AIMC Topic: Female

Clear Filters Showing 1991 to 2000 of 29210 articles

Attitudes and readiness to adopt artificial intelligence among healthcare practitioners in Pakistan's resource-limited settings.

BMC health services research
BACKGROUND: Artificial Intelligence (AI) can empower clinicians to make data-driven decisions, treatments and streamline administrative tasks. However, it is vital to understand their perception towards AI for seamless implementation in practice. The...

Dynamic neural network modulation associated with rumination in major depressive disorder: a prospective observational comparative analysis of cognitive behavioral therapy and pharmacotherapy.

Translational psychiatry
Cognitive behavioral therapy (CBT) and pharmacotherapy are primary treatments for major depressive disorder (MDD). However, their differential effects on the neural networks associated with rumination, or repetitive negative thinking, remain poorly u...

An improved domain-adversarial network for predicting hemodialysis adequacy.

Biomedical physics & engineering express
Hemodialysis (HD) is the primary life-sustaining treatment for patients with end-stage renal disease (ESRD). However, current real-time monitoring methods during dialysis are costly, complex, and not widely adopted. Therefore, this study aims to prop...

Neural Synchrony and Consumer Behavior: Predicting Friends' Behavior in Real-World Social Networks.

The Journal of neuroscience : the official journal of the Society for Neuroscience
The endogenous aspect of social influence, reflected in the spontaneous alignment of behaviors within close social relationships, plays a crucial role in understanding human social behavior. In two studies involving 222 human subjects (Study 1:  = 17...

Predicting depression using serum perfluoroalkyl and polyfluoroalkyl substances levels via interpretable machine learning.

Journal of affective disorders
BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals with widespread environmental persistence and human exposure. Currently, no studies have used machine learning (ML) to predict depression based on PFAS exposure. This stud...

Neural Signals-Based Respiratory Motion Tracking: A Surface Electromyography Study.

International journal of radiation oncology, biology, physics
PURPOSE: Neural signals-based respiratory motion tracking offers a potential solution to the system latency issue of medical linear accelerators in respiratory motion tracking radiation therapy. However, decoding respiratory-related neural signals fr...

Altered effective connectivity in patients with drug-naïve first-episode, recurrent, and medicated major depressive disorder: A multi-site fMRI study.

Behavioural brain research
BACKGROUND: Major depressive disorder (MDD) has been diagnosed through subjective and inconsistent clinical assessments. Resting-state functional magnetic resonance imaging (rs-fMRI) with connectivity analysis has been valuable for identifying neural...

A deep learning model for diagnosing autism using brain time series.

Neuroscience
The early identification of autism is especially critical as it can significantly enhance the effectiveness of intervention strategies. However, the recognition task remains challenging due to the subtle differences between ASD patients and neurotypi...

Beyond unimodal analysis: Multimodal ensemble learning for enhanced assessment of atherosclerotic disease progression.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Atherosclerosis is a leading cardiovascular disease typified by fatty streaks accumulating within arterial walls, culminating in potential plaque ruptures and subsequent strokes. Existing clinical risk scores, such as systematic coronary risk estimat...

An AI-driven video based goniometer for knee joint range of motion (ROM) Assessment: Reliability and validity compared to traditional goniometry.

Computers in biology and medicine
BACKGROUND: Accurate measurement of knee joint range of motion (ROM) is crucial in clinical and rehabilitation settings. Traditional goniometry, which is widely used, requires calibration and is subject to human errors and measurement inconsistencies...