AIMC Topic: Meningioma
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The Co-Pilot Project in wartime: lessons from Lviv, Ukraine.
Neurosurgical focus
Mar 1, 2025
OBJECTIVE: The ongoing war in Ukraine has introduced many challenges to an already overburdened and resource-limited medical system. Longitudinal collaborations, material support, educational outreach, and surgical mentorship are essential for improv...
Brain tumor classification using MRI images and deep learning techniques.
PloS one
Jan 1, 2025
Brain tumors pose a significant medical challenge, necessitating early detection and precise classification for effective treatment. This study aims to address this challenge by introducing an automated brain tumor classification system that utilizes...
Stiffness analysis of meningiomas using neural network-based inversion on MR Elastography.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Meningiomas are the most prevalent benign intracranial tumors, and surgical intervention is the primary treatment. The physical characteristics of meningiomas, such as tumor stiffness or consistency, play a crucial role in the surgical approach. This...
Design and Development of Hypertuned Deep learning Frameworks for Detection and Severity Grading of Brain Tumor using Medical Brain MR images.
Current medical imaging
Jan 1, 2024
BACKGROUND: Brain tumor is a grave illness causing worldwide fatalities. The current detection methods for brain tumors are manual, invasive, and rely on histopathological analysis. Determining the type of brain tumor after its detection relies on bi...
Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model.
Neurosurgery
Mar 15, 2021
BACKGROUND: Machine learning (ML)-based predictive models are increasingly common in neurosurgery, but typically require large databases of discrete variables for training. Natural language processing (NLP) can extract meaningful data from unstructur...
A Deep Convolutional Neural Network With Performance Comparable to Radiologists for Differentiating Between Spinal Schwannoma and Meningioma.
Spine
May 15, 2020
STUDY DESIGN: Retrospective analysis of magnetic resonance imaging (MRI).
Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.
Journal of digital imaging
Aug 1, 2019
Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to have high accuracy in semantic segmentation of medical (e.g., radiology and pathology) images. However...
Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging.
Neurosurgical focus
Nov 1, 2018
OBJECTIVEPrognostication and surgical planning for WHO grade I versus grade II meningioma requires thoughtful decision-making based on radiographic evidence, among other factors. Although conventional statistical models such as logistic regression ar...
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