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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

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Artificial Intelligence in Rotator Cuff Tear Detection: A Systematic Review of MRI-Based Models.

Umile Giuseppe Longo^1,2, Benedetta Bandini^1,2, Letizia Mancini^1,3

¹Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Rome, Italy.

Diagnostics (Basel, Switzerland)

|June 13, 2025

Summary

Artificial Intelligence (AI) models show strong performance in diagnosing Rotator Cuff Tears (RCTs) from MRI scans, matching human expert accuracy. Further research is needed for clinical integration.

Keywords:

MRI artificial intelligence diagnosis rotator cuff

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Area of Science:

Orthopedic Imaging
Artificial Intelligence in Medicine
Diagnostic Accuracy

Background:

Rotator Cuff Tears (RCTs) are common musculoskeletal injuries.
Accurate diagnosis of RCTs using Magnetic Resonance Imaging (MRI) is crucial for effective treatment.
The role of Artificial Intelligence (AI) in interpreting medical images is rapidly evolving.

Purpose of the Study:

To systematically review the literature on AI model applications for diagnosing and classifying RCTs via MRI.
To assess the performance and current use of AI in rotator cuff tear detection.

Main Methods:

A descriptive systematic review of diagnostic studies utilizing AI on rotator cuff MRI images.
Searched literature from 2020 to November 2024, focusing on supraspinatus and biceps tears.
Excluded studies using Ultrasound or X-ray, or those with only healthy rotator cuffs.

Main Results:

The VGG network was the most common AI model, predominantly using coronal T2-weighted MRI sequences.
AI models demonstrated high performance, with accuracy ranging from 71.0% to 100%.
No significant differences in diagnostic metrics (accuracy, sensitivity, specificity, precision) were found between AI and human experts.

Conclusions:

AI shows potential to enhance diagnostic efficiency and optimize workflows in orthopedic imaging.
Future research should prioritize external validation, regulatory considerations, and AI-human collaboration models.
Safe and effective integration of AI into clinical practice for RCT diagnosis requires further investigation.