AIMC Topic: Hematoma

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Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine.

EBioMedicine May 3, 2019
BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality rate. This study aimed to predict hematoma expansion in spontaneous ICH from routinely available variables by using support vector machine (SVM) metho...
Image Processing, Computer-Assisted Support Vector Machine Cerebral Hemorrhage Models, Biological Hematoma Odds Ratio Glasgow Coma Scale Sensitivity and Specificity Retrospective Studies Aged, 80 and over Male Young Adult Tomography, X-Ray Computed Middle Aged Adult Female ROC Curve Humans Aged Biomarkers Prognosis
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Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types.

European radiology Oct 10, 2018
OBJECTIVE: To investigate the classification ability of quantitative radiomics features extracted on non-contrast-enhanced CT (NECT) image for discrimination of AVM-related hematomas from those caused by other etiologies.
Adolescent Child Hematoma Brain Diseases Machine Learning Adult Image Enhancement Humans Tomography, X-Ray Computed Reproducibility of Results Male Female Aged Middle Aged Aged, 80 and over Young Adult
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Deep learning-based clinical decision support system for intracerebral hemorrhage: an imaging-based AI-driven framework for automated hematoma segmentation and trajectory planning.

Neurosurgical focus Jul 1, 2025
OBJECTIVE: Intracerebral hemorrhage (ICH) remains a critical neurosurgical emergency with high mortality and long-term disability. Despite advancements in minimally invasive techniques, procedural precision remains limited by hematoma complexity and ...
Humans Retrospective Studies Tomography, X-Ray Computed Female Cohort Studies Decision Support Systems, Clinical Cerebral Hemorrhage Hematoma Aged Male Middle Aged Deep Learning
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Development of machine learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma.

Medicine Nov 1, 2024
The aim of this study was to develop a machine-learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma. We included patients who underwent craniotomy and evacuation of hematoma due to traumatic brain inj...
Hematoma Craniotomy Craniocerebral Trauma Acute Kidney Injury Female Aged Adult Humans Machine Learning Male Retrospective Studies Middle Aged Postoperative Complications ROC Curve
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Research on predicting hematoma expansion in spontaneous intracerebral hemorrhage based on deep features of the VGG-19 network.

Postgraduate medical journal Jul 18, 2024
PURPOSE: To construct a clinical noncontrastive computed tomography (NCCT) deep learning joint model for predicting early hematoma expansion (HE) after cerebral hemorrhage (sICH) and evaluate its predictive performance.
Humans Middle Aged Disease Progression Deep Learning Logistic Models Tomography, X-Ray Computed Female Aged Male Hematoma Cerebral Hemorrhage Predictive Value of Tests Bayes Theorem
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An Early Warning System Using Machine Learning for the Detection of Intracranial Hematomas in the Emergency Trauma Setting.

Turkish neurosurgery Jan 1, 2022
AIM: To present an early warning system (EWS) that employs a supervised machine learning algorithm for the rapid detection of extra-axial hematomas (EAHs) in an emergency trauma setting.
Algorithms Tomography, X-Ray Computed Hematoma Humans Machine Learning
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Robot-Assisted Instrumented Fusion of a T8-9 Extension Distraction Fracture and Epidural Hematoma Evacuation: 2-Dimensional Operative Video.

Operative neurosurgery (Hagerstown, Md.) Sep 15, 2020
The utilization of robotics has been gaining increased popularity in spine surgery. It can be used to assist in pedicle screw insertion when anatomy is complex in deformity surgery, but is also helpful in degenerative spine as it can minimize tissue ...
Pedicle Screws Humans Male Middle Aged Robotics Fluoroscopy Spinal Fusion Hematoma
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Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support.

The journal of trauma and acute care surgery Mar 1, 2020
INTRODUCTION: Admission computed tomography (CT) is a widely used diagnostic tool for patients with pelvic fractures. In this pilot study, we hypothesized that pelvic hematoma volumes derived using a rapid automated deep learning-based quantitative v...
Embolization, Therapeutic Endotamponade Deep Learning Female Pelvic Bones Pilot Projects Adult Humans Fractures, Bone Hospital Mortality Algorithms Male Blood Transfusion Decision Support Systems, Clinical Retrospective Studies Middle Aged Hematoma
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Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT.

Journal of digital imaging Feb 1, 2020
The volume of pelvic hematoma at CT has been shown to be the strongest independent predictor of major arterial injury requiring angioembolization in trauma victims with pelvic fractures, and also correlates with transfusion requirement and mortality....
Tomography, X-Ray Computed Deep Learning Pelvis Hematoma Humans
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Hematoma Segmentation Using Dilated Convolutional Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Traumatic brain injury (TBI) is a global health challenge. Accurate and fast automatic detection of hematoma in the brain is essential for TBI diagnosis and treatment. In this study, we developed a fully automated system to detect and segment hematom...
Sensitivity and Specificity Image Processing, Computer-Assisted Humans Hematoma Neural Networks, Computer Tomography, X-Ray Computed
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