Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls

Area of Science:

• Neuroscience
• Ophthalmology
• Medical Imaging

Background:

• Multiple sclerosis (MS) is a CNS autoimmune disease with inflammatory, demyelinating, and neurodegenerative components.
• Retinal imaging, especially Optical coherence tomography (OCT), is vital for assessing MS-related retinal injury.
• Artificial intelligence (AI) integration shows potential for advanced OCT analysis in MS.

Purpose of the Study:

• To review current research on AI, including machine learning (ML) and deep learning (DL), integrated with OCT for MS.
• To examine AI's role in MS diagnosis, disease progression monitoring, and patient care.
• To discuss advancements, challenges, and ethical considerations of AI in MS OCT analysis.

Main Methods:

• Review of current research studies on AI algorithms (ML/DL) applied to OCT imaging in multiple sclerosis.
• Analysis of AI's capabilities in detecting, classifying, and segmenting MS-related retinal abnormalities.
• Evaluation of AI's prognostic value in predicting MS disease progression using longitudinal OCT data.

Main Results:

• AI algorithms demonstrate high accuracy in detecting and classifying MS-related retinal abnormalities on OCT scans.
• AI enhances OCT image segmentation, streamlining diagnosis and reducing human error.
• AI-driven prognostic models predict MS disease progression, enabling early intervention and personalized treatment.

Conclusions:

• AI integration with OCT significantly advances MS diagnosis, monitoring, and personalized treatment planning.
• AI tools offer improved efficiency, accuracy, and prognostic capabilities for managing MS.
• Future directions include developing AI-powered screening tools and supporting clinical decision-making in MS care.