OBJECTIVES: It remains difficult to characterize the source of pain in knee joints either using radiographs or magnetic resonance imaging (MRI). We sought to determine if advanced machine learning methods such as deep neural networks could distinguis...
OBJECTIVE: To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays.
Seminars in musculoskeletal radiology
Jan 28, 2020
Artificial intelligence (AI) holds the potential to revolutionize the field of radiology by increasing the efficiency and accuracy of both interpretive and noninterpretive tasks. We have only just begun to explore AI applications in the diagnostic ev...
International journal of computer assisted radiology and surgery
Jan 14, 2020
PURPOSE: Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Radiologists usually review knee X-ray images and grade the severity of the impairments according to the Kellgren-Lawrence grading scheme. However, this...
Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accel...
BACKGROUND: Preoperative identification of knee arthroplasty is important for planning revision surgery. However, up to 10% of implants are not identified prior to surgery. The purposes of this study were to develop and test the performance of a deep...
Computer methods and programs in biomedicine
Sep 24, 2019
BACKGROUND AND OBJECTIVE: With the rapid development of medical imaging and intelligent diagnosis, artificial intelligence methods have become a research hotspot of radiography processing technology in recent years. The low definition of knee magneti...
PURPOSE: Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. This study assessed key features that can be used as mechanical biomarkers for KOA severity and progression. The identified features were validated statistically...
BACKGROUND: The variation in articular cartilage thickness (ACT) in healthy knees is difficult to quantify and therefore poorly documented. Our aims are to (1) define how machine learning (ML) algorithms can automate the segmentation and measurement ...
BACKGROUND: Tracking patient-generated health data (PGHD) following total joint arthroplasty (TJA) may enable data-driven early intervention to improve clinical results. We aim to demonstrate the feasibility of combining machine learning (ML) with PG...