AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Glioma

Showing 131 to 140 of 340 articles

Clear Filters

MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas.

Journal of medical imaging and radiation oncology
Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. T...

Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM).

Neuroradiology
PURPOSE: The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) is a recently released guideline designed for the optimal reporting methodology of artificial intelligence (AI) studies. Gliomas are the most common form of primary maligna...

Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for clinical trials. MRI can noninvasively depict morphological and metabolic characteristics of H3 K27M ...

MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques.

BMC medical informatics and decision making
BACKGROUND: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and ...

Deep Learning With Radiogenomics Towards Personalized Management of Gliomas.

IEEE reviews in biomedical engineering
A state-of-the-art interdisciplinary survey on multi-modal radiogenomic approaches is presented involving applications to the diagnosis and personalized management of gliomas a common kind of brain tumors through noninvasive imaging integrated with g...

Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images.

Zeitschrift fur medizinische Physik
Today, as in every life-threatening disease, early diagnosis of brain tumors plays a life-saving role. The brain tumor is formed by the transformation of brain cells from their normal structures into abnormal cell structures. These formed abnormal ce...

Development and validation of a machine learning algorithm for predicting diffuse midline glioma, H3 K27-altered, H3 K27 wild-type high-grade glioma, and primary CNS lymphoma of the brain midline in adults.

Journal of neurosurgery
OBJECTIVE: Preoperative diagnosis of diffuse midline glioma, H3 K27-altered (DMG-A) and midline high-grade glioma without H3 K27 alteration (DMG-W), as well as midline primary CNS lymphoma (PCNSL) in adults, is challenging but crucial. The aim of thi...

Construction of a machine learning-based artificial neural network for discriminating PANoptosis related subgroups to predict prognosis in low-grade gliomas.

Scientific reports
The poor prognosis of gliomas necessitates the search for biomarkers for predicting clinical outcomes. Recent studies have shown that PANoptosis play an important role in tumor progression. However, the role of PANoptosis in in gliomas has not been f...

DeepEOR: automated perioperative volumetric assessment of variable grade gliomas using deep learning.

Acta neurochirurgica
PURPOSE: Volumetric assessments, such as extent of resection (EOR) or residual tumor volume, are essential criterions in glioma resection surgery. Our goal is to develop and validate segmentation machine learning models for pre- and postoperative mag...