AIMC Topic: Machine Learning

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A real-world demonstration of machine learning generalizability in the detection of intracranial hemorrhage on head computerized tomography.

Scientific reports
Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, these are often evaluated under laboratory settings. The importance of rea...

How can artificial intelligence be used for peptidomics?

Expert review of proteomics
INTRODUCTION: Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful ...

Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data.

The journal of pain
Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. The primary aim of this review was to evaluate the effectiveness of ML algorithms for predicting pai...

Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients.

International journal of computer assisted radiology and surgery
PURPOSE: Body weight is a crucial parameter for patient-specific treatments, particularly in the context of proper drug dosage. Contactless weight estimation from visual sensor data constitutes a promising approach to overcome challenges arising in e...

CBDPS 1.0: A Python GUI Application for Machine Learning Models to Predict Bitter-Tasting Children's Oral Medicines.

Chemical & pharmaceutical bulletin
Bitter tastes are innately aversive and are thought to help protect animals from consuming poisons. Children are extremely sensitive to drug tastes, and their compliance is especially poor with bitter medicine. Therefore, judging whether a drug is bi...

A 3D-2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification.

Computational intelligence and neuroscience
As one of the fast evolution of remote sensing and spectral imagery techniques, hyperspectral image (HSI) classification has attracted considerable attention in various fields, including land survey, resource monitoring, and among others. Nonetheless...

Preface.

Biochimica et biophysica acta. Reviews on cancer

Strategies for feature extraction from structural brain imaging in lesion-deficit modelling.

Human brain mapping
High-dimensional modelling of post-stroke deficits from structural brain imaging is highly relevant to basic cognitive neuroscience and bears the potential to be translationally used to guide individual rehabilitation measures. One strategy to optimi...

Fast identification of off-target liabilities in early antibiotic discovery with Fourier-transform infrared spectroscopy.

Biotechnology and bioengineering
Structural modifications of known antibiotic scaffolds have kept the upper hand on resistance, but we are on the verge of not having antibiotics for many common infections. Mechanism-based discovery assays reveal novelty, exclude off-target liabiliti...

Machine Learning Approach to Analyze the Surface Properties of Biological Materials.

ACS biomaterials science & engineering
Similar to how CRISPR has revolutionized the field of molecular biology, machine learning may drastically boost research in the area of materials science. Machine learning is a fast-evolving method that allows for analyzing big data and unveiling cor...