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Osteoarthritis, Hip

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Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints.

Acta orthopaedica
Background and purpose - Machine learning (ML) techniques are a form of artificial intelligence able to analyze big data. Analyzing the outcome of (digital) questionnaires, ML might recognize different patterns in answers that might relate to differe...

Detecting hip osteoarthritis on clinical CT: a deep learning application based on 2-D summation images derived from CT.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
UNLABELLED: We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large train...

Weight-shifting-based robot control system improves the weight-bearing rate and balance ability of the static standing position in hip osteoarthritis patients: a randomized controlled trial focusing on outcomes after total hip arthroplasty.

PeerJ
BACKGROUND: After a total hip arthroplasty (THA), standing and walking balance are greatly affected in the early stages of recovery, so it is important to increase the weight-bearing amount (WBA) on the operated side. Sometimes, traditional treatment...

Classification of inertial sensor-based gait patterns of orthopaedic conditions using machine learning: A pilot study.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making. Therefore, we investigated whether gait pattern...

Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographs.

International journal of computer assisted radiology and surgery
PURPOSE: Progression of hip osteoarthritis (hip OA) leads to pain and disability, likely leading to surgical treatment such as hip arthroplasty at the terminal stage. The severity of hip OA is often classified using the Crowe and Kellgren-Lawrence (K...

Hip contact forces can be predicted with a neural network using only synthesised key points and electromyography in people with hip osteoarthritis.

Osteoarthritis and cartilage
OBJECTIVE: To develop and validate a neural network to estimate hip contact forces (HCF), and lower body kinematics and kinetics during walking in individuals with hip osteoarthritis (OA) using synthesised anatomical key points and electromyography. ...

Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study.

BMC musculoskeletal disorders
BACKGROUND: Predicting the progression of hip osteoarthritis (OA) remains challenging, and no reliable predictive method has been established. This study aimed to develop an artificial intelligence (AI) model to predict hip OA progression via plain r...

Applying Machine Learning to Gait Analysis Data for Hip Osteoarthritis Diagnosis.

Studies in health technology and informatics
BACKGROUND: Hip osteoarthritis (OA) is a degenerative joint disease that affects approximately 25% of individuals over their lifetime, with prevalence expected to rise due to population aging. Gait analysis is recognized as a valuable tool for unders...

Automated determination of hip arthrosis on the Kellgren-Lawrence scale in pelvic digital radiographs scans using machine learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automated analysis of digital radiographs of the pelvis to determine the hip arthrosis state in concordance with the Kellgren-Lawrence scale could facilitate and standardize radiogram descriptions.