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Hematoma, Subdural

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A natural language processing algorithm to extract characteristics of subdural hematoma from head CT reports.

Emergency radiology
PURPOSE: Subdural hematoma (SDH) is the most common form of traumatic intracranial hemorrhage, and radiographic characteristics of SDH are predictive of complications and patient outcomes. We created a natural language processing (NLP) algorithm to e...

Evaluation of Traumatic Subdural Hematoma Volume by Using Image Segmentation Assessment Based on Deep Learning.

Computational and mathematical methods in medicine
Rapid and accurate evaluations of hematoma volume can guide the treatment of traumatic subdural hematoma. We aim to explore the consistency between the measurement results of traumatic subdural hematoma (TSDH) using a deep learn-based image segmentat...

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

Computers in biology and medicine
The most common cause of long-term disability and death in young adults is a traumatic brain injury. The decision for surgical intervention for craniotomy is dependent on the injury type and the patient's neurologic exam. The potential subtypes of in...

Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis.

Journal of neurointerventional surgery
BACKGROUND: This study aimed to investigate the application of deep learning (DL) models for the detection of subdural hematoma (SDH).

How artificial intelligence can provide information about subdural hematoma: Assessment of readability, reliability, and quality of ChatGPT, BARD, and perplexity responses.

Medicine
Subdural hematoma is defined as blood collection in the subdural space between the dura mater and arachnoid. Subdural hematoma is a condition that neurosurgeons frequently encounter and has acute, subacute and chronic forms. The incidence in adults i...

Intelligent Recognition and Segmentation of Blunt Craniocerebral Injury CT Images Based on DeepLabV3+ Model.

Fa yi xue za zhi
OBJECTIVES: To achieve intelligent recognition and segmentation of common craniocerebral injuries (hereinafter referred to as "segmentation") by training convolutional neural network DeepLabV3+ model based on CT images of blunt craniocerebral injury ...

Deep learning segmentation-based bone removal from computed tomography of the brain improves subdural hematoma detection.

Journal of neuroradiology = Journal de neuroradiologie
PURPOSE: Timely identification of intracranial blood products is clinically impactful, however the detection of subdural hematoma (SDH) on non-contrast CT scans of the head (NCCTH) is challenging given interference from the adjacent calvarium. This w...