Head Trauma

Functional Gait Assessment Using Manual, Semi-Automated and Deep Learning Approaches Following Standardized Models of Peripheral Nerve Injury in Mice.

Objective: To develop a standardized model of stretch−crush sciatic nerve injury in mice, and to compare outcomes of crush and novel stretch−crush injuries using standard manual gait and sensory assays, and compare them to both semi-automated as well as deep-learning gait analysis methods. Methods: Initial studies in C57/Bl6 mice were used to develop crush and stretch−crush injury models followed ...

Development and External Validation of a Machine Learning Model for the Early Prediction of Doses of Harmful Intracranial Pressure in Patients with Severe Traumatic Brain Injury.

Treatment and prevention of elevated intracranial pressure (ICP) is crucial in patients with severe traumatic brain injury (TBI). Elevated ICP is associated with secondary brain injury, and both intensity and duration of an episode of intracranial hypertension, often referred to as "ICP dose," are associated with worse outcomes. Prediction of such harmful episodes of ICP dose could allow for a mor...

Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images.

The detection of maxillary sinus wall is important in dental fields such as implant surgery, tooth extraction, and odontogenic disease diagnosis. The ...

An interpretable neural network for outcome prediction in traumatic brain injury.

BACKGROUND: Traumatic Brain Injury (TBI) is a common condition with potentially severe long-term complications, the prediction of which remains challe...

A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation.

AMPylation is an emerging post-translational modification that occurs on the hydroxyl group of threonine, serine, or tyrosine via a phosphodiester bon...

Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mapping.

Stroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that...

Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning.

This study evaluates the accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning. We developed a dynamic ...

Robot-Assisted Laparoscopic Calyceo-Pyelostomy for Vascular Compression of the Upper Calyx (Fraley Syndrome).

Fraley's Syndrome is a rare anatomic vascular malformation described in 1966 where an aberrant crossing vessel compresses the upper infundibulum and l...

Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists.

OBJECTIVES: Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-ba...

Patient Benefit and Quality of Life after Robot-Assisted Head and Neck Surgery.

Robotic systems for head and neck surgery are at different stages of technical development and clinical application. Currently, robotic systems are pr...

Identification of hand-foot syndrome from cancer patients' blog posts: BERT-based deep-learning approach to detect potential adverse drug reaction symptoms.

Early detection and management of adverse drug reactions (ADRs) is crucial for improving patients' quality of life. Hand-foot syndrome (HFS) is one of...

Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans.

Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict o...

Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using 3D deep learning model.

The most common cause of long-term disability and death in young adults is a traumatic brain injury. The decision for surgical intervention for cranio...

Scalable deep learning algorithm to compute percent pulmonary contusion among patients with rib fractures.

BACKGROUND: Pulmonary contusion exists along a spectrum of severity, yet is commonly binarily classified as present or absent. We aimed to develop a d...

Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement.

Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods d...

Splenic artery steal syndrome in patients with orthotopic liver transplant: Where to embolize the splenic artery?

PURPOSE: This study compared proximal and distal embolization of the splenic artery (SA) in patients with splenic artery steal syndrome (SAS) after or...

Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting.

OBJECTIVE: Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient progn...

Three-dimensional deep learning to automatically generate cranial implant geometry.

We present a 3D deep learning framework that can generate a complete cranial model using a defective one. The Boolean subtraction between these two mo...

Comparative Study of Postoperative Pain Relief Using Preincisional Versus Postincisional Infiltration With 0.25% Bupivacaine in Abdominal Hysterectomy Under General Anesthesia.

BACKGROUND AND AIMS: Hysterectomy, probably the most common non-pregnancy related surgery performed in gynaecology, is associated with moderate to sev...

Deep Learning Post-Filtering Using Multi-Head Attention and Multiresolution Feature Fusion for Image and Intra-Video Quality Enhancement.

The paper proposes a novel post-filtering method based on convolutional neural networks (CNNs) for quality enhancement of RGB/grayscale images and vid...