AI Medical Compendium Journal:
The Journal of arthroplasty

Showing 21 to 30 of 78 articles

A Deep Learning Tool for Automated Landmark Annotation on Hip and Pelvis Radiographs.

The Journal of arthroplasty
BACKGROUND: Automatic methods for labeling and segmenting pelvis structures can improve the efficiency of clinical and research workflows and reduce the variability introduced with manual labeling. The purpose of this study was to develop a single de...

Exposure to Extended Reality and Artificial Intelligence-Based Manifestations: A Primer on the Future of Hip and Knee Arthroplasty.

The Journal of arthroplasty
BACKGROUND: Software-infused services, from robot-assisted and wearable technologies to artificial intelligence (AI)-laden analytics, continue to augment clinical orthopaedics - namely hip and knee arthroplasty. Extended reality (XR) tools, which enc...

Artificial Intelligence for Automated Implant Identification in Knee Arthroplasty: A Multicenter External Validation Study Exceeding 3.5 Million Plain Radiographs.

The Journal of arthroplasty
BACKGROUND: Surgical management of complications following knee arthroplasty demands accurate and timely identification of implant manufacturer and model. Automated image processing using deep machine learning has been previously developed and intern...

Leg-Length Discrepancy Variability on Standard Anteroposterior Pelvis Radiographs: An Analysis Using Deep Learning Measurements.

The Journal of arthroplasty
BACKGROUND: Leg-length discrepancy (LLD) is a critical factor in component selection and placement for total hip arthroplasty. However, LLD radiographic measurements are subject to variation based on the femoral/pelvic landmarks chosen. This study le...

Artificial Intelligence Autonomously Measures Cup Orientation, Corrects for Pelvis Orientation, and Identifies Retroversion From Antero-Posterior Pelvis Radiographs.

The Journal of arthroplasty
BACKGROUND: Measuring cup orientation is time consuming and inaccurate, but orientation influences the risk of impingement and dislocation following total hip arthroplasty (THA). This study designed an artificial intelligence (AI) program to autonomo...

Deep Learning Phenotype Automation and Cohort Analyses of 1,946 Knees Using the Coronal Plane Alignment of the Knee Classification.

The Journal of arthroplasty
BACKGROUND: The Coronal Plane Alignment of the Knee (CPAK) classification allows for knee phenotyping which can be used in preoperative planning prior to total knee arthroplasty. We used deep learning (DL) to automate knee phenotyping and analyzed CP...

Cost-Effectiveness of Robot-Assisted Total Knee Arthroplasty: A Markov Decision Analysis.

The Journal of arthroplasty
BACKGROUND: Robot-assisted total knee arthroplasty (rTKA) may improve clinical outcomes for patients who have end-stage osteoarthritis of the knee. However, the costs of rTKA are high, and there is a paucity of data evaluating the cost-effectiveness ...

Standardized Fixation Zones and Cone Assessments for Revision Total Knee Arthroplasty Using Deep Learning.

The Journal of arthroplasty
BACKGROUND: Achieving adequate implant fixation is critical to optimize survivorship and postoperative outcomes after revision total knee arthroplasty (rTKA). Three anatomical zones (ie, epiphysis, metaphysis, and diaphysis) have been proposed to ass...

The Impact of Robotic-Assisted Total Knee Arthroplasty on Resident Training.

The Journal of arthroplasty
BACKGROUND: As robotic-assisted total knee replacement (rTKA) continues to gain popularity, the impact of this technology on resident education remains unknown. The purpose of this study was to describe trainee experience and perceptions of rTKA and ...