AIMC Topic: Arthroplasty, Replacement, Hip
Clear Filters Showing 81 to 90 of 143 articles
Natural language processing with deep learning for medical adverse event detection from free-text medical narratives: A case study of detecting total hip replacement dislocation.
Computers in biology and medicine
Nov 24, 2020
BACKGROUND: Accurate and timely detection of medical adverse events (AEs) from free-text medical narratives can be challenging. Natural language processing (NLP) with deep learning has already shown great potential for analyzing free-text data, but i...
Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip.
The Journal of arthroplasty
Nov 16, 2020
BACKGROUND: The surgical management of complications surrounding patients who have undergone hip arthroplasty necessitates accurate identification of the femoral implant manufacturer and model. Failure to do so risks delays in care, increased morbidi...
Precision and accuracy of robot-assisted technology with simplified express femoral workflow in measuring leg length and offset in total hip arthroplasty.
The international journal of medical robotics + computer assisted surgery : MRCAS
Aug 19, 2020
BACKGROUND: Semi-active robot-assisted total hip arthroplasty (THA) has two options to measure the leg length discrepancy (LLD) and combined offset (CO), the 'enhanced' femoral workflow and the so-called 'express' simplified workflow. The purpose of ...
Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty.
Sensors (Basel, Switzerland)
Aug 6, 2020
Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explai...
Using artificial neural networks to predict impingement and dislocation in total hip arthroplasty.
Computer methods in biomechanics and biomedical engineering
May 4, 2020
Dislocation after total hip arthroplasty (THA) remains a major issue and an important post-surgical complication. Impingement and subsequent dislocation are influenced by the design (head size) and position (anteversion and abduction angles) of the a...
A commentary on "A retrospective study comparing a single surgeon's experience on manual versus robot-assisted total hip arthroplasty after the learning curve of the latter procedure, a cohort study".
International journal of surgery (London, England)
Apr 23, 2020
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...
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...
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.
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...
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