Head Trauma

Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data.

Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. However, interpreting the biological implications of these associations remains a challenge. This review highlights 2 promising post-GWAS methods for PGx. First, we discuss the polygenic architecture of the PGx traits, especially for drug-induced liver ...

Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence.

Three-quarters of lifetime mental illness occurs by the age of 24, but relatively little is known about how to robustly identify youth at risk to target intervention efforts known to improve outcomes. Barriers to knowledge have included obtaining robust predictions while simultaneously analyzing large numbers of different types of candidate predictors. In a new, large, transdiagnostic youth sample...

Multi-omics integration strategy in the post-mortem interval of forensic science.

Estimates of post-mortem interval (PMI), which often serve as pivotal evidence in forensic contexts, are fundamentally based on assessments of variabi...

Development of an automated estimation of foot process width using deep learning in kidney biopsies from patients with Fabry, minimal change, and diabetic kidney diseases.

Podocyte injury plays a key role in pathogenesis of many kidney diseases with increased podocyte foot process width (FPW), an important measure of pod...

Systematic review of machine learning in PTSD studies for automated diagnosis evaluation.

Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barri...

Mortality prediction using medical time series on TBI patients.

BACKGROUND AND OBJECTIVE: Traumatic Brain Injury (TBI) is one of the leading causes of injury-related mortality in the world, with severe cases reachi...

Comparison of post reconstruction- and reconstruction-based deep learning denoising methods in cardiac SPECT.

. The quality of myocardial perfusion SPECT (MPS) images is often hampered by low count statistics. Poor image quality might hinder reporting the stud...

Applicability of machine learning technique in the screening of patients with mild traumatic brain injury.

Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only...

Domain Agnostic Post-Processing for QRS Detection Using Recurrent Neural Network.

Deep-learning-based QRS-detection algorithms often require essential post-processing to refine the output prediction-stream for R-peak localisation. T...

Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture.

Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do n...

HCformer: Hybrid CNN-Transformer for LDCT Image Denoising.

Low-dose computed tomography (LDCT) is an effective way to reduce radiation exposure for patients. However, it will increase the noise of reconstructe...

Implementation of totally robotic right hemicolectomy: lessons learned from a prospective cohort.

Robotics facilitates the realization of intra-corporeal anastomosis during right hemicolectomy and allows extracting the operative specimen through a ...

A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage.

Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investigation and recognition by physicians. Computed tom...

Impact of Obstructive Sleep Apnea Syndrome on Time to Complete Recovery of Continence After Robot-Assisted Radical Prostatectomy: A Propensity Score Matching Analysis.

Postoperative urinary incontinence after robot-assisted radical prostatectomy (RARP) represents a bothersome side effect, which may permanently affec...

Pulmonary contusion: automated deep learning-based quantitative visualization.

PURPOSE: Rapid automated CT volumetry of pulmonary contusion may predict progression to Acute Respiratory Distress Syndrome (ARDS) and help guide earl...

Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement.

PURPOSE: This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast compu...

Identifying Barriers to Post-Acute Care Referral and Characterizing Negative Patient Preferences Among Hospitalized Older Adults Using Natural Language Processing.

Our objective was to detect common barriers to post-acute care (B2PAC) among hospitalized older adults using natural language processing (NLP) of clin...

An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury.

A longstanding challenge surrounding deep learning algorithms is unpacking and understanding how they make their decisions. Explainable Artificial Int...

Assessment of stress response due to C-Mac D-blade guided videolaryngoscopic endotracheal intubation and docking of da Vinci surgical robot using perfusion index in patients undergoing transoral robotic oncosurgery.

Clinical utility of perfusion index (PI) has entered a new realm as a non-invasive, quantitative index of stress response to endotracheal intubation. ...

Applications of artificial intelligence-machine learning for detection of stress: a critical overview.

Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immu...