AIMC Topic: Rotator Cuff

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Deep learning method for segmentation of rotator cuff muscles on MR images.

Skeletal radiology Sep 16, 2020
OBJECTIVE: To develop and validate a deep convolutional neural network (CNN) method capable of (1) selecting a specific shoulder sagittal MR image (Y-view) and (2) automatically segmenting rotator cuff (RC) muscles on a Y-view. We hypothesized a CNN ...
Shoulder Rotator Cuff Humans Neural Networks, Computer Deep Learning
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Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets.

European radiology Jul 22, 2020
OBJECTIVES: This study aimed at developing a convolutional neural network (CNN) able to automatically quantify and characterize the level of degeneration of rotator cuff (RC) muscles from shoulder CT images including muscle atrophy and fatty infiltra...
Humans Retrospective Studies Tomography, X-Ray Computed Deep Learning Rotator Cuff Shoulder Muscular Atrophy Adipose Tissue Rotator Cuff Injuries
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Ruling out rotator cuff tear in shoulder radiograph series using deep learning: redefining the role of conventional radiograph.

European radiology Feb 5, 2020
OBJECTIVE: To develop a deep learning algorithm that can rule out significant rotator cuff tear based on conventional shoulder radiographs in patients suspected of rotator cuff tear.
Rotator Cuff Injuries Rotator Cuff Radiography Radiographic Image Interpretation, Computer-Assisted Female Reproducibility of Results Humans Retrospective Studies Middle Aged Sensitivity and Specificity Deep Learning Male
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Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning.

Computer methods and programs in biomedicine Sep 3, 2019
BACKGROUND AND OBJECTIVE: Rotator cuff muscle tear is one of the most frequent reason of operations in orthopedic surgery. There are several clinical indicators such as Goutallier grade and occupation ratio in the diagnosis and surgery of these disea...
Muscle, Skeletal Automation Muscular Atrophy Rotator Cuff Deep Learning Magnetic Resonance Imaging Humans Algorithms
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Concomitant Procedures, Black Race, Male Sex, and General Anesthesia Show Fair Predictive Value for Prolonged Rotator Cuff Repair Operative Time: Analysis of the National Quality Improvement Program Database Using Machine Learning.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association May 1, 2025
PURPOSE: To develop machine learning models using the American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database to predict prolonged operative time (POT) for rotator cuff repair (RCR), as well as use the trained machine l...
Quality Improvement Rotator Cuff Injuries Anesthesia, General Sex Factors White Rotator Cuff Arthroscopy Black or African American Middle Aged United States Operative Time Predictive Value of Tests Adult Male Female Aged Databases, Factual Retrospective Studies Humans Machine Learning
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Ultrasound for Automated Classification of Full-Thickness Rotator Cuff Tendon Tears using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Rotator cuff tendon tears, the most common shoulder injuries, are typically diagnosed mainly through MRI, but can also be seen on ultrasound (US), a much less costly test that currently requires highly-trained human expert operators. An AI tool to id...
Automation Rotator Cuff Image Processing, Computer-Assisted Rotator Cuff Injuries Male Middle Aged Deep Learning Ultrasonography Humans Female
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