AIMC Topic: Machine Learning

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Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning.

Ultrasound in medicine & biology
Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell....

Key indicators of phase transition for clinical trials through machine learning.

Drug discovery today
A significant number of drugs fail during the clinical testing stage. To understand the attrition of drugs through the regulatory process, here we review and advance machine-learning (ML) and natural language-processing algorithms to investigate the ...

Endocannabinoid degradation inhibitors ameliorate neuronal and synaptic alterations following traumatic brain injury.

Journal of neurophysiology
Our previous work showed that lateral fluid percussion injury to the sensorimotor cortex (SMC) of anesthetized rats increased neuronal synaptic hyperexcitability in layer 5 (L5) neurons in ex vivo brain slices 10 days postinjury. Furthermore, endocan...

Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning.

Cell systems
Mechanistic models explicitly represent hypothesized biological knowledge. As such, they offer more generalizability than data-driven models. However, identifying model curation efforts that improve performance for mechanistic models is nontrivial. H...

Assessing stroke severity using electronic health record data: a machine learning approach.

BMC medical informatics and decision making
BACKGROUND: Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text in physician reports, structur...

Improving Low-contrast Detectability and Noise Texture Pattern for Computed Tomography Using Iterative Reconstruction Accelerated with Machine Learning Method: A Phantom Study.

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate the performance of iterative reconstruction (IR) and filtered back projection (FBP) images in terms of low-contrast detectability at different radiation doses, IR levels, and slice thickness using the mathematica...

Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization.

Heart rhythm
BACKGROUND: Diagnosing long QT syndrome (LQTS) remains challenging because of a considerable overlap in QT interval between patients with LQTS and healthy subjects. Characterizing T-wave morphology might improve LQTS diagnosis.

Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor.

Journal of healthcare engineering
Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human ...

Machine learning methods for optimal prediction of motor outcome in Parkinson's disease.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: It is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson's disease (PD). Thus, it is critical to improve prediction of outcome in PD patients.

Machine Learning in Fetal Cardiology: What to Expect.

Fetal diagnosis and therapy
In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approac...