Neurology

Head Trauma

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

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A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.

OBJECTIVE: Neurological deterioration after mild traumatic brain injury (TBI) has been recognized as...

Current trends in pharmaceutical industry: Post -CoVid-19 pandemic effects.

The pharmaceutical industry is undergoing a period of significant transformation. This is driven by ...

Prediction of prognosis in patients with cerebral contusions based on machine learning.

Traumatic brain injury (TBI) is a global issue and a major cause of patient mortality, and cerebral ...

Advancing brain tumor detection and classification in Low-Dose CT images using the innovative multi-layered deep neural network model.

BackgroundEffective brain tumour therapy and better patient outcomes depend on early tumour diagnosi...

A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction.

The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epilept...

Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma.

We employed a mechanistic learning approach, integrating on-treatment tumor kinetics (TK) modeling w...

Optimizing autonomous artificial intelligence diagnostics for neuro-ocular health in space missions.

Spaceflight-Associated Neuro-Ocular Syndrome (SANS) presents a critical risk in long-duration missio...

Predicting the infecting dengue serotype from antibody titre data using machine learning.

The development of a safe and efficacious vaccine that provides immunity against all four dengue vir...

FPANet: Frequency-based video demoiréing using frame-level post alignment.

Moiré patterns, created by the interference between overlapping grid patterns in the pixel space, de...

Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a member of the large coronavirus fa...

Using artificial intelligence to predict post-operative outcomes in congenital heart surgeries: a systematic review.

INTRODUCTION: Congenital heart disease (CHD) represents the most common group of congenital anomalie...

Reliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function.

OBJECTIVE: The total examination time can be reduced if high-quality two-dimensional (2D) cine image...

Predicting Early recurrence of atrial fibrilation post-catheter ablation using machine learning techniques.

BACKGROUND: Catheter ablation is a common treatment for atrial fibrillation (AF), but recurrence rat...

Machine learning for predicting post-operative outcomes in meningiomas: a systematic review and meta-analysis.

PURPOSE: Meningiomas are the most common primary brain tumour and account for over one-third of case...

Machine Learning-Based Model for Prediction of Post-Stroke Cognitive Impairment in Acute Ischemic Stroke: A Cross-Sectional Study.

BACKGROUND AND OBJECTIVE: Early identification of post-stroke cognitive impairment (PSCI) is an impo...

Chemical-functional characterization of and and dietary supplementation in post-weaning pigs.

INTRODUCTION: As the livestock industry grapples with the need for sustainable land, maintaining pro...

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