Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives.

Area of Science:

• Neurology
• Medical Imaging
• Artificial Intelligence

Background:

• Brain tumors are abnormal growths within the skull that can cause severe health issues and increase mortality rates, especially malignant types.
• Early detection of brain tumors is critical for timely intervention and improved patient outcomes.
• Computer-aided diagnostic (CAD) systems, integrated with artificial intelligence (AI), show significant promise for the early identification of brain tumors.

Purpose of the Study:

• To review the existing literature on computer-aided diagnostic (CAD) systems for brain tumor detection.
• To identify challenges and limitations associated with current CAD systems across various imaging modalities.
• To outline the current requirements and future research prospects in AI-driven brain tumor diagnosis.

Main Methods:

• Systematic review of 124 research articles published between 2000 and 2022.
• Analysis of challenges faced by CAD systems based on different medical imaging modalities.
• Identification of current needs and future trends in the field of AI for brain tumor detection.

Main Results:

• The review identified key challenges in CAD systems for brain tumor detection, including data variability, algorithm generalizability, and interpretability.
• Different imaging modalities present unique challenges for AI-based analysis.
• There is a growing need for robust and reliable AI tools to support clinicians in early brain tumor diagnosis.

Conclusions:

• AI and CAD systems are essential tools for the early detection of brain tumors, offering potential to improve diagnostic accuracy and speed.
• Addressing the identified challenges is crucial for the advancement and clinical adoption of these technologies.
• Future research should focus on developing more sophisticated AI algorithms, multimodal data integration, and clinical validation to enhance brain tumor diagnosis and patient care.