AIMC Topic: Middle Aged

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Clinical efficacy of NIBS in enhancing neuroplasticity for stroke recovery.

Journal of neuroscience methods
BACKGROUND: For stroke patients, a therapeutic approach named Non-invasive brain stimulation (NIBS) was applied and it has gained attention. This NIBS approach enhances the neuroplasticity and facilitates in functional Stroke Rehabilitation (SR) thro...

Interpretation of basal nuclei in brain dopamine transporter scans using a deep convolutional neural network.

Nuclear medicine communications
OBJECTIVE: Functional imaging using the dopamine transporter (DAT) as a biomarker has proven effective in assessing dopaminergic neuron degeneration in the striatum. In assessing the neuron degeneration, visual and semiquantitative methods are used t...

Explaining electroencephalogram channel and subband sensitivity for alcoholism detection.

Computers in biology and medicine
Alcoholism, a progressive loss of control over alcohol consumption, deteriorates mental and physical health over time. Automatic alcoholism detection can aid in early interventions and timely corrective actions. For this purpose, electroencephalogram...

Intelligent Verification Tool for Surgical Information of Ophthalmic Patients: A Study Based on Artificial Intelligence Technology.

Journal of patient safety
OBJECTIVE: With the development of day surgery, the characteristics of "short, frequent and fast" ophthalmic surgery are becoming more prominent. However, nurses are not efficient in verifying patients' surgical information, and problems such as pati...

Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point F-FDG PET/CT.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point F-FDG PET/CT to predict the malignant ris...

Diabetic peripheral neuropathy detection of type 2 diabetes using machine learning from TCM features: a cross-sectional study.

BMC medical informatics and decision making
AIMS: Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus. Early identification of individuals at high risk of DPN is essential for successful early intervention. Traditional Chinese medicine (TCM) tongue diagnos...

Sway frequencies may predict postural instability in Parkinson's disease: a novel convolutional neural network approach.

Journal of neuroengineering and rehabilitation
BACKGROUND: Postural instability greatly reduces quality of life in people with Parkinson's disease (PD). Early and objective detection of postural impairments is crucial to facilitate interventions. Our aim was to use a convolutional neural network ...

Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer.

BMC cancer
OBJECTIVE: This study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopathological features in the survival analysis of triple-negative breast cancer (TNBC) patients.

Deep learning-based classification of diffusion-weighted imaging-fluid-attenuated inversion recovery mismatch.

Scientific reports
The presence of a diffusion-weighted imaging (DWI)-fluid-attenuated inversion recovery (FLAIR) mismatch holds potential value in identifying candidates for recanalization treatment. However, the visual assessment of DWI-FLAIR mismatch is subject to l...

The development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas.

Scientific reports
Linear and volumetric analysis are the typical methods to measure tumor size. 3D volumetric analysis has risen in popularity; however, this is very time and labor intensive limiting its implementation in clinical practice. This study aims to show tha...