AIMC Topic: Arthroplasty, Replacement, Hip
Clear Filters Showing 11 to 20 of 137 articles
Predicting Early Hospital Discharge Following Revision Total Hip Arthroplasty: An Analysis of a Large National Database Using Machine Learning.
The Journal of arthroplasty
Dec 9, 2024
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
Nov 8, 2024
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
Oct 28, 2024
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
Oct 20, 2024
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
Sep 19, 2024
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...
Predicting extended hospital stay following revision total hip arthroplasty: a machine learning model analysis based on the ACS-NSQIP database.
Archives of orthopaedic and trauma surgery
Sep 19, 2024
INTRODUCTION: Prolonged length of stay (LOS) following revision total hip arthroplasty (THA) can lead to increased healthcare costs, higher rates of readmission, and lower patient satisfaction. In this study, we investigated the predictive power of m...
Machine learning for predicting duration of surgery and length of stay: A literature review on joint arthroplasty.
International journal of medical informatics
Sep 15, 2024
INTRODUCTION: In recent years, different factors such as population aging have caused escalating demand for hip and knee arthroplasty straining already limited hospitals' resources. To address this challenge, focus is put on medical and operational e...
Deep Learning for Automated Classification of Hip Hardware on Radiographs.
Journal of imaging informatics in medicine
Sep 12, 2024
PURPOSE: To develop a deep learning model for automated classification of orthopedic hardware on pelvic and hip radiographs, which can be clinically implemented to decrease radiologist workload and improve consistency among radiology reports.
Artificial Intelligence-Based Surgery Support Model Using Intraoperative Radiographs for Assessing the Acetabular Component Angle.
The Journal of arthroplasty
Sep 12, 2024
BACKGROUND: This study aimed to develop an artificial intelligence-based surgical support model for assessing the acetabular component angle using intraoperative radiographs during total hip arthroplasty and verify its accuracy.
Machine learning model identifies patient gait speed throughout the episode of care, generating notifications for clinician evaluation.
Gait & posture
Sep 6, 2024
INTRODUCTION: The advent of digital and mobile health innovations, especially use of wearables for passive data collection, allows remote monitoring and creates an abundance of data. For this information to be interpretable, machine learning (ML) pro...
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