Artificial intelligence- image learning and its applications in neurooncology: a review
Malaika Javed1, Mohammad Hamza Bajwa1, Saqib Kamran Bakhshi1
1Department of Surgery, Section of Neurosurgery, The Aga Khan University, Karachi, Pakistan.
Summary
Artificial intelligence (AI) in medical imaging enhances early tumor detection and prognosis prediction. AI-driven radiomics offers less invasive diagnostics, improving patient management and outcomes in oncology.
Area of Science:
- Oncology
- Radiology
- Artificial Intelligence
Background:
- Artificial intelligence (AI) is revolutionizing radiological image analysis.
- Machine and deep learning techniques extract features for tumor detection and survival prediction.
- Radiomics aids in predicting genetic mutations and differentiating treatment effects.
Purpose of the Study:
- To review the applications of AI-based image learning in current medical practice.
- To highlight the shift from invasive procedures to AI-driven diagnostics.
- To explore the potential of AI in neurooncology for improved patient management.
Main Methods:
- Analysis of machine learning and deep learning techniques for image processing.
- Review of radiomics applications in tumor classification and prognosis.
- Exploration of AI feasibility in neurooncology.
Main Results:
- AI enables early tumor detection and survival prediction.
- Radiomics can predict genetic mutations and distinguish tumor progression from treatment side effects.
- AI facilitates less invasive diagnostic techniques.
Conclusions:
- AI-powered image learning is crucial for the future of oncology.
- AI enhances diagnostic accuracy and prognostic prediction.
- AI integration promises more personalized and less invasive cancer management strategies.
