Artificial Intelligence in Spine Imaging Interpretation

Area of Science:

• Radiology and Medical Imaging
• Artificial Intelligence in Medicine
• Spinal Diagnostics

Background:

• Spinal disorders are a major global cause of disability.
• Imaging studies are crucial for assessing spinal conditions.
• Current diagnostic workflows can be improved.

Purpose of the Study:

• To review innovative artificial intelligence (AI) applications in spine imaging interpretation.
• To focus on a pathology-based approach for AI in spinal diagnostics.
• To provide an updated overview for musculoskeletal radiologists.

Main Methods:

• Narrative review of artificial intelligence in spine imaging.
• Focus on deep learning and conventional machine learning methods.
• Pathology-based organization: vertebral fractures, spinal deformities, degenerative disease, skeletal tumors, inflammatory disorders, opportunistic screening.

Main Results:

• AI demonstrates significant potential to improve diagnostic accuracy in spine imaging.
• AI can enhance workflow efficiency for interpreting spinal pathologies.
• Various AI applications exist across different spinal conditions.

Conclusions:

• Artificial intelligence offers promising advancements for spine imaging interpretation.
• Musculoskeletal radiologists can benefit from understanding AI applications for improved clinical adoption.
• AI tools can assist in the efficient diagnosis of diverse spinal disorders.