AIMC Topic: Arthroplasty, Replacement, Hip

Clear Filters Showing 11 to 20 of 143 articles

Development of a Deep Learning Model for Automating Implant Position in Total Hip Arthroplasty.

The Journal of arthroplasty
BACKGROUND: Novel methods for annotating antero-posterior pelvis radiographs and fluoroscopic images with deep-learning models have recently been developed. However, their clinical use has been limited. Therefore, the purpose of this study was to dev...

Machine learning to predict periprosthetic joint infections following primary total hip arthroplasty using a national database.

Archives of orthopaedic and trauma surgery
INTRODUCTION: Periprosthetic joint infection (PJI) following total hip arthroplasty (THA) remains a devastating complication for patients and surgeons. Given the implications of these infections and the current paucity of risk calculators utilizing m...

Adverse Outcomes after Cemented and Cementless Primary Elective Total Hip Arthroplasty in 60,064 Matched Patients: A Study of Data from the Swedish Arthroplasty Register.

The Journal of arthroplasty
BACKGROUND: The choice between cemented and cementless fixation in primary elective total hip arthroplasty (THA) remains a subject of ongoing debate. However, comparisons between the two are subject to limited adjustments for patient characteristics,...

Perspectives surrounding robotic total hip arthroplasty: a cross-sectional analysis using natural language processing.

Canadian journal of surgery. Journal canadien de chirurgie
BACKGROUND: Robotic technology has been used in total hip arthroplasty (THA) for several years. Despite the advances in this field, perspectives surrounding robotic THA are not fully understood. This study aimed to characterize the landscape of robot...

Predicting Early Hospital Discharge Following Revision Total Hip Arthroplasty: An Analysis of a Large National Database Using Machine Learning.

The Journal of arthroplasty
BACKGROUND: Revision total hip arthroplasty (rTHA) was recently removed from the Medicare inpatient-only list. However, appropriate candidate selection for outpatient rTHA remains paramount. The purpose of this study was to evaluate the utility of a ...

Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study.

BMC musculoskeletal disorders
BACKGROUND: Predicting the progression of hip osteoarthritis (OA) remains challenging, and no reliable predictive method has been established. This study aimed to develop an artificial intelligence (AI) model to predict hip OA progression via plain r...

Utilization of Machine Learning Models to More Accurately Predict Case Duration in Primary Total Joint Arthroplasty.

The Journal of arthroplasty
BACKGROUND: Accurate operative scheduling is essential for the appropriation of operating room esources. We sought to implement a machine learning model to predict primary total hip arthroplasty (THA) and total knee arthroplasty (TKA) case time.

Racial and Ethnic Disparities in Predictive Accuracy of Machine Learning Algorithms Developed Using a National Database for 30-Day Complications Following Total Joint Arthroplasty.

The Journal of arthroplasty
BACKGROUND: While predictive capabilities of machine learning (ML) algorithms for hip and knee total joint arthroplasty (TJA) have been demonstrated in previous studies, their performance in racial and ethnic minority patients has not been investigat...

Prediction of intraoperative press-fit stability of the acetabular cup in total hip arthroplasty using radiomics-based machine learning models.

European journal of radiology
BACKGROUND: Preoperative prediction of the acetabular cup press-fit stability in total hip arthroplasty is necessary for clinical decision-making. This study aims to establish and validate machine learning models to investigate the feasibility of pre...