AIMC Topic: Humans

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Three machine learning algorithms and their utility in exploring risk factors associated with primary cesarean section in low-risk women: A methods paper.

Research in nursing & health
Machine learning, a branch of artificial intelligence, is increasingly used in health research, including nursing and maternal outcomes research. Machine learning algorithms are complex and involve statistics and terminology that are not common in he...

Alzheimer's disease detection using depthwise separable convolutional neural networks.

Computer methods and programs in biomedicine
To diagnose Alzheimer's disease (AD), neuroimaging methods such as magnetic resonance imaging have been employed. Recent progress in computer vision with deep learning (DL) has further inspired research focused on machine learning algorithms. However...

Comparison of clinical outcomes and accuracy of electrode placement between robot-assisted and conventional deep brain stimulation of the subthalamic nucleus: a single-center study.

Acta neurochirurgica
BACKGROUND: Several surgical methods are used for deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD). This study aimed to compare clinical outcomes and electrode placement accuracy after robot-assisted (RAS) ver...

Dental disease detection on periapical radiographs based on deep convolutional neural networks.

International journal of computer assisted radiology and surgery
OBJECTIVES: It is with a great prospect to develop an auxiliary diagnosis system for dental periapical radiographs based on deep convolutional neural networks (CNNs), and the indications and performances should be investigated. The aim of this study ...

A Deep Learning Model for Classification of Endoscopic Gastroesophageal Reflux Disease.

International journal of environmental research and public health
Gastroesophageal reflux disease (GERD) is a common disease with high prevalence, and its endoscopic severity can be evaluated using the Los Angeles classification (LA grade). This paper proposes a deep learning model (i.e., GERD-VGGNet) that employs ...

The Influence of EMG-Triggered Robotic Movement on Walking, Muscle Force and Spasticity after an Ischemic Stroke.

Medicina (Kaunas, Lithuania)
: Application of the EMG-driven robotic training in everyday therapeutic processes is a modern and innovative form of neurorehabilitation among patients after stroke. Active participation of the patient contributes to significantly higher activation ...

Biped Walking Based on Stiffness Optimization and Hierarchical Quadratic Programming.

Sensors (Basel, Switzerland)
The spring-loaded inverted pendulum model is similar to human walking in terms of the center of mass (CoM) trajectory and the ground reaction force. It is thus widely used in humanoid robot motion planning. A method that uses a velocity feedback cont...

Machine learning-based prediction model using clinico-pathologic factors for papillary thyroid carcinoma recurrence.

Scientific reports
This study analyzed the prognostic significance of clinico-pathologic factors, including the number of metastatic lymph nodes (LNs) and lymph node ratio (LNR), in patients with papillary thyroid carcinoma (PTC), and attempted to construct a disease r...

A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications.

BMC bioinformatics
BACKGROUND: Non-targeted cytotoxics with anticancer activity are often developed through preclinical stages using response criteria observed in cell lines and xenografts. A panel of the NCI-60 cell lines is frequently the first line to define tumor t...