AIMC Topic: Rotator Cuff

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Development of a deep learning-based fully automated segmentation of rotator cuff muscles from clinical MR scans.

Acta radiologica (Stockholm, Sweden : 1987) Jul 23, 2024
BACKGROUND: The fatty infiltration and atrophy in the muscle after a rotator cuff (RC) tear are important in surgical decision-making and are linked to poor clinical outcomes after rotator cuff repair. An accurate and reliable quantitative method sho...
Aged Adult Male Female Reproducibility of Results Magnetic Resonance Imaging Rotator Cuff Injuries Rotator Cuff Middle Aged Deep Learning Humans
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Glenohumeral joint force prediction with deep learning.

Journal of biomechanics Jan 12, 2024
Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequentl...
Biomechanical Phenomena Deep Learning Rotator Cuff Shoulder Joint Humans Reproducibility of Results
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Objective analysis of partial three-dimensional rotator cuff muscle volume and fat infiltration across ages and sex from clinical MRI scans.

Scientific reports Sep 1, 2023
Objective analysis of rotator cuff (RC) atrophy and fatty infiltration (FI) from clinical MRI is limited by qualitative measures and variation in scapular coverage. The goals of this study were to: develop/evaluate a method to quantify RC muscle size...
Female Male Magnetic Resonance Imaging Humans Artificial Intelligence Rotator Cuff Sexual Behavior Atrophy
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Prediction of Retear After Arthroscopic Rotator Cuff Repair Based on Intraoperative Arthroscopic Images Using Deep Learning.

The American journal of sports medicine Aug 11, 2023
BACKGROUND: It is challenging to predict retear after arthroscopic rotator cuff repair (ARCR). The usefulness of arthroscopic intraoperative images as predictors of the ARCR prognosis has not been analyzed.
Arthroscopy Recurrence Rotator Cuff Magnetic Resonance Imaging Rotator Cuff Injuries Humans Retrospective Studies Cohort Studies Treatment Outcome Deep Learning
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Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears.

Journal of orthopaedic surgery and research Jun 13, 2023
BACKGROUND: Accurately diagnosing supraspinatus tears based on magnetic resonance imaging (MRI) is challenging and time-combusting due to the experience level variability of the musculoskeletal radiologists and orthopedic surgeons. We developed a dee...
Arthroplasty Rotator Cuff Deep Learning Rotator Cuff Injuries Humans Retrospective Studies
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Developing a machine learning algorithm to predict probability of retear and functional outcomes in patients undergoing rotator cuff repair surgery: protocol for a retrospective, multicentre study.

BMJ open Feb 10, 2023
INTRODUCTION: The effectiveness of rotator cuff tear repair surgery is influenced by multiple patient-related, pathology-centred and technical factors, which is thought to contribute to the reported retear rates between 17% and 94%. Adequate patient ...
Artificial Intelligence Machine Learning Humans Probability Arthroscopy Rotator Cuff Treatment Outcome Magnetic Resonance Imaging Multicenter Studies as Topic Retrospective Studies Cohort Studies
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Deep Learning Diagnosis and Classification of Rotator Cuff Tears on Shoulder MRI.

Investigative radiology Jan 18, 2023
BACKGROUND: Detection of rotator cuff tears, a common cause of shoulder disability, can be time-consuming and subject to reader variability. Deep learning (DL) has the potential to increase radiologist accuracy and consistency.
Shoulder Rotator Cuff Magnetic Resonance Imaging Rotator Cuff Injuries Humans Deep Learning
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Evaluation of a deep learning method for the automated detection of supraspinatus tears on MRI.

Skeletal radiology Feb 22, 2022
OBJECTIVE: To evaluate if deep learning is a feasible approach for automated detection of supraspinatus tears on MRI.
Humans Magnetic Resonance Imaging Rotator Cuff Injuries Rotator Cuff Retrospective Studies Deep Learning
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Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning.

European radiology May 20, 2021
OBJECTIVE: To develop a deep learning algorithm capable of evaluating subscapularis tendon (SSC) tears based on axillary lateral shoulder radiography.
Arthroscopy Rotator Cuff Radiography Rotator Cuff Injuries Retrospective Studies Humans Deep Learning
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Imbalanced Loss-Integrated Deep-Learning-Based Ultrasound Image Analysis for Diagnosis of Rotator-Cuff Tear.

Sensors (Basel, Switzerland) Mar 22, 2021
A rotator cuff tear (RCT) is an injury in adults that causes difficulty in moving, weakness, and pain. Only limited diagnostic tools such as magnetic resonance imaging (MRI) and ultrasound Imaging (UI) systems can be utilized for an RCT diagnosis. Al...
Magnetic Resonance Imaging Ultrasonography Rotator Cuff Neural Networks, Computer Image Processing, Computer-Assisted Deep Learning
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