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Anterior Cruciate Ligament Injuries

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Economical hybrid novelty detection leveraging global aleatoric semantic uncertainty for enhanced MRI-based ACL tear diagnosis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This study presents an innovative hybrid deep learning (DL) framework that reformulates the sagittal MRI-based anterior cruciate ligament (ACL) tear classification task as a novelty detection problem to tackle class imbalance. We introduce a highly d...

Deep Learning-Assisted Automatic Diagnosis of Anterior Cruciate Ligament Tear in Knee Magnetic Resonance Images.

Tomography (Ann Arbor, Mich.)
Anterior cruciate ligament (ACL) tears are prevalent knee injures, particularly among active individuals. Accurate and timely diagnosis is essential for determining the optimal treatment strategy and assessing patient prognosis. Various previous stud...

Finding the needle in the haystack of isokinetic knee data: Random Forest modelling improves information about ACLR-related deficiencies.

Journal of sports sciences
The difficulties of rehabilitation after anterior cruciate ligament (ACL) injuries, subsequent return-to-sport (RTS) let alone achieving pre-injury performance, are well known. Isokinetic testing is often used to assess strength capacities during tha...

Effectiveness of an intelligent weight-bearing rehabilitation robot in enhancing recovery following anterior cruciate ligament reconstruction.

Frontiers in public health
AIM: Orthopedic surgery patients frequently delay early rehabilitation due to postoperative discomfort. This is especially true for younger patients with anterior cruciate ligament injuries who are eager to return to sports after discharge. Despite t...

Machine learning models predicting risk of revision or secondary knee injury after anterior cruciate ligament reconstruction demonstrate variable discriminatory and accuracy performance: a systematic review.

BMC musculoskeletal disorders
BACKGROUND: To summarize the statistical performance of machine learning in predicting revision, secondary knee injury, or reoperations following anterior cruciate ligament reconstruction (ACLR), and to provide a general overview of the statistical p...

A Robotic Clamped-Kinematic System to Study Knee Ligament Injury.

Annals of biomedical engineering
Knee ligament injury is among the most common sports injuries and is associated with long recovery periods and low return-to-sport rates. Unfortunately, the mechanics of ligament injury are difficult to study in vivo, and computational studies provid...

Combining an improved political optimizer with convolutional neural networks for accurate anterior cruciate ligament tear detection in sports injuries.

Scientific reports
A new technique has been developed to identify ACL tears in sports injuries. This method utilizes a Convolutional Neural Network (CNN) in combination with a modified Political Optimizer (IPO) algorithm, resulting in a major breakthrough in detecting ...

Thessaly Graft Index: An Artificial Intelligence-Based Index for the Assessment of Graft Integrity in ACL-Reconstructed Knees.

The Journal of bone and joint surgery. American volume
BACKGROUND: Magnetic resonance imaging (MRI) has proven to be a valuable noninvasive tool to evaluate graft integrity after anterior cruciate ligament (ACL) reconstruction. However, MRI protocols and interpretation methodologies are quite diverse, pr...

Posttraumatic Arthritis After Anterior Cruciate Ligament Injury: Machine Learning Comparison Between Surgery and Nonoperative Management.

The American journal of sports medicine
BACKGROUND: Nonoperative and operative management techniques after anterior cruciate ligament (ACL) injury are both appropriate treatment options for selected patients. However, the subsequent development of posttraumatic knee osteoarthritis (PTOA) r...