AIMC Topic: Machine Learning

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Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study.

European radiology experimental
BACKGROUND: Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise A...

Predicting atrial fibrillation in primary care using machine learning.

PloS one
BACKGROUND: Atrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of t...

Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study.

Journal of the American Academy of Dermatology
BACKGROUND: Psoriasis is associated with elevated risk of heart attack and increased accumulation of subclinical noncalcified coronary burden by coronary computed tomography angiography (CCTA). Machine learning algorithms have been shown to effective...

Systematic review of machine learning for diagnosis and prognosis in dermatology.

The Journal of dermatological treatment
Software systems using artificial intelligence for medical purposes have been developed in recent years. The success of deep neural networks (DNN) in 2012 in the image recognition challenge ImageNet LSVRC 2010 fueled expectations of the potential fo...

Machine Learning to Predict Acute Kidney Injury.

American journal of kidney diseases : the official journal of the National Kidney Foundation

Synthesizing diffusion tensor imaging from functional MRI using fully convolutional networks.

Computers in biology and medicine
PURPOSE: Medical image synthesis can simulate a target modality of interest based on existing modalities and has the potential to save scanning time while contributing to efficient data collection. This study proposed a three-dimensional (3D) deep le...

Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions.

Journal of chemical information and modeling
Structure-based drug design is critically dependent on accuracy of molecular docking scoring functions, and there is of significant interest to advance scoring functions with machine learning approaches. In this work, by judiciously expanding the tra...

Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification.

BioMed research international
The neuromuscular disorders are diagnosed using electromyographic (EMG) signals. Machine learning algorithms are employed as a decision support system to diagnose neuromuscular disorders. This paper compares bagging and boosting ensemble learning met...

Election forensics: Using machine learning and synthetic data for possible election anomaly detection.

PloS one
Assuring election integrity is essential for the legitimacy of elected representative democratic government. Until recently, other than in-person election observation, there have been few quantitative methods for determining the integrity of a democr...