AIMC Topic: Predictive Value of Tests
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The Use of Machine Learning Techniques to Determine the Predictive Value of Inflammatory Biomarkers in the Development of Type 2 Diabetes Mellitus.
Metabolic syndrome and related disorders
Feb 16, 2021
Certain inflammatory biomarkers, such as interleukin-6, interleukin-1, C-reactive protein (CRP), and fibrinogen, are prototypical acute-phase parameters that can also be predictors of cardiovascular disease. However, this inflammatory response can a...
A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.
The international journal of cardiovascular imaging
Feb 15, 2021
Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have f...
DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images.
Interdisciplinary sciences, computational life sciences
Feb 15, 2021
In the hospital, a limited number of COVID-19 test kits are available due to the spike in cases every day. For this reason, a rapid alternative diagnostic option should be introduced as an automated detection method to prevent COVID-19 spreading amon...
Automated Histological Segmentation on Micro Computed Tomography Images of Atherosclerotic Arteries.
European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
Feb 13, 2021
Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints.
Acta orthopaedica
Feb 12, 2021
Background and purpose - Machine learning (ML) techniques are a form of artificial intelligence able to analyze big data. Analyzing the outcome of (digital) questionnaires, ML might recognize different patterns in answers that might relate to differe...
Machine Learning-Derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins From Cardiac Computed Tomography Scans Are Associated With Risk of Recurrence of Atrial Fibrillation Postablation.
Circulation. Arrhythmia and electrophysiology
Feb 12, 2021
[Figure: see text].
Atrial Function, Left
Fractals
Pulmonary Veins
Heart Atria
Heart Rate
Catheter Ablation
Recurrence
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted
Atrial Fibrillation
Tomography, X-Ray Computed
Female
Risk Factors
Male
Middle Aged
Risk Assessment
Treatment Outcome
Humans
Action Potentials
Machine Learning
Retrospective Studies
Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor.
Prenatal diagnosis
Feb 12, 2021
OBJECTIVE: To investigate the performance of the machine learning (ML) model in predicting small-for-gestational-age (SGA) at birth, using second-trimester data.
DeepACSON automated segmentation of white matter in 3D electron microscopy.
Communications biology
Feb 10, 2021
Tracing the entirety of ultrastructures in large three-dimensional electron microscopy (3D-EM) images of the brain tissue requires automated segmentation techniques. Current segmentation techniques use deep convolutional neural networks (DCNNs) and r...
Animals
Male
Imaging, Three-Dimensional
Rats
Artificial Intelligence
Reproducibility of Results
Microscopy, Electron
White Matter
Nerve Fibers, Myelinated
Predictive Value of Tests
Brain Injuries, Traumatic
Rats, Sprague-Dawley
Disease Models, Animal
Mitochondria
Image Interpretation, Computer-Assisted
Cell Nucleus
Intelligent Neutrosophic Diagnostic System for Cardiotocography Data.
Computational intelligence and neuroscience
Feb 10, 2021
Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. The ...
Deep learning models for the prediction of intraoperative hypotension.
British journal of anaesthesia
Feb 6, 2021
BACKGROUND: Intraoperative hypotension is associated with a risk of postoperative organ dysfunction. In this study, we aimed to present deep learning algorithms for real-time predictions 5, 10, and 15 min before a hypotensive event.
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