AI Medical Compendium Topic

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

Rats

Showing 71 to 80 of 578 articles

Clear Filters

RS-Net: An end-to-end deep learning framework for rodent skull stripping in multi-center brain MRI.

NeuroImage
Skull stripping is a crucial preprocessing step in magnetic resonance imaging (MRI), where experts manually create brain masks. This labor-intensive process heavily relies on the annotator's expertise, as automation faces challenges such as low tissu...

Synthesis of higher-B CEST Z-spectra from lower-B data via deep learning and singular value decomposition.

NMR in biomedicine
Chemical exchange saturation transfer (CEST) MRI at 3 T suffers from low specificity due to overlapping CEST effects from multiple metabolites, while higher field strengths (B) allow for better separation of Z-spectral "peaks," aiding signal interpre...

Multi-parametric MRI-based machine learning model for prediction of pathological grade of renal injury in a rat kidney cold ischemia-reperfusion injury model.

BMC medical imaging
BACKGROUND: Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function and negatively impact graft survival and function. There is a lack of an accurate and non-invasive t...

Identification of eupneic breathing using machine learning.

Journal of neurophysiology
The diaphragm muscle (DIAm) is the primary inspiratory muscle in mammals. In awake animals, considerable heterogeneity in the electromyographic (EMG) activity of the DIAm reflects varied ventilatory and nonventilatory behaviors. Experiments in awake ...

Accelerating multipool CEST MRI of Parkinson's disease using deep learning-based Z-spectral compressed sensing.

Magnetic resonance in medicine
PURPOSE: To develop a deep learning-based approach to reduce the scan time of multipool CEST MRI for Parkinson's disease (PD) while maintaining sufficient prediction accuracy.

Automated identification and segmentation of urine spots based on deep-learning.

PeerJ
Micturition serves an essential physiological function that allows the body to eliminate metabolic wastes and maintain water-electrolyte balance. The urine spot assay (VSA), as a simple and economical assay, has been widely used in the study of mictu...

Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation.

Nature communications
Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation. Conventional artificial neural networks (ANNs) h...

Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor.

ACS chemical neuroscience
Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here, we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on t...

Application of machine learning models for property prediction to targeted protein degraders.

Nature communications
Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the...

Artificial intelligence derived categorizations significantly improve HOMA IR/β indicators: Combating diabetes through cross-interacting drugs.

Computers in biology and medicine
Improvements in the homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of beta-cell function (HOMA-β) significantly reduce the risk of disabling diabetic pathies. Nanoparticle (AuNP-AgNP)-metformin are conce...