The emerging role of artificial intelligence in multiple sclerosis imaging

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Medicine
• Neurology

Background:

• Computer-aided diagnosis (CAD) aids early multiple sclerosis (MS) detection, enabling timely interventions and reducing disability.
• Artificial intelligence (AI) advancements improve classification and pattern identification in medical images for diseases like MS.
• AI techniques offer automated data analysis, surpassing traditional labor-intensive manual methods.

Purpose of the Study:

• To review current and future developments in AI-based diagnosis and prognosis of MS.
• To provide insights for researchers on AI applications in multiple sclerosis.
• To guide the understanding of AI's role in managing MS.

Main Methods:

• Investigating various AI approaches and classifiers for MS.
• Comparing state-of-the-art AI techniques for lesion segmentation/detection and prognosis.
• Describing AI techniques used with magnetic resonance imaging (MRI) for MS detection and prognosis.

Main Results:

• Evaluation of the clinical maturity of current AI techniques for MS.
• Assessment of AI's effectiveness in lesion detection and disease prognosis.
• Identification of AI's current capabilities and limitations in MS diagnosis.

Conclusions:

• AI shows significant promise for improving MS diagnosis and prognosis.
• Further research is needed to address challenges and enhance AI methods for MS.
• AI-driven insights can accelerate advancements in multiple sclerosis care.