Offerings, Sales Patterns, and Future Forecasts Using Machine Learning Approach - A Case Study of Montenegro.

Probiotics and antimicrobial proteins
Global increasing awareness about the health benefits of probiotics resulted to explorational growth in probiotic food supplement market. However, in some countries such as Montenegro, specific probiotic supplement regulation and comprehensive market...

Daily stress detection from real-life speeches using acoustic and semantic information.

Ergonomics
Detecting daily stress is of vital importance for workplace safety and health, and natural speech is recommended as one of the main methods of mental stress detection. This study developed machine-learning models for daily stress detection from real-...

Motivation and socialization during summer predict medical students' success: An artificial intelligence study.

Medical teacher
PURPOSE: The latest reform of French medical studies has moved the National Ranking Examination before residency to the beginning of the sixth-year for undergraduate medical students, thus placing unprecedented workload during the preceding summer. T...

The Value of Whole-Volume Radiomics Machine Learning Model Based on Multiparametric MRI in Predicting Triple-Negative Breast Cancer.

Journal of computer assisted tomography
OBJECTIVE: This study aimed to investigate the value of radiomics analysis in the precise diagnosis of triple-negative breast cancer (TNBC) based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coeffici...

Hearing loss prediction equation for Iranian truck drivers using neural network algorithm.

Work (Reading, Mass.)
BACKGROUND:  Given the high prevalence of hearing loss among truck drivers, using artificial neural networks (ANNs) to predict and detect contributing factors early can aid managers significantly.

Detecting irregularities in randomized controlled trials using machine learning.

Clinical trials (London, England)
BACKGROUND: Over the course of a clinical trial, irregularities may arise in the data. Trialists implement human-intensive, expensive central statistical monitoring procedures to identify and correct these irregularities before the results of the tri...

The added value of including thyroid nodule features into large language models for automatic ACR TI-RADS classification based on ultrasound reports.

Japanese journal of radiology
OBJECTIVE: The ACR Thyroid Imaging, Reporting, and Data System (TI-RADS) uses a score based on ultrasound (US) imaging to stratify the risk of nodule malignancy and recommend appropriate follow-up. This study aims to analyze US reports and explore ho...