Bioterrorism

Detection of transient neurotransmitter response using personalized neural networks.

Measurement of stimulus-induced dopamine release and other types of transient neurotransmitter respo...

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment.

PURPOSE: Advanced medical image analytics is increasingly used to predict clinical outcome in patien...

M Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging.

To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role ...

Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning.

This study aimed to investigate the predictive efficacy of positron emission tomography/computed tom...

Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant-Positive Non-Small Cell Lung Cancer.

IMPORTANCE: An end-to-end efficacy evaluation approach for identifying progression risk after epider...

Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases.

PURPOSE: The purpose of this study was to develop and validate a deep learning (DL)-based radiomics ...

Perioperative automation: Time to become artificial intelligence literate? Response to Br J Anaesth 2020; 125: 843-6.

Intractable airsickness associated with COVID-19: A case report.

A 29-yr-old male Melicopter co-pilot with high flight hours in air transport missions of the patient...

Artificial Intelligence Predicts Drug Response.

A new artificial intelligence-based predictive modeling framework called DrugCell could accurately p...

Reducing Response Time in Motor Imagery Using A Headband and Deep Learning.

Electroencephalography (EEG) signals to detect motor imagery have been used to help patients with lo...

Prediction of response to cardiac resynchronization therapy using a multi-feature learning method.

We hypothesized that a multiparametric evaluation, based on the combination of electrocardiographic ...

Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning.

Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in m...

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.

PURPOSE: Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are...

Deep learning for the prediction of treatment response in depression.

BACKGROUND: Mood disorders are characterized by heterogeneity in severity, symptoms and treatment re...

Leveraging machine learning for predicting human body model response in restraint design simulations.

The objective of this study was to leverage and compare multiple machine learning techniques for pre...

Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction.

BACKGROUND: Protein kinases are a large family of druggable proteins that are genomically and proteo...

Artificial Intelligence for Response Evaluation With PET/CT.

Positron emission tomography (PET)/computed tomography (CT) are nuclear diagnostic imaging modalitie...

Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still's Disease: A Machine Learning Study.

Adult-onset Still's disease (AOSD) is an autoinflammatory disease with multisystem involvement. Earl...

Ready for the robot? A cross-sectional survey of OB/GYN fellowship directors' experience and expectations of their incoming fellow's robotic surgical skills.

To describe OB/GYN fellowship directors' (FDs) observations, expectations, and preferences of incomi...

The COVID-19 pandemic: How predictive analysis, artificial intelligence and GIS can be integrated into a clinical command system to improve disaster response and preparedness.

Single-Cell Techniques and Deep Learning in Predicting Drug Response.

Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomi...