Objective:

To review recent advances in artificial intelligence technologies for diagnosing disc diseases across different imaging modalities and how AI can assist in clinical workflows.

Approach:

• Introduction: Discusses the importance of accurate diagnosis of intervertebral disc pathologies and the limitations of conventional imaging techniques.
• AI Integration: Explores how AI can optimize imaging workflows, improve segmentation, classification, and detection of disc diseases.

Key Findings:

• Conventional imaging modalities like MRI, CT, and X-ray have significant limitations in diagnosing intervertebral disc diseases.
• AI technologies can enhance diagnostic accuracy and efficiency by automating segmentation and classification tasks.

Interpretation:

AI has the potential to transform the diagnostic process for intervertebral disc diseases by addressing inefficiencies and improving accuracy.

Limitations:

• Conventional imaging techniques are still widely used despite their limitations in soft tissue resolution and diagnostic accuracy.
• Manual assessment in traditional diagnostics is time-consuming and prone to inter-observer variability.

Conclusion:

AI technologies represent a promising advancement in the diagnosis of intervertebral disc diseases, potentially leading to more efficient and standardized diagnostic tools.