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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Brain Tumor Imaging: Applications of Artificial Intelligence.

Muhammad Afridi¹, Abhi Jain², Mariam Aboian²

¹School of Osteopathic Medicine, Rowan University, Stratford, NJ.

Seminars in Ultrasound, CT, and MR

|March 27, 2022

Summary

Artificial intelligence (AI) shows promise in neuro-oncology for brain tumor analysis. Further large-scale studies are needed before AI can be used in routine clinical decisions.

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Area of Science:

Neuroscience
Medical Imaging
Artificial Intelligence

Background:

Artificial intelligence (AI) is increasingly integrated into clinical decision-making.
Machine learning (ML) and deep learning (DL) models, using radiomic features, are common in predictive modeling.
AI shows potential in brain tumor imaging for characterization, differentiation, and prognostication.

Purpose of the Study:

To review the literature on AI applications in neuro-oncology.
To explore the use of ML-based and DL-based AI for brain tumor molecular classification, differentiation, and treatment response.

Main Methods:

Literature review of current research on AI in neuro-oncology.
Focus on machine learning and deep learning applications in brain tumor imaging analysis.

Main Results:

Promising evidence supports AI's role in neuro-oncology.
AI can potentially characterize, differentiate, and prognosticate brain tumors.
Current applications show potential for molecular classification and treatment response prediction.

Conclusions:

AI demonstrates significant potential in neuro-oncology for brain tumor analysis.
Larger, multicenter studies are required to validate AI's efficacy.
Standardized image processing workflows are necessary for clinical integration.