AIMC Topic: Humans

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Patch-type wearable electrocardiography and impedance pneumography for sleep staging: A multi-modal deep learning approach.

Computers in biology and medicine
Sleep staging is critical for investigating sleep quality and detecting disorders. Polysomnography (PSG) remains the gold standard, but is costly and impractical for routine monitoring. This study evaluates the feasibility of a patch-type wearable de...

Predicting occupant response curves in vehicle crashes via Attention-enhanced multimodal temporal Network.

Accident; analysis and prevention
Accurately predicting safety responses, especially occupant crash response curves across multiple body regions, plays a crucial role in advancing vehicle crash safety by enabling design optimization and reducing the reliance on costly physical testin...

Reliability and validity of lower extremity and trunk kinematics measured with markerless motion capture during sports-related and functional tasks: A systematic review.

Journal of sports sciences
This study reviewed the literature regarding reliability and validity of markerless motion capture (MMC) for measuring lower extremities and trunk kinematics during sports-related and functional tasks. Articles published until 28 February 2024 were a...

Predicting response to patients with gastric cancer via a dynamic-aware model with longitudinal liquid biopsy data.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND: Gastric cancer (GC) presents challenges in predicting treatment responses due to its patient-specific heterogeneity. Recently, liquid biopsies have emerged as a valuable data modality, offering essential cellular and molecular insights wh...

Achieving success through ethical introduction of artificial intelligence in the healthcare ecosystem.

Healthcare management forum
Just as healthcare organizations must carefully consider how to incorporate Artificial Intelligence (AI) into patient-facing apps and messaging, so must they also think about how to ethically introduce AI into the workplace. Unreasonable expectations...

Unraveling ADHD Through Eye-Tracking Procedures: A Scoping Review.

Journal of attention disorders
OBJECTIVE: This scoping review aimed to examine the application of eye-tracking technology in children with Attention-Deficit/Hyperactivity Disorder (ADHD), focusing on the scientific fields involved, methodologies employed, research goals, and outco...

Aphasia severity prediction using a multi-modal machine learning approach.

NeuroImage
The present study examined an integrated multiple neuroimaging modality (T1 structural, Diffusion Tensor Imaging (DTI), and resting-state FMRI (rsFMRI)) to predict aphasia severity using Western Aphasia Battery-Revised Aphasia Quotient (WAB-R AQ) in ...

Differences in resting-state functional connectivity between depressed bipolar and major depressive disorder patients: A machine learning study.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Nearly 60 % of individuals with bipolar disorder (BD) are initially classified as major depressive disorder (MDD) patients, resulting in inappropriate drug treatment. Identifying reliable biomarkers for the differential diagnosis between MDD and BD p...

Impact of Field-of-view Zooming and Segmentation Batches on Radiomics Features Reproducibility and Machine Learning Performance in Thyroid Scintigraphy.

Clinical nuclear medicine
BACKGROUND: Thyroid diseases are the second most common hormonal disorders, necessitating accurate diagnostics. Advances in artificial intelligence and radiomics have enhanced diagnostic precision by analyzing quantitative imaging features. However, ...

A Robust Residual Three-dimensional Convolutional Neural Networks Model for Prediction of Amyloid-β Positivity by Using FDG-PET.

Clinical nuclear medicine
BACKGROUND: Widely used in oncology PET, 2-deoxy-2- 18 F-FDG PET is more accessible and affordable than amyloid PET, which is a crucial tool to determine amyloid positivity in diagnosis of Alzheimer disease (AD). This study aimed to leverage deep lea...