AIMC Topic: Anterior Cruciate Ligament Injuries

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Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I.

Sensors (Basel, Switzerland)
Laboratory studies have limitations in screening for anterior cruciate ligament (ACL) injury risk due to their lack of ecological validity. Machine learning (ML) methods coupled with wearable sensors are state-of-art approaches for joint load estimat...

Enhanced reliability and time efficiency of deep learning-based posterior tibial slope measurement over manual techniques.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: Multifaceted factors contribute to inferior outcomes following anterior cruciate ligament (ACL) reconstruction surgery. A particular focus is placed on the posterior tibial slope (PTS). This study introduces the integration of machine learni...

Robot-assisted anterior cruciate ligament reconstruction based on three-dimensional images.

Journal of orthopaedic surgery and research
Background Tunnel placement is a key step in anterior cruciate ligament (ACL) reconstruction. The purpose of this study was to evaluate the accuracy of bone tunnel drilling in arthroscopic ACL reconstruction assisted by a three-dimensional (3D) image...

Medical Imaging Applications Developed Using Artificial Intelligence Demonstrate High Internal Validity Yet Are Limited in Scope and Lack External Validation.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
PURPOSE: To (1) review definitions and concepts necessary to interpret applications of deep learning (DL; a domain of artificial intelligence that leverages neural networks to make predictions on media inputs such as images) and (2) identify knowledg...

Approaching expert-level accuracy for differentiating ACL tear types on MRI with deep learning.

Scientific reports
Treatment for anterior cruciate ligament (ACL) tears depends on the condition of the ligament. We aimed to identify different tear statuses from preoperative MRI using deep learning-based radiomics with sex and age. We reviewed 862 patients with preo...

A Deep Learning Model Enhances Clinicians' Diagnostic Accuracy to More Than 96% for Anterior Cruciate Ligament Ruptures on Magnetic Resonance Imaging.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
PURPOSE: To develop a deep learning model to accurately detect anterior cruciate ligament (ACL) ruptures on magnetic resonance imaging (MRI) and to evaluate its effect on the diagnostic accuracy and efficiency of clinicians.

Systematic review of artificial intelligence development and evaluation for MRI diagnosis of knee ligament or meniscus tears.

Skeletal radiology
OBJECTIVE: The purpose of this systematic review was to summarize the results of original research studies evaluating the characteristics and performance of deep learning models for detection of knee ligament and meniscus tears on MRI.

In slope-changing osteotomy one millimeter is not one degree: results of an artificial intelligence-automated software analysis.

International orthopaedics
BACKGROUND: Anterior closing wedge osteotomies (ACWO) are performed in revision anterior cruciate ligament (ACL) surgery to correct an excessive posterior tibial slope (PTS).

End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to develop a semi-supervised segmentation and classification deep learning model for the diagnosis of anterior cruciate ligament (ACL) tears on MRI based on a semi-supervised framework, double-linear layers U-Ne...