Neurology

Head Trauma

Latest AI and machine learning research in head trauma for healthcare professionals.

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Estimation of current and post-treatment retinal function in chronic central serous chorioretinopathy using artificial intelligence.

Refined understanding of the association of retinal microstructure with current and future (post-tre...

Feasibility and tolerance of a robotic postural training to improve standing in a person with ambulatory spinal cord injury.

An ambulatory elder with SCI, AIS C, balance deficits, and right ankle-foot-orthosis participated. R...

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.

PURPOSE: Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cogn...

Artificial neural network and decision tree models of post-stroke depression at 3 months after stroke in patients with BMI ≥ 24.

OBJECTIVE: Previous studies have shown that excess weight (including obesity and overweight) can inc...

Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology.

Artificial intelligence (AI) is a branch of computer science with a variety of subfields and techniq...

Pre-surgical and Post-surgical Aortic Aneurysm Maximum Diameter Measurement: Full Automation by Artificial Intelligence.

OBJECTIVE: The aim of this study was to evaluate an automatic, deep learning based method (Augmented...

Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.

Gadolinium-enhancing lesions reflect active disease and are critical for in-patient monitoring in mu...

Predicting post-operative right ventricular failure using video-based deep learning.

Despite progressive improvements over the decades, the rich temporally resolved data in an echocardi...

Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.

Efficient, reliable and reproducible target volume delineation is a key step in the effective planni...

Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions.

Muscle fat infiltration (MFI) has been widely reported across cervical spine disorders. The quantifi...

A geometry-guided deep learning technique for CBCT reconstruction.

Although deep learning (DL) technique has been successfully used for computed tomography (CT) recons...

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.

Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health p...

Anomaly Detection in Videos Using Two-Stream Autoencoder with Post Hoc Interpretability.

The growing interest in deep learning approaches to video surveillance raises concerns about the acc...

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing.

Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the...

The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions.

In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to f...

Deep Learning-Based Magnetic Resonance Imaging Image Features for Diagnosis of Anterior Cruciate Ligament Injury.

To study and explore the adoption value of magnetic resonance imaging (MRI) in the diagnosis of ante...

Optimization and Simulation of Enterprise Management Resource Scheduling Based on the Radial Basis Function (RBF) Neural Network.

In the human resource system of modern enterprises, human-post matching big data occupies an importa...

Application of fuzzy neural network model and current-voltage analysis of biologically active points for prediction post-surgery risks.

The work investigates neural network model for prediction of post-surgical treatment risks. The desc...

Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging.

INTRODUCTION: To develop an image-based deep learning model for predicting pathological response in ...

A Gastrointestinal Endoscopy Quality Control System Incorporated With Deep Learning Improved Endoscopist Performance in a Pretest and Post-Test Trial.

INTRODUCTION: Gastrointestinal endoscopic quality is operator-dependent. To ensure the endoscopy qua...

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