AI Medical Compendium Topic

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Arthroplasty, Replacement, Hip

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Artificial Intelligence Autonomously Measures Cup Orientation, Corrects for Pelvis Orientation, and Identifies Retroversion From Antero-Posterior Pelvis Radiographs.

The Journal of arthroplasty
BACKGROUND: Measuring cup orientation is time consuming and inaccurate, but orientation influences the risk of impingement and dislocation following total hip arthroplasty (THA). This study designed an artificial intelligence (AI) program to autonomo...

Hip arthroplasty dislocation risk calculator: evaluation of one million primary implants and twenty-five thousand dislocations with deep learning artificial intelligence in a systematic review of reviews.

International orthopaedics
PURPOSE: This paper aims to provide an overview of the possibility regarding the artificial intelligence application in orthopaedics to predict dislocation with a calculator according to the type of implant (hemiarthroplasty, standard total hip arthr...

Status of robot-assisted artificial total joint arthroplasty in China: a cross-sectional survey of joint surgeons.

International orthopaedics
PURPOSE: The purpose of this study is to report on the use of Robotic-assisted total joint arthroplasty (RA-TJA) in China as well as the experience and expectations of Chinese doctors regarding this technology.

Is Robotic-Assisted Technology Still Accurate in Total Hip Arthroplasty for Fibrous-Fused Hips?

The Journal of arthroplasty
BACKGROUND: Total hip arthroplasty (THA) for fibrous-fused hips is technically demanding. This study aimed to evaluate the precision and accuracy, as well as the rate of conversion of robotic-assisted THA in such difficult patients.

Applying Deep Learning to Establish a Total Hip Arthroplasty Radiography Registry: A Stepwise Approach.

The Journal of bone and joint surgery. American volume
BACKGROUND: Establishing imaging registries for large patient cohorts is challenging because manual labeling is tedious and relying solely on DICOM (digital imaging and communications in medicine) metadata can result in errors. We endeavored to estab...

Deep Learning-Based Postoperative Recovery and Nursing of Total Hip Arthroplasty.

Computational and mathematical methods in medicine
OBJECTIVE: To develop a deep learning-assisted recovery and nursing system after total hip arthroplasty and to conduct clinical trials in order to verify its accuracy.

Detecting total hip arthroplasty dislocations using deep learning: clinical and Internet validation.

Emergency radiology
OBJECTIVE: Periprosthetic dislocations of total hip arthroplasty (THA) are time-sensitive injuries, as the longer diagnosis and treatment are delayed, the more difficult they are to reduce. Automated triage of radiographs with dislocations could help...

Automatic prosthetic-parameter estimation from anteroposterior pelvic radiographs after total hip arthroplasty using deep learning-based keypoint detection.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: X-ray is a necessary tool for post-total hip arthroplasty (THA) check-ups; however, parameter measurements are time-consuming. We proposed a deep learning tool, BKNet that automates localization of landmarks with parameter measurements.

John Charnley Award: Deep Learning Prediction of Hip Joint Center on Standard Pelvis Radiographs.

The Journal of arthroplasty
BACKGROUND: Accurate hip joint center (HJC) determination is critical for preoperative planning, intraoperative execution, clinical outcomes after total hip arthroplasty, and commonly used classification systems in primary and revision hip replacemen...