AIMC Topic: Machine Learning

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Applications of Machine Learning in Bone and Mineral Research.

Endocrinology and metabolism (Seoul, Korea)
In this unprecedented era of the overwhelming volume of medical data, machine learning can be a promising tool that may shed light on an individualized approach and a better understanding of the disease in the field of osteoporosis research, similar ...

Interpretable machine learning for genomics.

Human genetics
High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced sta...

Elucidating factors influencing machine learning algorithm prediction in spasticity assessment: a prospective observational study.

Computer methods in biomechanics and biomedical engineering
The Machine Learning Model (MLM) has garnered popularity in rehabilitation, ranging from developing algorithms in outcome prediction, prognostication, and training artificial intelligence. High-quality data plays a critical role in algorithm developm...

Workflow for automatic renal perfusion quantification using ASL-MRI and machine learning.

Magnetic resonance in medicine
PURPOSE: Clinical applicability of renal arterial spin labeling (ASL) MRI is hampered because of time consuming and observer dependent post-processing, including manual segmentation of the cortex to obtain cortical renal blood flow (RBF). Machine lea...

Brain functional and effective connectivity based on electroencephalography recordings: A review.

Human brain mapping
Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional...

Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists' decisions of deep myometrial invasion.

Magnetic resonance imaging
PURPOSE: To evaluate radiomic machine learning (ML) classifiers based on multiparametric magnetic resonance images (MRI) in pretreatment assessment of endometrial cancer (EC) risk factors and to examine effects on radiologists' interpretation of deep...

Prediction of dMRI signals with neural architecture search.

Journal of neuroscience methods
BACKGROUND: There is growing interest in the neuroscience community in estimating and mapping microscopic properties of brain tissue non-invasively using magnetic resonance measurements. Machine learning methods are actively investigated to predict t...

Prediction of Micronucleus Assay Outcome Using In Vivo Activity Data and Molecular Structure Features.

Applied biochemistry and biotechnology
In vivo micronucleus assay is the widely used genotoxic test to determine the extent of chromosomal aberrations caused by the chemicals in human beings, which plays a significant role in the drug discovery paradigm. To reduce the uncertainties of the...

Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters.

Sensors (Basel, Switzerland)
Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering. At present, manual identific...

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning.

Sensors (Basel, Switzerland)
The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation...