Analysis of Cerebral CT Based on Supervised Machine Learning as a Predictor of Outcome After Out-of-Hospital Cardiac Arrest

Information Science, Artificial Intelligence, Medicine, Emergency Medicine, Neurology, Medical Imaging,
cerebral CT   machine learning   out-of-hospital cardiac arrest   prognosis   gray matter   neurological injury  

Brain CT Analysis as a Tool for Outcome Prediction after Out-of-Hospital Cardiac Arrest: A Supervised Machine Learning Analysis Research Background Out-of-Hospital Cardiac Arrest (OHCA) is one of the leading causes of death in the Western world, with extremely low survival rates, ranging from 3% to 16%. The neurological and overall outcomes after O...

Radiomics-based Prediction of Local Control in Patients with Brain Metastases Following Postoperative Stereotactic Radiotherapy

Information Science, Artificial Intelligence, Medicine, Neurology, Neurosurgery, Medical Imaging, Oncology,
radiomics   machine learning   local failure prediction   brain metastases   artificial intelligence  

Application of Radiomics in Predicting Local Control in Postoperative Stereotactic Radiotherapy for Brain Metastasis Patients Academic Background Brain Metastases (BMs) are the most common malignant brain tumors, far surpassing primary brain tumors like gliomas in incidence. Recent medical guidelines recommend surgical treatment for patients with s...

Raman-Based Machine Learning Platform Reveals Unique Metabolic Differences Between IDHmut and IDHwt Glioma

Information Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology, Medical Imaging, Oncology, Life Sciences, Cell Biology, Biochemistry,
Raman spectroscopy   glioma   machine learning   FFPE tissue   lipid metabolism  

Study on Metabolic Differences between IDH Mutant and Wild-type Glioma Cells Using Raman Spectroscopy and Machine Learning Platform Background Introduction In the diagnosis and treatment of gliomas, formalin-fixed, paraffin-embedded (FFPE) tissue sections are commonly used. However, due to background noise interference from the embedding medium, th...

A Novel CNN-Based Image Segmentation Pipeline for Individualized Feline Spinal Cord Stimulation Modeling

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Bioengineering,
spinal cord stimulation   machine learning   automatic segmentation   axonal recruitment   convolutional neural network   artificial intelligence  

Automated Spinal Cord Segmentation Pipeline Based on Convolutional Neural Network (CNN) for Individualized Cat Spinal Cord Stimulation Modeling Background and Research Motivation Spinal cord stimulation (SCS) is a widely used treatment method for chronic pain management. In recent years, it has also been used to modulate neural activity, aiming to ...

Accelerating Ionizable Lipid Discovery for mRNA Delivery Using Machine Learning and Combinatorial Chemistry

Chemistry, Computational Chemistry, Information Science, Artificial Intelligence, Life Sciences, Biochemistry, Bioengineering,
mRNA delivery   machine learning   ionizable lipids   combinatorial chemistry   lipid nanoparticles  

Accelerating the Discovery of Ionizable Lipids for mRNA Delivery using Machine Learning and Combinatorial Chemistry Research Background To fully realize the potential of mRNA therapies, it is essential to expand the toolkit of lipid nanoparticles (LNPs). However, a key bottleneck in LNP development is identifying new ionizable lipids. Previous stud...

Sequential Safe Static and Dynamic Screening Rule for Accelerating Support Tensor Machine

Mathematics, Information Science, Computer Science, Artificial Intelligence,
support tensor machine   safe screening rule   sparse learning   machine learning   high-dimensional data  

With the continuous advancement of data acquisition technology, obtaining large amounts of high-dimensional data containing multiple features has become very easy, such as images and vision data. However, traditional machine learning methods, especially those based on vectors and matrices, face challenges such as the curse of dimensionality, increa...

Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots

Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots

Engineering, Transportation Engineering, Information Science, Automation, Artificial Intelligence,
autonomous navigation   wheeled-legged robots   autonomous logistics   urban environments   machine learning   deep reinforcement learning  

Autonomous Navigation and Walking Wheel-Leg Robot Background Introduction The acceleration of urbanization has posed significant challenges for supply chain logistics, especially for last-mile delivery. As traffic pressure increases and the demand for faster delivery services rises, particularly with complex routes indoors and on city streets, trad...

Comprehensive Peripheral Blood Immunoprofiling Reveals Five Immunotypes with Immunotherapy Response Characteristics in Patients with Cancer

Medicine, Rheumatology and Immunology, Basic Medical Sciences, Oncology, Life Sciences, Immunology,
peripheral blood   immunoprofiling   immunotherapy   cancer   machine learning  

Research Report on the Analysis of Immunological Characteristics of Peripheral Blood in Cancer Patients Cancer is a major and pervasive health problem globally. Despite significant advances in cancer treatment in recent years, many challenges remain, including how to accurately predict patients’ responses to various treatments. Immunotherapy, parti...

Quantification and Diagnosis of Mobility Deficits

Information Science, Artificial Intelligence, Medicine, Neurology, Life Sciences, Biophysics,
mobility deficits   Parkinson's disease   wearable sensors   machine learning   motion tracking  

Background and Research Motivation Parkinson’s Disease (PD) is a neurodegenerative disorder primarily affecting patients’ motor abilities, leading to tremors, bradykinesia, limb rigidity, and problems with gait and balance. These motor deficits significantly impact patients’ ability to live independently and their quality of life. Statistics predic...

Strokeclassifier: Ischemic Stroke Etiology Classification by Ensemble Consensus Modeling Using Electronic Health Records

Information Science, Artificial Intelligence, Medicine, Environmental and Public Health, Neurology,
stroke   machine learning   artificial intelligence   etiology classification   health records   diagnostic accuracy   secondary prevention  

StrokeClassifier: An AI Tool for Etiological Classification of Ischemic Stroke Based on Electronic Health Records Project Background and Motivation Identifying the etiology of strokes, particularly acute ischemic stroke (AIS), is crucial for secondary prevention, but it is often very challenging. In the United States, there are nearly 676,000 new c...