AIMC Topic: Humans

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Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study.

Scientific reports
Medical images such as magnetic resonance (MR) imaging provide valuable information for cancer detection, diagnosis, and prognosis. In addition to the anatomical information these images provide, machine learning can identify texture features from th...

LncRNA-Disease Associations Prediction Using Bipartite Local Model With Nearest Profile-Based Association Inferring.

IEEE journal of biomedical and health informatics
There is much evidence that long non-coding RNA (lncRNA) is associated with many diseases. However, it is time-consuming and expensive to identify meaningful lncRNA-disease associations (LDAs) through medical or biological experiments. Therefore, inv...

Prediction of Immunohistochemistry of Suspected Thyroid Nodules by Use of Machine Learning-Based Radiomics.

AJR. American journal of roentgenology
The purpose of this study was to develop and validate a radiomics model for evaluating immunohistochemical characteristics in patients with suspected thyroid nodules. A total of 103 patients (training cohort-to-validation cohort ratio, ≈ 3:1) with ...

A machine learning-assisted decision-support model to better identify patients with prostate cancer requiring an extended pelvic lymph node dissection.

BJU international
OBJECTIVES: To develop a machine learning (ML)-assisted model to identify candidates for extended pelvic lymph node dissection (ePLND) in prostate cancer by integrating clinical, biopsy, and precisely defined magnetic resonance imaging (MRI) findings...

Multicategory individualized treatment regime using outcome weighted learning.

Biometrics
Individualized treatment regimes (ITRs) aim to recommend treatments based on patient-specific characteristics in order to maximize the expected clinical outcome. Outcome weighted learning approaches have been proposed for this optimization problem wi...

Development of accurate human head models for personalized electromagnetic dosimetry using deep learning.

NeuroImage
The development of personalized human head models from medical images has become an important topic in the electromagnetic dosimetry field, including the optimization of electrostimulation, safety assessments, etc. Human head models are commonly gene...

Bayesian additive regression trees and the General BART model.

Statistics in medicine
Bayesian additive regression trees (BART) is a flexible prediction model/machine learning approach that has gained widespread popularity in recent years. As BART becomes more mainstream, there is an increased need for a paper that walks readers throu...

Infrared (IR) thermography as a potential screening modality for carotid artery stenosis.

Computers in biology and medicine
In the present study, an infrared (IR) thermal camera was used to map the temperature of the target skin surface, and the resulting thermal image was evaluated for the presence of carotid artery stenosis (CAS). In the presence of stenosis in the caro...

Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM.

Sensors (Basel, Switzerland)
Human falls are the premier cause of fatal and nonfatal injuries among older adults. The health outcome of a fall event is largely dependent on rapid response and rescue of the fallen elder. Being able to provide an accurate and fast fall detection w...

OrgaQuant: Human Intestinal Organoid Localization and Quantification Using Deep Convolutional Neural Networks.

Scientific reports
Organoid cultures are proving to be powerful in vitro models that closely mimic the cellular constituents of their native tissue. Organoids are typically expanded and cultured in a 3D environment using either naturally derived or synthetic extracellu...