AIMC Topic: Adult

Clear Filters Showing 11461 to 11470 of 15606 articles

Automated detection and classification of the proximal humerus fracture by using deep learning algorithm.

Acta orthopaedica
Background and purpose - We aimed to evaluate the ability of artificial intelligence (a deep learning algorithm) to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs. Patients and methods - 1,891 images (...

Resting-state functional connectivity predicts the ability to adapt arm reaching in a robot-mediated force field.

NeuroImage
Motor deficits are common outcomes of neurological conditions such as stroke. In order to design personalised motor rehabilitation programmes such as robot-assisted therapy, it would be advantageous to predict how a patient might respond to such trea...

Pulling force prediction using neural networks.

International journal of occupational safety and ergonomics : JOSE
PURPOSE: In ergonomics and human factors investigations, pulling force (PF) estimation has usually been achieved using various types of biomechanical models, and independent approximation of PF was done with the help of upper extremity joints. Recent...

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI.

EBioMedicine
BACKGROUND: A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning met...

The future of the provision process for mobility assistive technology: a survey of providers.

Disability and rehabilitation. Assistive technology
PURPOSE: The purpose of this study was to evaluate the opinions of providers of mobility assistive technologies to help inform a research agenda and set priorities.

Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
BACKGROUND AND PURPOSE: Current prognostic models for soft tissue sarcoma (STS) patients are solely based on staging information. Treatment-related data have not been included to date. Including such information, however, could help to improve these ...

Ongoing brain rhythms shape I-wave properties in a computational model.

Brain stimulation
BACKGROUND: Responses to transcranial magnetic stimulation (TMS) are notoriously variable. Previous studies have observed a dependence of TMS-induced responses on ongoing brain activity, for instance sensorimotor rhythms. This suggests an opportunity...

Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

Journal of voice : official journal of the Voice Foundation
OBJECTIVES: Computerized detection of voice disorders has attracted considerable academic and clinical interest in the hope of providing an effective screening method for voice diseases before endoscopic confirmation. This study proposes a deep-learn...

Thalamocortical dysrhythmia detected by machine learning.

Nature communications
Thalamocortical dysrhythmia (TCD) is a model proposed to explain divergent neurological disorders. It is characterized by a common oscillatory pattern in which resting-state alpha activity is replaced by cross-frequency coupling of low- and high-freq...