AIMC Topic: Arthroplasty, Replacement, Knee

Clear Filters Showing 1 to 10 of 224 articles

Measuring provider-level differences in perioperative workflow using computer vision-based artificial intelligence.

BMJ health & care informatics
OBJECTIVES: To evaluate provider-level variability across the full perioperative workflow using a computer vision-based artificial intelligence (AI) system that automatically detects and timestamps operating room events.

A comparative analysis of ChatGPT and Google in providing quality and clinical relevance of responses to patients' frequently asked questions on robotic-assisted total knee arthroplasty.

Archives of orthopaedic and trauma surgery
INTRODUCTION: The purpose of this study was to identify the most frequent questions a patient might encounter in an internet search about robotic-assisted total knee arthroplasty (RATKA), and to identify and categorize the answers to these questions ...

Using Machine Learning to Predict-Then-Optimize Elective Orthopedic Surgery Scheduling to Improve Operating Room Utilization: Retrospective Study.

JMIR medical informatics
BACKGROUND: Total knee and hip arthroplasty (TKA and THA) are among the most performed elective procedures. Rising demand and the resource-intensive nature of these procedures have contributed to longer wait times despite significant health care inve...

Distinct 3-Dimensional Morphologies of Arthritic Knee Anatomy Exist: CT-Based Phenotyping Offers Outlier Detection in Total Knee Arthroplasty.

The Journal of bone and joint surgery. American volume
BACKGROUND: There is no foundational classification that 3-dimensionally characterizes arthritic anatomy to preoperatively plan and postoperatively evaluate total knee arthroplasty (TKA). With the advent of computed tomography (CT) as a preoperative ...

The future of robotic surgery in joint arthroplasty : Current platforms, emerging technologies and outlook for 2035.

Orthopadie (Heidelberg, Germany)
Robotic-assisted total joint arthroplasty shows clear evidence of improved component positioning across all applications with modest functional improvements at early to mid-term follow-up. The strongest economic value proposition exists for partial k...

A development of machine learning models to preoperatively predict insufficient clinical improvement after total knee arthroplasty.

Journal of orthopaedic surgery and research
BACKGROUND: Identifying patients unlikely to achieve meaningful improvement following total knee arthroplasty (TKA) supports more effective shared decision-making (SDM). This study aimed to develop and validate machine learning (ML) models that preop...

Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms.

BMC anesthesiology
BACKGROUND: With the aging demographic on the rise, we're seeing a spike in the occurrence of postoperative delirium (POD). Our research aims to delve into the connection between plasma bilirubin levels and postoperative delirium, with the goal of cr...

[Update 2025: Biomechanics and kinematics after total knee arthroplasty (TKA)].

Orthopadie (Heidelberg, Germany)
BACKGROUND: In order to optimise clinical outcomes after primary total knee arthroplasty (TKA), research has refocused on the knee joint's biomechanical characteristics. Beyond implant design and alignment philosophy, the restoration of natural joint...

Fully automated workflow for designing patient-specific orthopaedic implants: Application to total knee arthroplasty.

PloS one
Background Osteoarthritis affects about 528 million people worldwide, causing pain and stiffness in the joints. Arthroplasty is commonly performed to treat joint osteoarthritis, reducing pain and improving mobility. Nevertheless, a significant share ...