AIMC Topic: Osteoarthritis, Knee
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Development of the machine learning model that is highly validated and easily applicable to predict radiographic knee osteoarthritis progression.
Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Oct 1, 2024
Many models using the aid of artificial intelligence have been recently proposed to predict the progression of knee osteoarthritis. However, previous models have not been properly validated with an external data set or have reported poor predictive p...
High accuracy in lower limb alignment analysis using convolutional neural networks, with improvements needed for joint-level metrics.
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Sep 22, 2024
PURPOSE: Evaluation of long-leg standing radiographs (LSR) is a standardised procedure for analysis of primary or secondary deformities of the lower limbs. Deep-learning convolutional neural networks (CNN) offer the potential to enhance radiological ...
Development of an artificial intelligence model for predicting implant size in total knee arthroplasty using simple X-ray images.
Journal of orthopaedic surgery and research
Aug 27, 2024
BACKGROUND: Accurate estimation of implant size before surgery is crucial in preparing for total knee arthroplasty. However, this task is time-consuming and labor-intensive. To alleviate this burden on surgeons, we developed a reliable artificial int...
Machine learning analysis of serum cholesterol's impact on knee osteoarthritis progression.
Scientific reports
Aug 14, 2024
The controversy surrounding whether serum total cholesterol is a risk factor for the graded progression of knee osteoarthritis (KOA) has prompted this study to develop an authentic prediction model using a machine learning (ML) algorithm. The objecti...
Diagnosing the Severity of Knee Osteoarthritis Using Regression Scores From Artificial Intelligence Convolution Neural Networks.
Orthopedics
Jul 29, 2024
BACKGROUND: This study focused on using deep learning neural networks to classify the severity of osteoarthritis in the knee. A continuous regression score of osteoarthritis severity has yet to be explored using artificial intelligence machine learni...
Reliable prediction of implant size and axial alignment in AI-based 3D preoperative planning for total knee arthroplasty.
Scientific reports
Jul 23, 2024
The size and axial alignment of prostheses, when planned during total knee replacement (TKA) are critical for recovery of knee function and improvement of knee pain symptoms. This research aims to study the effect of artificial intelligence (AI)-base...
Artificial intelligence in total and unicompartmental knee arthroplasty.
BMC musculoskeletal disorders
Jul 22, 2024
The application of Artificial intelligence (AI) and machine learning (ML) tools in total (TKA) and unicompartmental knee arthroplasty (UKA) emerges with the potential to improve patient-centered decision-making and outcome prediction in orthopedics, ...
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...
The value of deep learning-based X-ray techniques in detecting and classifying K-L grades of knee osteoarthritis: a systematic review and meta-analysis.
European radiology
Jul 12, 2024
OBJECTIVES: Knee osteoarthritis (KOA), a prevalent degenerative joint disease, is primarily diagnosed through X-ray imaging. The Kellgren-Lawrence grading system (K-L) is the gold standard for evaluating KOA severity through X-ray analysis. However, ...
Integrating Radiomics and Neural Networks for Knee Osteoarthritis Incidence Prediction.
Arthritis & rheumatology (Hoboken, N.J.)
Jul 8, 2024
OBJECTIVE: Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)-based joint space (JS) radiomic model (RM) to predict radiographic...
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