Artificial intelligence powered advancements in upper extremity joint MRI: A review

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Musculoskeletal Medicine

Background:

• Magnetic Resonance Imaging (MRI) is crucial for musculoskeletal diagnostics, offering high-quality soft tissue and skeletal imaging without ionizing radiation.
• Current MRI limitations include long acquisition times, artifacts, noise, and difficulty distinguishing similar soft tissue structures.
• Artificial Intelligence (AI) has emerged as a key technology to address these MRI challenges over the past decade.

Purpose of the Study:

• To review current research and applications of AI in musculoskeletal MRI.
• To highlight the advancements of Deep Learning (DL) in musculoskeletal MRI.
• To focus on DL's role in identifying upper extremity joint structures and lesions on MRI images.

Main Methods:

• Review of recent scientific literature on AI and DL in musculoskeletal MRI.
• Analysis of AI applications for improving MRI image quality and reducing acquisition time.
• Examination of DL algorithms for anatomical structure and lesion identification in the upper extremity.

Main Results:

• AI significantly reduces MRI acquisition times and enhances image quality.
• AI assists clinicians in more accurate disease diagnosis and treatment planning.
• DL shows promise in accurately identifying musculoskeletal structures and pathologies in the upper extremity.

Conclusions:

• AI, particularly DL, is transforming musculoskeletal MRI by improving efficiency and diagnostic accuracy.
• AI integration in MRI leads to reduced medical costs and better patient outcomes.
• Further advancements in DL are expected to enhance musculoskeletal MRI capabilities for upper extremity imaging.