Search results for "artificial intelligence"
showing 10 items of 6122 documents
Classification of Schizophrenia Patients and Healthy Controls Using ICA of Complex-Valued fMRI Data and Convolutional Neural Networks
2019
Deep learning has contributed greatly to functional magnetic resonance imaging (fMRI) analysis, however, spatial maps derived from fMRI data by independent component analysis (ICA), as promising biomarkers, have rarely been directly used to perform individualized diagnosis. As such, this study proposes a novel framework combining ICA and convolutional neural network (CNN) for classifying schizophrenia patients (SZs) and healthy controls (HCs). ICA is first used to obtain components of interest which have been previously implicated in schizophrenia. Functionally informative slices of these components are then selected and labelled. CNN is finally employed to learn hierarchical diagnostic fea…
Digital Acquisition and Processing of Video Angiocardiograms
1986
Angiocardiographic diagnosis is still mainly based on the visual assessment of radiographic projection images recorded on photographic film. Considerable amounts of contrast material have to be selectively injected into the circulation in order to make the regions of diagnostic relevance visible in the superposition of the shadows of tissue and bone structures displayed in these transmission images. In addition, selective angiocardiography requires exact positioning of the catheter, a time-consuming procedure which is not without risk. The processing of the angiographie films obtained is difficult to maintain at a constant high quality level, and this introduces an disadvantageous delay bet…
Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition
2018
Tensor decomposition has been widely employed for EEG signal processing in recent years. Constrained and regularized tensor decomposition often attains more meaningful and interpretable results. In this study, we applied sparse nonnegative CANDECOMP/PARAFAC tensor decomposition to ongoing EEG data under naturalistic music stimulus. Interesting temporal, spectral and spatial components highly related with music features were extracted. We explored the ongoing EEG decomposition results and properties in a wide range of sparsity levels, and proposed a paradigm to select reasonable sparsity regularization parameters. The stability of interesting components extraction from fourteen subjects’ dat…
Functional Near Infrared Spectroscopy System Validation for Simultaneous EEG-FNIRS Measurements
2019
Functional near-infrared spectroscopy (fNIRS) applied to brain monitoring has been gaining increasing relevance in the last years due to its not invasive nature and the capability to work in combination with other well–known techniques such as the EEG. The possible use cases span from neural-rehabilitation to early diagnosis of some neural diseases. In this work a wireline FPGA–based fNIRS system, that use SiPM sensors and dual-wavelength LED sources, has been designed and validated to work with a commercial EEG machine without reciprocal interference.
Empirical Mode Decomposition on Mismatch Negativity
2008
Empirical mode decomposition (EMD) has been applied in the various disciplines to extract the desired signal. The basic principle is to decompose a time series into intrinsic mode functions (IFMs) and each IFM corresponds to an oscillation phenomenon. A statistical description of the oscillatory activities of the EEG has been well known. It is desired to extract single oscillatory process from the EEG by EMD. Mismatch negativity (MMN) can be automatically elicited by the deviant stimulus in an oddball paradigm, in which physically the deviant stimulus occurs among repetitive and homogeneous stimuli. MMN thus reflects the ability of the brain to detect changes in auditory stimuli. So, the MM…
Extract Mismatch Negativity and P3a through Two-Dimensional Nonnegative Decomposition on Time-Frequency Represented Event-Related Potentials
2010
This study compares the row-wise unfolding nonnegative tensor factorization (NTF) and the standard nonnegative matrix factorization (NMF) in extracting time-frequency represented event-related potentials—mismatch negativity (MMN) and P3a from EEG under the two-dimensional decomposition The criterion to judge performance of NMF and NTF is based on psychology knowledge of MMN and P3a MMN is elicited by an oddball paradigm and may be proportionally modulated by the attention So, participants are usually instructed to ignore the stimuli However the deviant stimulus inevitably attracts some attention of the participant towards the stimuli Thus, P3a often follows MMN As a result, if P3a was large…
Real and simulated endoscopy of neurosurgical approaches in an anatomical model
1997
Endoscopy simulation is a new visualisation technique which can be used to visualise patient anatomy as seen through endoscopes. In the following, we describe the application of simulated endoscopy to neurosurgical endoscopic approaches. We assess different aspects of virtual images and their influence on the final result, as judged by clinicians. One of these aspects is the projective geometry which is used to render the 3D images. Rendering using stereographic projection leads to more realistic endoscopic views than perspective projection.
Study of CT/MRI mutual information based registration applied in brachytherapy
2016
The present work aims to include magnetic resonance imaging (MRI) in a Medical Image-based Graphical platfOrm - Brachytherapy module (AMIGOBrachy) which coupled to the Monte Carlo N-Particle (MCNP6) code allows absorbed dose calculations. Computed tomography (CT) and MRI images were registered using mutual information algorithms to improve tissue segmentation potentially leading to a more accurate treatment planning system.
Towards data-driven medical imaging using natural language processing in patients with suspected urolithiasis.
2020
Abstract Objective The majority of radiological reports are still written as free text and lack structure. Further evaluation of free-text reports is difficult to achieve without a great deal of manual effort, and is not possible in everyday clinical practice. This study aims to automatically capture clinical information and positive hit rates from narrative radiological reports of suspected urolithiasis using natural language processing (NLP). Methods Narrative reports of low dose computed tomography (CT) of the retroperitoneum from April 2016 to July 2018 (n = 1714) were analyzed using NLP. These free-text reports were automatically structured based on RadLex concepts. Manual feedback was…
Diagnostic Performance of an Artificial Intelligence System in Breast Ultrasound.
2021
Objectives We study the performance of an artificial intelligence (AI) program designed to assist radiologists in the diagnosis of breast cancer, relative to measures obtained from conventional readings by radiologists. Methods A total of 10 radiologists read a curated, anonymized group of 299 breast ultrasound images that contained at least one suspicious lesion and for which a final diagnosis was independently determined. Separately, the AI program was initialized by a lead radiologist and the computed results compared against those of the radiologists. Results The AI program's diagnoses of breast lesions had concordance with the 10 radiologists' readings across a number of BI-RADS descri…