AIMC Topic: Automation

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Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Detection and quantification of functional deficits due to moderate traumatic brain injury (mTBI) is crucial for clinical decision-making and timely commencement of functional therapy. In this work, we explore magnetoencephalography (MEG) based funct...

Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to...

Fully automated 3D segmentation and separation of multiple cervical vertebrae in CT images using a 2D convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: We investigated a novel method using a 2D convolutional neural network (CNN) to identify superior and inferior vertebrae in a single slice of CT images, and a post-processing for 3D segmentation and separation of cervical ve...

Automation With Intelligence in Drug Research.

Clinical therapeutics
The industry has adopted Clinical Data Interchange Standards Consortium standards for clinical trial data and the Food and Drug Administration electronic common technical document standard for documents for many years but still faces many challenges....

A strategy combining intrinsic time-scale decomposition and a feedforward neural network for automatic seizure detection.

Physiological measurement
UNLABELLED: Epilepsy is a common neurological disorder which can occur in people of all ages globally. For the clinical treatment of epileptic patients, the detection of epileptic seizures is of great significance.

A deep learning framework for automatic diagnosis of unipolar depression.

International journal of medical informatics
BACKGROUND AND PURPOSE: In recent years, the development of machine learning (ML) frameworks for automatic diagnosis of unipolar depression has escalated to a next level of deep learning frameworks. However, this idea needs further validation. Theref...

Automating Complex, Multistep Processes on a Single Robotic Platform to Generate Reproducible Phosphoproteomic Data.

SLAS discovery : advancing life sciences R & D
Mass spectrometry-based phosphoproteomics holds promise for advancing drug treatment and disease diagnosis; however, its clinical translation has thus far been limited. This is in part due to an unstandardized and segmented sample preparation process...

Automatic detection of contouring errors using convolutional neural networks.

Medical physics
PURPOSE: To develop a head and neck normal structures autocontouring tool that could be used to automatically detect the errors in autocontours from a clinically validated autocontouring tool.

Deep Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs.

Journal of vascular and interventional radiology : JVIR
PURPOSE: To demonstrate the feasibility and evaluate the performance of a deep-learning convolutional neural network (CNN) classification model for automated identification of different types of inferior vena cava (IVC) filters on radiographs.

Automated Detection of Vulnerable Plaque for Intravascular Optical Coherence Tomography Images.

Cardiovascular engineering and technology
PURPOSE: Vulnerable plaque detection is important to acute coronary syndrome (ACS) diagnosis. In recent years, intravascular optical coherence tomography (IVOCT) imaging has been used for vulnerable plaque detection. Current automated detection metho...