AIMC Topic: Female

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Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.

European radiology
OBJECTIVES: Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine ...

Transductive Joint-Knowledge-Transfer TSK FS for Recognition of Epileptic EEG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Intelligent recognition of electroencephalogram (EEG) signals is an important means to detect seizure. Traditional methods for recognizing epileptic EEG signals are usually based on two assumptions: 1) adequate training examples are available for mod...

Translation of robot-assisted rehabilitation to clinical service: a comparison of the rehabilitation effectiveness of EMG-driven robot hand assisted upper limb training in practical clinical service and in clinical trial with laboratory configuration for chronic stroke.

Biomedical engineering online
BACKGROUND: Rehabilitation robots can provide intensive physical training after stroke. However, variations of the rehabilitation effects in translation from well-controlled research studies to clinical services have not been well evaluated yet. This...

Sex Determination of 3D Skull Based on a Novel Unsupervised Learning Method.

Computational and mathematical methods in medicine
In law enforcement investigation cases, sex determination from skull morphology is one of the important steps in establishing the identity of an individual from unidentified human skeleton. To our knowledge, existing studies of sex determination of t...

EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces.

Journal of neural engineering
OBJECTIVE: Brain-computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given...

Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network.

Molecular medicine reports
Asthma, the most common chronic respiratory tract disease in children, is characterized by allergy, recurring airway obstruction and bronchospasm. The aim of the present study was to screen critical differentially expressed genes (DEGs) involved in a...

A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data.

BMC medical informatics and decision making
BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthca...

A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training.

IEEE transactions on bio-medical engineering
OBJECTIVE: Loss of arm function is common in individuals with neurological damage, such as stroke or cerebral palsy. Robotic devices that address muscle strength deficits in a task-specific manner can assist in the recovery of arm function; however, ...

Design and Evaluation of a Motorized Robotic Bed Mover With Omnidirectional Mobility for Patient Transportation.

IEEE journal of biomedical and health informatics
Patient transportation in hospitals faces many challenges, including the limited manpower, work-related injuries, and low efficiency of current bed pushing methods. This paper presents a new motorized robotic bed mover with omnidirectional mobility t...

Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors.

Scientific reports
Black-blood (BB) imaging is used to complement contrast-enhanced 3D gradient-echo (CE 3D-GRE) imaging for detecting brain metastases, requiring additional scan time. In this study, we proposed deep-learned 3D BB imaging with an auto-labelling techniq...