AIMC Topic: Male

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Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images.

Scientific reports
Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classificat...

An Evaluation of the Effect of Vision on Standing Stability in the Early Stage of Parkinson's Disease.

European neurology
BACKGROUND: Postural instability is a common disorder in Parkinson's disease (PD). The aim of this study was to evaluate stance stability of the subjects at the early stage of PD with both linear and nonlinear approaches. Moreover, this study aimed t...

Predicting centre of mass horizontal speed in low to severe swimming intensities with linear and non-linear models.

Journal of sports sciences
We aimed to compare multilayer perceptron (MLP) neural networks, radial basis function neural networks (RBF) and linear models (LM) accuracy to predict the centre of mass (CM) horizontal speed at low-moderate, heavy and severe swimming intensities us...

An integrated molecular diagnostic report for heart transplant biopsies using an ensemble of diagnostic algorithms.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation
BACKGROUND: We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. In the present study we ex...

The use of artificial neural network analysis can improve the risk-stratification of patients presenting with suspected deep vein thrombosis.

British journal of haematology
Artificial neural networks are machine-learning algorithms designed to analyse data without a pre-existing hypothesis as to any associations that may exist. This technique has not previously been applied to the risk stratification of patients referre...

Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study.

JMIR mHealth and uHealth
BACKGROUND: Wearable accelerometers have greatly improved measurement of physical activity, and the increasing popularity of smartwatches with inherent acceleration data collection suggest their potential use in the physical activity research domain;...

Predicting inadequate postoperative pain management in depressed patients: A machine learning approach.

PloS one
Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity...

Prediction of IDH1 Mutation Status in Glioblastoma Using Machine Learning Technique Based on Quantitative Radiomic Data.

World neurosurgery
OBJECTIVE: Isocitrate dehydrogenase 1 (IDH1) mutation status is an independent favorable prognostic factor for glioblastoma (GBM) and is usually determined by sequencing or immunohistochemistry. An accurate prediction of IDH1 mutation status via noni...

Sprint Assessment Using Machine Learning and a Wearable Accelerometer.

Journal of applied biomechanics
Field-based sprint performance assessments rely on metrics derived from a simple model of sprinting dynamics parameterized by 2 constants, v and τ, which indicate a sprinter's maximal theoretical velocity and the time it takes to approach v, respecti...

Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity.

PloS one
Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and languages to quickly and reliably convey emotional information. Historically, facial expression researc...