Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

Imaging Studies for Cardiovascular System IV: CMRI

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Soft tissue degeneration 10+ years after ACLR and their association with radiographic PTOA and knee pain development: radiomic analysis using qMRI approach in MOON nested on-site cohort.

Osteoarthritis imaging·2026

Same author

The post-arthroscopic knee: fundamental imaging concepts and emerging techniques.

Skeletal radiology·2026

Same author

Standardized Knee Meniscus MRI Reporting: An Interdisciplinary Delphi Consensus.

Radiology·2026

Same author

Associations Between Patient Characteristics and Cartilage T1ρ Relaxation Times Vary Over Time Following Patellar Dislocation.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society·2026

Same author

Perineurioma-like EMA-positive calvarial neoplasms: clinicopathological study of eight cases.

Histopathology·2026

Same author

Multi-institutional classification of fibroglandular tissue and background parenchymal enhancement in breast MRI using deep learning.

Journal of medical imaging (Bellingham, Wash.)·2026

Search research articles

Related Experiment Video

Updated: Aug 12, 2025

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Published on: March 12, 2021

Deep Learning Diagnosis and Classification of Rotator Cuff Tears on Shoulder MRI.

Dana J Lin¹, Michael Schwier², Bernhard Geiger²

¹From the Department of Radiology, NYU Grossman School of Medicine, New York, NY.

Investigative Radiology

|February 2, 2023

Summary

Deep learning (DL) models can accurately detect rotator cuff tears on MRI, showing performance comparable to radiologists. This technology offers a promising solution for improving diagnostic consistency and efficiency in identifying these common shoulder injuries.

More Related Videos

Reverse Total Shoulder Arthroplasty

Reverse Total Shoulder Arthroplasty

Published on: July 5, 2011

Treatment Protocol for Rotator Cuff Calcific Tendinitis Using a Single-Crystal Piezoelectric Focused Shock Wave Source

Treatment Protocol for Rotator Cuff Calcific Tendinitis Using a Single-Crystal Piezoelectric Focused Shock Wave Source

Published on: December 23, 2022

Related Experiment Videos

Last Updated: Aug 12, 2025

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Published on: March 12, 2021

Reverse Total Shoulder Arthroplasty

Reverse Total Shoulder Arthroplasty

Published on: July 5, 2011

Treatment Protocol for Rotator Cuff Calcific Tendinitis Using a Single-Crystal Piezoelectric Focused Shock Wave Source

Treatment Protocol for Rotator Cuff Calcific Tendinitis Using a Single-Crystal Piezoelectric Focused Shock Wave Source

Published on: December 23, 2022

Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Rotator cuff tears are a frequent cause of shoulder disability.
Current detection methods can be time-consuming and vary between readers.
Deep learning (DL) shows potential to enhance radiologist accuracy and consistency.

Purpose of the Study:

To develop a DL model for detecting and classifying rotator cuff tears on shoulder MRI.
The model aims to categorize tears into no tear, partial-thickness tear, or full-thickness tear.

Main Methods:

A DL ensemble algorithm was developed using 11,925 shoulder MRIs (11,405 for training, 520 for testing).
The model utilized 4 MRI sequences: fluid-sensitive in 3 planes and sagittal oblique T1-weighted.
Ground truth was established using radiology reports and a multireader study.

Main Results:

The DL model achieved high AUCs for overall tears (0.93 for supraspinatus, 0.89 for infraspinatus, 0.90 for subscapularis).
Performance was excellent for full-thickness tears, with AUCs up to 0.99.
Model accuracy was comparable to that of subspecialty-trained musculoskeletal radiologists.

Conclusions:

Deep learning diagnosis of rotator cuff tears is feasible and demonstrates excellent performance.
The DL model's accuracy is similar to that of experienced radiologists, particularly for full-thickness tears.