AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Tibial Meniscus Injuries

Showing 1 to 10 of 18 articles

Clear Filters

Diagnostic Performance of Artificial Intelligence for Detection of Anterior Cruciate Ligament and Meniscus Tears: A Systematic Review.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
PURPOSE: To (1) determine the diagnostic efficacy of artificial intelligence (AI) methods for detecting anterior cruciate ligament (ACL) and meniscus tears and to (2) compare the efficacy to human clinical experts.

Deep Learning-Based Image Feature with Arthroscopy-Aided Early Diagnosis and Treatment of Meniscus Injury of Knee Joint.

Journal of healthcare engineering
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 ...

Osteoarthritis year in review 2020: imaging.

Osteoarthritis and cartilage
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...

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)
PURPOSE: Evaluation of a deep learning approach for the detection of meniscal tears and their characterization (presence/absence of migrated meniscal fragment).

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
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.

Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI.

Journal of imaging informatics in medicine
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
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.

Multitask learning for automatic detection of meniscal injury on 3D knee MRI.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
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...

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
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...