AIMC Topic: Femoracetabular Impingement

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Using Google web search to analyze and evaluate the application of ChatGPT in femoroacetabular impingement syndrome.

Frontiers in public health May 31, 2024
BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT) is a new machine learning tool that allows patients to access health information online, specifically compared to Google, the most commonly used search engine in the United States. Patient...
Humans Machine Learning Female Internet Search Engine Femoracetabular Impingement Cross-Sectional Studies Male Adult
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The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods.

Joint diseases and related surgery Apr 26, 2023
OBJECTIVES: The aim of this study was to evaluate diagnostic ability of deep learning models, particularly convolutional neural network models used for image classification, for femoroacetabular impingement (FAI) using hip radiographs.
Pelvis Female Femoracetabular Impingement Adult Deep Learning Radiography Male Humans Retrospective Studies
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3D MRI Models of the Musculoskeletal System.

Seminars in musculoskeletal radiology Sep 21, 2021
Computed tomography (CT) is most commonly used to produce three-dimensional (3D) models for evaluating bone and joint morphology in clinical practice. However, 3D models created from magnetic resonance imaging (MRI) data can be equally effective for ...
Imaging, Three-Dimensional Magnetic Resonance Imaging Femoracetabular Impingement Knee Joint Humans Artificial Intelligence
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Radiographic Indices Are Not Predictive of Clinical Outcomes Among 1735 Patients Indicated for Hip Arthroscopic Surgery: A Machine Learning Analysis.

The American journal of sports medicine Sep 14, 2020
BACKGROUND: The relationship between the preoperative radiographic indices for femoroacetabular impingement syndrome (FAIS) and postoperative patient-reported outcome measure (PROM) scores continues to be under investigation, with inconsistent findin...
Cohort Studies Femoracetabular Impingement Treatment Outcome Humans Hip Joint Patient Reported Outcome Measures Machine Learning Minimal Clinically Important Difference Arthroscopy Activities of Daily Living
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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...
Adult Range of Motion, Articular Arthroplasty, Replacement, Hip Femur Hip Joint Neural Networks, Computer Algorithms Humans Reproducibility of Results Femoracetabular Impingement Joint Dislocations Finite Element Analysis
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Application of Machine Learning for Predicting Clinically Meaningful Outcome After Arthroscopic Femoroacetabular Impingement Surgery.

The American journal of sports medicine Dec 23, 2019
BACKGROUND: Hip arthroscopy has become an important tool for surgical treatment of intra-articular hip pathology. Predictive models for clinically meaningful outcomes in patients undergoing hip arthroscopy for femoroacetabular impingement syndrome (F...
Femoracetabular Impingement Middle Aged Case-Control Studies Treatment Outcome Adult Male Arthroscopy Hip Joint Body Mass Index Minimal Clinically Important Difference Humans Activities of Daily Living Female Machine Learning Young Adult
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Machine Learning Algorithms Predict Functional Improvement After Hip Arthroscopy for Femoroacetabular Impingement Syndrome in Athletes.

The Journal of bone and joint surgery. American volume Jun 16, 2021
BACKGROUND: Despite previous reports of improvements for athletes following hip arthroscopy for femoroacetabular impingement syndrome (FAIS), many do not achieve clinically relevant outcomes. The purpose of this study was to develop machine learning ...
Female Young Adult Adult Machine Learning Algorithms Recovery of Function Predictive Value of Tests Arthroscopy Male Treatment Outcome Humans Femoracetabular Impingement Sports Minimal Clinically Important Difference
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Deep Learning-Based Automatic Segmentation of the Proximal Femur from MR Images.

Advances in experimental medicine and biology Jan 1, 2018
This chapter addresses the problem of segmentation of proximal femur in 3D MR images. We propose a deeply supervised 3D U-net-like fully convolutional network for segmentation of proximal femur in 3D MR images. After training, our network can directl...
Femoracetabular Impingement Imaging, Three-Dimensional Magnetic Resonance Imaging Humans Femur Deep Learning
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