AIMC Topic: Rotator Cuff Injuries
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Deep Learning-Driven Abbreviated Shoulder MRI Protocols: Diagnostic Accuracy in Clinical Practice.
Tomography (Ann Arbor, Mich.)
Apr 17, 2025
BACKGROUND: Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexpl...
Predicting the Reparability of Rotator Cuff Tears: Machine Learning and Comparison With Previous Scoring Systems.
The American journal of sports medicine
Nov 3, 2024
BACKGROUND: Repair of rotator cuff tear is not always feasible, depending on the severity. Although several studies have investigated factors related to reparability and various methods to predict it, inconsistent scoring methods and a lack of valida...
Machine-learning models for shoulder rehabilitation exercises classification using a wearable system.
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Aug 18, 2024
PURPOSE: The objective of this study is to train and test machine-learning (ML) models to automatically classify shoulder rehabilitation exercises.
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...
Machine-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint.
SLAS technology
May 23, 2024
OBJECTIVE: This study aims to diagnose Rotator Cuff Tears (RCT) and classify the severity of RCT in patients with Osteoporosis (OP) through the analysis of shoulder joint anteroposterior (AP) X-ray-based localized proximal humeral bone mineral densit...
Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning.
Journal of shoulder and elbow surgery
Feb 3, 2024
BACKGROUND: The diagnosis of rotator cuff tears (RCTs) using radiographs alone is clinically challenging; thus, the utility of deep learning algorithms based on convolutional neural networks has been remarkable in the field of medical imaging recogni...
MRI-based automated multitask deep learning system to evaluate supraspinatus tendon injuries.
European radiology
Nov 15, 2023
OBJECTIVE: To establish an automated, multitask, MRI-based deep learning system for the detailed evaluation of supraspinatus tendon (SST) injuries.
Deep learning for automated measurement of CSA related acromion morphological parameters on anteroposterior radiographs.
European journal of radiology
Sep 9, 2023
BACKGROUND: The Critical Shoulder Angle Related Acromion Morphological Parameter (CSA- RAMP) is a valuable tool in the analyzing the etiology of the rotator cuff tears (RCTs). However, its clinical application has been limited by the time-consuming a...
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.
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...
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