AIMC Topic: Adult

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EEG based depression detection by machine learning: Does inner or overt speech condition provide better biomarkers when using emotion words as experimental cues?

Journal of psychiatric research
BACKGROUND: Objective diagnostic approaches need to be tested to enhance the efficacy of depression detection. Non-invasive EEG-based identification represents a promising area.

A Fully Automated Pipeline Using Swin Transformers for Deep Learning-Based Blood Segmentation on Head Computed Tomography Scans After Aneurysmal Subarachnoid Hemorrhage.

World neurosurgery
BACKGROUND: Accurate volumetric assessment of spontaneous aneurysmal subarachnoid hemorrhage (aSAH) is a labor-intensive task performed with current manual and semiautomatic methods that might be relevant for its clinical and prognostic implications....

Development and validation of a two-stage convolutional neural network algorithm for segmentation of MRI white matter hyperintensities for longitudinal studies in CADASIL.

Computers in biology and medicine
BACKGROUND: Segmentation of white matter hyperintensities (WMH) in CADASIL, one of the most severe cerebral small vessel disease of genetic origin, is challenging.

Identifying psychological predictors of SARS-CoV-2 vaccination: A machine learning study.

Vaccine
BACKGROUND: Major barriers to addressing SARS-CoV-2 vaccine hesitancy include limited knowledge of what causes delay/refusal of SARS-CoV-2 vaccination and limited ability to predict who will remain unvaccinated over significant time periods despite v...

Estimating highest capacity propulsion performance using backward-directed force during walking evaluation for individuals with acquired brain injury.

Journal of neuroengineering and rehabilitation
There are over 5.3 million Americans who face acquired brain injury (ABI)-related disability as well as almost 800,000 who suffer from stroke each year. To improve mobility and quality of life, rehabilitation professionals often focus on walking reco...

Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study.

BMC psychiatry
BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during childhood but often persists into adulthood, significantly impacting individuals' functioning, relati...

The impact of deep learning based- psychological capital with ideological and political education on entrepreneurial intentions.

Scientific reports
The aim of this study is to investigate the influence of psychological capital on college students' entrepreneurial intentions. Through a combination of relevant analysis and linear regression, the primary focus is on exploring the relationship betwe...

Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study.

BMC women's health
OBJECTIVE: Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approac...

Machine learning of brain-specific biomarkers from EEG.

EBioMedicine
BACKGROUND: Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnes...