AIMC Topic: Reference Values
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Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves.
Radiology
Nov 24, 2020
Background Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obtaining these metrics has been too resource intensive for large-scale use. Thus, population-wide distributions of BC remain uncertain. Purpose To demon...
COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.
Interdisciplinary sciences, computational life sciences
Sep 21, 2020
The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce ...
Humans
Neural Networks, Computer
Machine Learning
Pandemics
COVID-19
Coronavirus
Models, Biological
Coronavirus Infections
Reference Values
COVID-19 Testing
Clinical Laboratory Techniques
Betacoronavirus
Pneumonia, Viral
Datasets as Topic
X-Rays
Lung
Deep Learning
Pneumonia
Radiography
Algorithms
Databases, Factual
Tomography, X-Ray Computed
SARS-CoV-2
Countermovement Jump Standards in Rugby League: What is a "Good" Performance?
Journal of strength and conditioning research
Jul 16, 2020
McMahon, JJ, Lake, JP, Dos'Santos, T, Jones, PA, Thomasson, ML, and Comfort, P. Countermovement jump standards in rugby league: what is a "good" performance? J Strength Cond Res 36(6): 1691-1698, 2022-The countermovement jump (CMJ) is considered an i...
Deep learning-based reconstruction of ultrasound images from raw channel data.
International journal of computer assisted radiology and surgery
Jun 3, 2020
PURPOSE: We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic...
Robot-Driven Locomotor Perturbations Reveal Synergy-Mediated, Context-Dependent Feedforward and Feedback Mechanisms of Adaptation.
Scientific reports
Mar 25, 2020
Humans respond to mechanical perturbations that affect their gait by changing their motor control strategy. Previous work indicates that adaptation during gait is context dependent, and perturbations altering long-term stability are compensated for e...
Effects of Deep Learning Reconstruction Technique in High-Resolution Non-contrast Magnetic Resonance Coronary Angiography at a 3-Tesla Machine.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Feb 19, 2020
PURPOSE: To evaluate the effects of deep learning reconstruction (DLR) in qualitative and quantitative image quality of non-contrast magnetic resonance coronary angiography (MRCA).
KETOS: Clinical decision support and machine learning as a service - A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services.
PloS one
Oct 3, 2019
BACKGROUND AND OBJECTIVE: To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in...
Comparison of Deep Learning-Based and Patch-Based Methods for Pseudo-CT Generation in MRI-Based Prostate Dose Planning.
International journal of radiation oncology, biology, physics
Sep 7, 2019
PURPOSE: Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for magnetic resonance imaging (MRI) based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network [GAN]) usin...
Is it possible to detect cerebral dominance via EEG signals by using deep learning?
Medical hypotheses
Jul 20, 2019
Each brain hemisphere is dominant for certain functions such as speech. The determination of speech laterality prior to surgery is of paramount importance for accurate risk prediction. In this study, we aimed to determine speech laterality via EEG si...
Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers.
British journal of anaesthesia
Jul 18, 2019
BACKGROUND: Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine le...
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