AIMC Topic: Adult

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Test-retest reliability of kinematic and EEG low-beta spectral features in a robot-based arm movement task.

Biomedical physics & engineering express
Low-beta (L, 13-20 Hz) power plays a key role in upper-limb motor control and afferent processing, making it a strong candidate for a neurophysiological biomarker. We investigate the test-retest reliability of Lpower and kinematic features from a rob...

A multicentric study examining a deep-learning-based computer model for classifying bipolar disorder using retinal vascular images.

Journal of affective disorders
OBJECTIVES: Due to easy accessibility, the retina is considered a window to the brain. Recent studies have reported retinal vascular abnormalities in bipolar disorder. Deep learning analysis, an advanced computational approach, has been implemented i...

Managers' perceptions and attitudes toward the use of artificial intelligence technology in selected hospital settings.

International journal of medical informatics
BACKGROUND: Over the past decade, artificial intelligence (AI) has transformed healthcare systems by improving cost control, clinical decision-making, and chronic disease management. This study assessed healthcare managers' perceptions of AI use in h...

Metabolomic profiling of plasma reveals differential disease severity markers in avian influenza A(H7N9) infection patients.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
OBJECTIVES: Avian influenza such as H7N9 is currently a major global public health risk, and at present, there is a lack of relevant diagnostic and treatment markers.

Automated sex and age estimation from orthopantomograms using deep learning: A comparison with human predictions.

Forensic science international
INTRODUCTION/OBJECTIVES: Estimating sex and chronological age is crucial in forensic dentistry and forensic identification. Traditional manual methods for sex and age estimation are labor-intensive, time-consuming, and prone to errors. This study aim...

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...

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, ...

Development and interpretation of machine learning-based prognostic models for predicting high-risk prognostic pathological components in pulmonary nodules: integrating clinical features, serum tumor marker and imaging features.

Journal of cancer research and clinical oncology
BACKGROUND: With the improvement of imaging, the screening rate of Pulmonary nodules (PNs) has further increased, but their identification of High-Risk Prognostic Pathological Components (HRPPC) is still a major challenge. In this study, we aimed to ...