AIMC Topic: Arthroplasty, Replacement, Hip

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Errors in femoral anteversion, femoral offset, and vertical offset following robot-assisted total hip arthroplasty.

The international journal of medical robotics + computer assisted surgery : MRCAS Apr 1, 2020
The objectives were to determine errors in femoral anteversion (FA), femoral offset (FO), and vertical offset (VO) with robot-assisted total hip arthroplasty (THA) and how consistently these errors are within clinically desirable limits of ±5° and ±5...
Robotics Arthroplasty, Replacement, Hip Femur Hip Prosthesis Humans Tomography, X-Ray Computed
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Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society Feb 11, 2020
Identifying the design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time-consuming and prone to error. Failure to identify...
Deep Learning Radiography Prosthesis Design Hip Joint Aged, 80 and over Male Middle Aged Arthroplasty, Replacement, Hip Hip Prosthesis Humans Retrospective Studies Female Aged
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Assessment of utilization efficiency using machine learning techniques: A study of heterogeneity in preoperative healthcare utilization among super-utilizers.

American journal of surgery Jan 27, 2020
INTRODUCTION: In the United States, 5% of patients represent up to 55% of all health care costs. This study sought to define healthcare utilization patterns among super-utilizers, as well as assess possible variation in patient outcomes.
Aged Aged, 80 and over Male Cohort Studies Humans Machine Learning Female Arthroplasty, Replacement, Hip Preoperative Period Patient Acceptance of Health Care United States Treatment Outcome Medicare Arthroplasty, Replacement, Knee
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A reliable time-series method for predicting arthritic disease outcomes: New step from regression toward a nonlinear artificial intelligence method.

Computer methods and programs in biomedicine Jan 9, 2020
BACKGROUND AND OBJECTIVE: The interrupted time-series (ITS) concept is performed using linear regression to evaluate the impact of policy changes in public health at a specific time. Objectives of this study were to verify, with an artificial intelli...
Arthroplasty, Replacement, Hip Hip Knee Interrupted Time Series Analysis Arthritis, Rheumatoid Outcome Assessment, Health Care Arthroplasty, Replacement, Knee Linear Models Artificial Intelligence Research Design Incidence Humans
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Fully automatic estimation of pelvic sagittal inclination from anterior-posterior radiography image using deep learning framework.

Computer methods and programs in biomedicine Dec 23, 2019
BACKGROUND AND OBJECTIVE: Malposition of the acetabular component causes dislocation and prosthetic impingement after Total Hip Arthroplasty (THA), which significantly affects the postoperative quality of life and implant longevity. The position of t...
Neural Networks, Computer Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Pelvis Humans Automation Arthroplasty, Replacement, Hip
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Artificial neural network metamodel for sensitivity analysis in a total hip replacement health economic model.

Expert review of pharmacoeconomics & outcomes research Sep 13, 2019
: Metamodels have been used to approximate complex simulations and have many applications with sensitivity analysis, optimization, etc. However, their use in health economics is very limited. Application of artificial neural network (ANN) with a heal...
Computer Simulation Models, Economic Quality-Adjusted Life Years Linear Models Arthroplasty, Replacement, Hip Normal Distribution Humans Neural Networks, Computer
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Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling.

IEEE transactions on medical imaging Sep 10, 2019
We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to th...
Muscle, Skeletal Thigh Bayes Theorem Arthroplasty, Replacement, Hip Hip Patient-Specific Modeling Female Male Deep Learning Image Processing, Computer-Assisted Humans Neural Networks, Computer Tomography, X-Ray Computed
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What Can We Expect Following Anterior Total Hip Arthroplasty on a Regular Operating Table? A Validation Study of an Artificial Intelligence Algorithm to Monitor Adverse Events in a High-Volume, Nonacademic Setting.

The Journal of arthroplasty Aug 3, 2019
BACKGROUND: Quality monitoring is increasingly important to support and assure sustainability of the orthopedic practice. Surgeons in nonacademic settings often lack resources to accurately monitor quality of care. Widespread use of electronic medica...
Humans Artificial Intelligence Algorithms Electronic Health Records Postoperative Complications Arthroplasty, Replacement, Hip Operating Tables
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Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial.

The Journal of arthroplasty Jul 24, 2019
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...
Osteoarthritis, Hip Monitoring, Ambulatory Postoperative Period Arthroplasty, Replacement, Hip Osteoarthritis, Knee Range of Motion, Articular Arthroplasty, Replacement, Knee Patient Reported Outcome Measures Machine Learning Algorithms Prospective Studies Female Humans Wearable Electronic Devices Signal Processing, Computer-Assisted Knee Joint Outcome Assessment, Health Care Aged Male Middle Aged Pilot Projects
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Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty.

The Journal of arthroplasty Jun 13, 2019
BACKGROUND: Postoperative recovery after total hip arthroplasty (THA) can lead to the development of prolonged opioid use but there are few tools for predicting this adverse outcome. The purpose of this study is to develop machine learning algorithms...
Arthroplasty, Replacement, Hip Middle Aged Prescriptions Risk Assessment Pain, Postoperative Humans Logistic Models Analgesics, Opioid Retrospective Studies Machine Learning Opioid-Related Disorders Female Algorithms Postoperative Period Aged Male
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