AIMC Topic: Tibial Meniscus Injuries
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Diagnosis of knee meniscal injuries using artificial intelligence: A systematic review and meta-analysis of diagnostic performance.
PloS one
Jun 24, 2025
AIM OF THE STUDY: The aim was to systematically review the literature and perform a meta-analysis to estimate the performance of artificial intelligence (AI) algorithms in detecting meniscal injuries.
Deep learning-assisted detection of meniscus and anterior cruciate ligament combined tears in adult knee magnetic resonance imaging: a crossover study with arthroscopy correlation.
International orthopaedics
Apr 28, 2025
AIM: We aimed to compare the diagnostic performance of physicians in the detection of arthroscopically confirmed meniscus and anterior cruciate ligament (ACL) tears on knee magnetic resonance imaging (MRI), with and without assistance from a deep lea...
American academy of Orthopedic Surgeons' OrthoInfo provides more readable information regarding meniscus injury than ChatGPT-4 while information accuracy is comparable.
Journal of ISAKOS : joint disorders & orthopaedic sports medicine
Feb 21, 2025
INTRODUCTION: Over 61% of Americans seek health information online, often using artificial intelligence (AI) tools like ChatGPT. However, concerns persist about the readability and accessibility of AI-generated content, especially for individuals wit...
Multitask learning for automatic detection of meniscal injury on 3D knee MRI.
Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Dec 2, 2024
Magnetic resonance imaging (MRI) of the knee is the recommended diagnostic method before invasive arthroscopy surgery. Nevertheless, interpreting knee MRI scans is a time-consuming process that is vulnerable to inaccuracies and inconsistencies. We pr...
Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI.
Journal of imaging informatics in medicine
Jul 17, 2024
Meniscal injury is a common cause of knee joint pain and a precursor to knee osteoarthritis (KOA). The purpose of this study is to develop an automatic pipeline for meniscal injury classification and localization using fully and weakly supervised net...
Achieving high accuracy in meniscus tear detection using advanced deep learning models with a relatively small data set.
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Jul 17, 2024
PURPOSE: This study aims to evaluate the effectiveness of advanced deep learning models, specifically YOLOv8 and EfficientNetV2, in detecting meniscal tears on magnetic resonance imaging (MRI) using a relatively small data set.
Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis.
European radiology
Feb 22, 2024
OBJECTIVES: To review and compare the accuracy of convolutional neural networks (CNN) for the diagnosis of meniscal tears in the current literature and analyze the decision-making processes utilized by these CNN algorithms.
Deep Learning-Based Image Feature with Arthroscopy-Aided Early Diagnosis and Treatment of Meniscus Injury of Knee Joint.
Journal of healthcare engineering
Sep 17, 2021
The aim of this study is to explore the clinical effect of deep learning-based MRI-assisted arthroscopy in the early treatment of knee meniscus sports injury. Based on convolutional neural network algorithm, Adam algorithm was introduced to optimize ...
Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Mar 11, 2021
PURPOSE: Evaluation of a deep learning approach for the detection of meniscal tears and their characterization (presence/absence of migrated meniscal fragment).
Osteoarthritis year in review 2020: imaging.
Osteoarthritis and cartilage
Jan 5, 2021
This narrative "Year in Review" highlights a selection of articles published between January 2019 and April 2020, to be presented at the OARSI World Congress 2020 within the field of osteoarthritis (OA) imaging. Articles were obtained from a PubMed s...
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