Head Trauma

Evaluation of Machine Learning Techniques to Predict the Likelihood of Mental Health Conditions Following a First mTBI.

OBJECTIVE: Limited research has evaluated the utility of machine learning models and longitudinal data from electronic health records (EHR) to forecast mental health outcomes following a traumatic brain injury (TBI). The objective of this study is to assess various data science and machine learning techniques and determine their efficacy in forecasting mental health (MH) conditions among active du...

Detecting Presence of PTSD Using Sentiment Analysis From Text Data.

Rates of Post-traumatic stress disorder (PTSD) have risen significantly due to the COVID-19 pandemic. Telehealth has emerged as a means to monitor symptoms for such disorders. This is partly due to isolation or inaccessibility of therapeutic intervention caused from the pandemic. Additional screening tools may be needed to augment identification and diagnosis of PTSD through a virtual medium. Sent...

Exploring the use of machine learning for the assessment of skeletal fracture morphology and differentiation between impact mechanisms: A pilot study.

Analyzing and interpreting traumatic injuries is a fundamental aspect of routine forensic case work. As the human skeleton can be impacted through a c...

Developing basic robotic skills using virtual reality simulation and automated assessment tools: a multidisciplinary robotic virtual reality-based curriculum using the Da Vinci Skills Simulator and tracking progress with the Intuitive Learning platform.

This study sought to develop basic robotic surgical skills among surgical trainees across multiple specialties using a VR-based curriculum and provide...

Nerve spare robot assisted laparoscopic prostatectomy with amniotic membranes: medium term outcomes.

dHACM is a source of factors including cytokines that allow anti-inflammatory and proliferative elements to be utilized for wound and ulcer management...

Effects of two different doses of 3% hypertonic saline with mannitol during decompressive craniectomy following traumatic brain injury: A prospective, controlled study.

BACKGROUND AND AIMS: The current study was designed to compare the effects of two different doses of 3% hypertonic saline with mannitol on intraoperat...

Predicting surgical outcomes for chronic exertional compartment syndrome using a machine learning framework with embedded trust by interrogation strategies.

Chronic exertional compartment syndrome (CECS) is a condition occurring most frequently in the lower limbs and often requires corrective surgery to al...

Elevated Levels of Urinary Biomarkers TIMP-2 and IGFBP-7 Predict Acute Kidney Injury in Neonates after Congenital Heart Surgery.

 This article investigated the utility of urine biomarkers tissue inhibitor of metalloproteinase-2 (TIMP-2) and insulin-like growth factor binding pr...

The effect of a post-scan processing denoising system on image quality and morphometric analysis.

PURPOSE: MR image quality and subsequent brain morphometric analysis are inevitably affected by noise. The purpose of this study was to evaluate the e...

Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus.

As low-field MRI technology is being disseminated into clinical settings around the world, it is important to assess the image quality required to pro...

Unraveling somatotopic organization in the human brain using machine learning and adaptive supervoxel-based parcellations.

In addition to the well-established somatotopy in the pre- and post-central gyrus, there is now strong evidence that somatotopic organization is evide...

A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning.

This paper proposes a post-processing method called bidirectional interpolation method for sampling-based path planning algorithms, such as rapidly-ex...

Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies.

Interstitial fibrosis, tubular atrophy, and inflammation are major contributors to kidney allograft failure. Here we sought an objective, quantitative...

Automated post-operative brain tumour segmentation: A deep learning model based on transfer learning from pre-operative images.

Automated brain tumour segmentation from post-operative images is a clinically relevant yet challenging problem. In this study, an automated method fo...

Deceptive Online Content Detection Using Only Message Characteristics and a Machine Learning Trained Expert System.

This paper considers the use of a post metadata-based approach to identifying intentionally deceptive online content. It presents the use of an inhere...

Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification.

OBJECTIVES: Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that...

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-treatment) function in chronic central serous chorior...

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 techniques, exploited to serve as a deductive tool that p...

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 multiple sclerosis (MS). In this work, we have devel...

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

Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments redu...