AIMC Topic: Aged

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Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI.

Behavioural brain research
Brain network alterations in patients with Alzheimer's disease (AD) has been the subject of much investigation, but the biological mechanisms underlying these alterations remain poorly understood. Here, we aim to identify the changes in brain network...

Neuro-fuzzy models for hand movements induced by functional electrical stimulation in able-bodied and hemiplegic subjects.

Medical engineering & physics
Functional Electrical Stimulation (FES) may be effective as a therapeutic treatment for improving functional reaching and grasping. Upper-limb FES models for predicting joint torques/angles from stimulation parameters can be useful to support the ite...

Da Vinci© Skills Simulator™: is an early selection of talented console surgeons possible?

Journal of robotic surgery
To investigate whether the learning curve of robotic surgery simulator training depends on the probands' characteristics, such as age and prior experience, we conducted a study of six distinct proband groups, using the da Vinci Skills Simulator: expe...

A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging.

Neuroscience
Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer's disease (AD). Non-invasive diagnostic methods are desirable to identify MCI for early therapeutic interventions. In this study, we proposed a support...

Using Machine Learning to Predict Laboratory Test Results.

American journal of clinical pathology
OBJECTIVES: While clinical laboratories report most test results as individual numbers, findings, or observations, clinical diagnosis usually relies on the results of multiple tests. Clinical decision support that integrates multiple elements of labo...

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning.

Scientific reports
Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automa...