AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Calibration

Showing 151 to 160 of 344 articles

Clear Filters

Deep learning extended depth-of-field microscope for fast and slide-free histology.

Proceedings of the National Academy of Sciences of the United States of America
Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into t...

Challenging handheld NIR spectrometers with moisture analysis in plant matrices: Performance of PLSR vs. GPR vs. ANN modelling.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The global demand for natural products grows rapidly, intensifying the request for the development of high-throughput, fast, non-invasive tools for quality control applicable on-site. Moisture content is one of the most important quality parameters o...

Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches.

Scientific reports
Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding high risk late intubation. This study evaluates whether machine learning can predict the need for intubation within...

Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation.

IEEE transactions on medical imaging
Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Moreover, batch normalization and Dice loss have been used successfully t...

Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.

Sensors (Basel, Switzerland)
The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the s...

Convolutional neural network based proton stopping-power-ratio estimation with dual-energy CT: a feasibility study.

Physics in medicine and biology
Dual-energy computed tomography (DECT) has shown a great potential for lowering range uncertainties, which is necessary for truly leveraging the Bragg peak in proton therapy. However, analytical stopping-power-ratio (SPR) estimation methods have limi...

Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification.

IEEE transactions on medical imaging
In this project, our goal is to develop a method for interpreting how a neural network makes layer-by-layer embedded decisions when trained for a classification task, and also to use this insight for improving the model performance. To do this, we fi...

Predicting population health with machine learning: a scoping review.

BMJ open
OBJECTIVE: To determine how machine learning has been applied to prediction applications in population health contexts. Specifically, to describe which outcomes have been studied, the data sources most widely used and whether reporting of machine lea...

A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra.

Journal of dairy science
Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an inter...