AIMC Topic: Arthroscopy

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Enhanced Detection, Using Deep Learning Technology, of Medial Meniscal Posterior Horn Ramp Lesions in Patients with ACL Injury.

The Journal of bone and joint surgery. American volume
BACKGROUND: Meniscal ramp lesions can impact knee stability, particularly when associated with anterior cruciate ligament (ACL) injuries. Although magnetic resonance imaging (MRI) is the primary diagnostic tool, its diagnostic accuracy remains subopt...

[The standardization and digitalization and intelligentization represent the future development direction of hip arthroscopy diagnosis and treatment technology].

Zhonghua yi xue za zhi
In recent years, hip arthroscopy has made great progress and has been extended to the treatment of intra-articular or periarticular diseases. However, the complex structure of the hip joint, high technical operation requirements and relatively long l...

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
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...

Arthroscopy-validated Diagnostic Performance of 7-Minute Five-Sequence Deep Learning Super-Resolution 3-T Shoulder MRI.

Radiology
Background Deep learning (DL) methods enable faster shoulder MRI than conventional methods, but arthroscopy-validated evidence of good diagnostic performance is scarce. Purpose To validate the clinical efficacy of 7-minute threefold parallel imaging ...

Deep Learning Superresolution for Simultaneous Multislice Parallel Imaging-Accelerated Knee MRI Using Arthroscopy Validation.

Radiology
Background Deep learning (DL) methods can improve accelerated MRI but require validation against an independent reference standard to ensure robustness and accuracy. Purpose To validate the diagnostic performance of twofold-simultaneous-multislice (S...

A Systematic Review on Deep Learning Model in Computer-aided Diagnosis for Anterior Cruciate Ligament Injury.

Current medical imaging
INTRODUCTION: In developing Computer-Aided Diagnosis (CAD), a Convolutional Neural Network (CNN) has been commonly used as a Deep Learning (DL) model. Although it is still early, DL has excellent potential in implementing computers in medical diagnos...

[Management of the nondisplaced type Herbert D1 scaphoid fracture with robot navigation combined with wrist arthroscopy].

Zhonghua yi xue za zhi
To investigate the feasibility and the clinical efficiency of robot navigation combined with wrist arthroscopy in minimally invasive treatment of nondisplaced type Herbert D1 scaphoid fracture. A retrospective analysis was performed on 9 patients who...

Machine Learning Algorithms Predict Functional Improvement After Hip Arthroscopy for Femoroacetabular Impingement Syndrome in Athletes.

The Journal of bone and joint surgery. American volume
BACKGROUND: Despite previous reports of improvements for athletes following hip arthroscopy for femoroacetabular impingement syndrome (FAIS), many do not achieve clinically relevant outcomes. The purpose of this study was to develop machine learning ...

Editorial Commentary: Predicting Satisfaction After Hip Arthroscopy Using Machine Learning: What Do Treadmills and Black Boxes Have to Do With Arthroscopy?

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
The use of advanced statistical methods and artificial intelligence including machine learning enables researchers to identify preoperative characteristics predictive of patients achieving minimal clinically important differences in health outcomes a...