AI Medical Compendium Topic

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Arthroplasty, Replacement, Hip

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Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs.

The Journal of arthroplasty
BACKGROUND: Dislocation is a common complication following total hip arthroplasty (THA), and accounts for a high percentage of subsequent revisions. The purpose of this study is to illustrate the potential of a convolutional neural network model to a...

Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning.

Scientific reports
Pain is an undesirable sensory experience that can induce depression and limit individuals' activities of daily living, in turn negatively impacting the labor force. Affected people frequently feel pain during activity; however, pain is subjective an...

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
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
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
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)
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
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...

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
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
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...