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Arthroscopy

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Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference.

Skeletal radiology 32170334
OBJECTIVE: To clinically validate a fully automated deep convolutional neural network (DCNN) for detection of surgically proven meniscus tears.
Adolescent Adult Aged Arthroscopy Clinical Competence Female Humans Magnetic Resonance Imaging Male Middle Aged Neural Networks, Computer Radiologists Reference Standards Retrospective Studies Sensitivity and Specificity Tibial Meniscus Injuries
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Deep Learning Approach for Anterior Cruciate Ligament Lesion Detection: Evaluation of Diagnostic Performance Using Arthroscopy as the Reference Standard.

Journal of magnetic resonance imaging : JMRI 32715584
BACKGROUND: MRI is the most commonly used imaging method for diagnosing anterior cruciate ligament (ACL) injuries. However, the interpretation of knee MRI is time-intensive and depends on the clinical experience of the reader. An automated detection ...
Anterior Cruciate Ligament Anterior Cruciate Ligament Injuries Arthroscopy Deep Learning Humans Magnetic Resonance Imaging Reference Standards Reproducibility of Results Retrospective Studies Sensitivity and Specificity
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Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?

Clinical orthopaedics and related research 32282466
BACKGROUND: Machine-learning methods such as the Bayesian belief network, random forest, gradient boosting machine, and decision trees have been used to develop decision-support tools in other clinical settings. Opioid abuse is a problem among civili...
Adult Anterior Cruciate Ligament Injuries Anterior Cruciate Ligament Reconstruction Arthroscopy Clinical Decision-Making Databases, Factual Decision Support Techniques Drug Administration Schedule Female Humans Machine Learning Male Military Medicine Narcotic Antagonists Opioid-Related Disorders Pain, Postoperative Reproducibility of Results Retrospective Studies Risk Assessment Risk Factors Time Factors Treatment Outcome Young Adult
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Radiographic Indices Are Not Predictive of Clinical Outcomes Among 1735 Patients Indicated for Hip Arthroscopic Surgery: A Machine Learning Analysis.

The American journal of sports medicine 32924530
BACKGROUND: The relationship between the preoperative radiographic indices for femoroacetabular impingement syndrome (FAIS) and postoperative patient-reported outcome measure (PROM) scores continues to be under investigation, with inconsistent findin...
Activities of Daily Living Arthroscopy Cohort Studies Femoracetabular Impingement Hip Joint Humans Machine Learning Minimal Clinically Important Difference Patient Reported Outcome Measures Treatment Outcome
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Machine learning augmented near-infrared spectroscopy: In vivo follow-up of cartilage defects.

Osteoarthritis and cartilage 33359249
OBJECTIVE: To assess the potential of near-infrared spectroscopy (NIRS) for in vivo arthroscopic monitoring of cartilage defects.
Animals Arthroscopy Carpal Joints Cartilage Diseases Cartilage, Articular Horses Machine Learning Neural Networks, Computer Organ Size Spectroscopy, Near-Infrared Wrist Joint
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Machine Learning Algorithms Predict Clinically Significant Improvements in Satisfaction After Hip Arthroscopy.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association 33359160
PURPOSE: To develop machine learning algorithms to predict failure to achieve clinically significant satisfaction after hip arthroscopy.
Adult Algorithms Arthroscopy Calibration Case-Control Studies Female Hip Humans Machine Learning Male Neural Networks, Computer Patient Satisfaction Postoperative Period Risk ROC Curve Treatment Outcome Young Adult
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Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting Clinically Significant Functional Improvement in a Mixed Population of Primary Hip Arthroscopy.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association 33460708
PURPOSE: To (1) develop and validate a machine learning algorithm to predict clinically significant functional improvements after hip arthroscopy for femoroacetabular impingement syndrome and to (2) develop a digital application capable of providing ...
Activities of Daily Living Adult Algorithms Arthroscopy Calibration Cohort Studies Female Hip Joint Humans Male Patient Reported Outcome Measures Recovery of Function Retrospective Studies Risk Factors ROC Curve Supervised Machine Learning Treatment Outcome Young Adult
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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 33812519
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...
Algorithms Arthroscopy Artificial Intelligence Humans Machine Learning Personal Satisfaction
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Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning.

European radiology 34014379
OBJECTIVE: To develop a deep learning algorithm capable of evaluating subscapularis tendon (SSC) tears based on axillary lateral shoulder radiography.
Arthroscopy Deep Learning Humans Radiography Retrospective Studies Rotator Cuff Rotator Cuff Injuries
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Machine Learning Algorithms Predict Functional Improvement After Hip Arthroscopy for Femoroacetabular Impingement Syndrome in Athletes.

The Journal of bone and joint surgery. American volume 33877058
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 ...
Adult Algorithms Arthroscopy Female Femoracetabular Impingement Humans Machine Learning Male Minimal Clinically Important Difference Predictive Value of Tests Recovery of Function Sports Treatment Outcome Young Adult
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