AIMC Topic: Hip Joint

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Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty.

Journal of orthopaedic surgery and research Mar 15, 2022
PURPOSE: Preoperative three-dimensional planning is important for total hip arthroplasty. To simulate the placement of joint implants on computed tomography (CT), pelvis and femur must be segmented. Accurate and rapid segmentation of the hip joint is...
Neural Networks, Computer Tomography, X-Ray Computed Female Aged Humans Pelvis Hip Joint Arthroplasty, Replacement, Hip Preoperative Period Aged, 80 and over Male Middle Aged
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Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification.

Medical image analysis Mar 12, 2022
Morphological abnormalities of the femoroacetabular (hip) joint are among the most common human musculoskeletal disorders and often develop asymptomatically at early easily treatable stages. In this paper, we propose an automated framework for landma...
Magnetic Resonance Imaging Hip Joint Neural Networks, Computer Humans Learning
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Machine learning outperforms clinical experts in classification of hip fractures.

Scientific reports Feb 8, 2022
Hip fractures are a major cause of morbidity and mortality in the elderly, and incur high health and social care costs. Given projected population ageing, the number of incident hip fractures is predicted to increase globally. As fracture classificat...
Radiography Hip Fractures Hip Joint Health Care Costs Humans Machine Learning
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A Deep Learning Approach for MRI in the Diagnosis of Labral Injuries of the Hip Joint.

Journal of magnetic resonance imaging : JMRI Jan 26, 2022
BACKGROUND: The diagnosis of labral injury on MRI is time-consuming and potential for incorrect diagnoses.
Humans Magnetic Resonance Imaging Hip Joint Neural Networks, Computer Retrospective Studies Deep Learning
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Duration of Care and Operative Time Are the Primary Drivers of Total Charges After Ambulatory Hip Arthroscopy: A Machine Learning Analysis.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association Dec 16, 2021
PURPOSE: To develop a machine learning algorithm to predict total charges after ambulatory hip arthroscopy and create a risk-adjusted payment model based on patient comorbidities.
Humans Machine Learning Databases, Factual Operative Time Arthroscopy Hip Joint Retrospective Studies Adult Treatment Outcome
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Abnormal synergistic gait mitigation in acute stroke using an innovative ankle-knee-hip interlimb humanoid robot: a preliminary randomized controlled trial.

Scientific reports Nov 24, 2021
Abnormal spasticity and associated synergistic patterns are the most common neuromuscular impairments affecting ankle-knee-hip interlimb coordinated gait kinematics and kinetics in patients with hemiparetic stroke. Although patients with hemiparetic ...
Gait Disorders, Neurologic Exoskeleton Device Physical Therapy Modalities Stroke Rehabilitation Equipment Design Knee Joint Hip Joint Robotics Humans Stroke Female Ankle Joint Functional Status Time Factors Aged Single-Blind Method Gait Treatment Outcome Male Range of Motion, Articular Recovery of Function Middle Aged Aged, 80 and over Biomechanical Phenomena
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Deep transfer learning can be used for the detection of hip joints in pelvis radiographs and the classification of their hip dysplasia status.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association Apr 5, 2021
Reports of machine learning implementations in veterinary imaging are infrequent but changes in machine learning architecture and access to increased computing power will likely prompt increased interest. This diagnostic accuracy study describes a pa...
Animals Deep Learning Humans Radiography Hip Joint Image Processing, Computer-Assisted Pelvis Mass Screening Hip Dislocation Predictive Value of Tests
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Volumetric quantitative measurement of hip effusions by manual versus automated artificial intelligence techniques: An OMERACT preliminary validation study.

Seminars in arthritis and rheumatism Mar 17, 2021
OBJECTIVE: Preliminary assessment, via OMERACT filter, of manual and automated MRI hip effusion Volumetric Quantitative Measurement (VQM).
Reproducibility of Results Magnetic Resonance Imaging Humans Hip Joint Synovial Fluid Artificial Intelligence
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Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting Clinically Significant Functional Improvement in a Mixed Population of Primary Hip Arthroscopy.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association Jan 16, 2021
PURPOSE: To (1) develop and validate a machine learning algorithm to predict clinically significant functional improvements after hip arthroscopy for femoroacetabular impingement syndrome and to (2) develop a digital application capable of providing ...
Retrospective Studies Female Risk Factors Arthroscopy Activities of Daily Living Young Adult Hip Joint Patient Reported Outcome Measures Humans Calibration Algorithms ROC Curve Adult Male Recovery of Function Treatment Outcome Cohort Studies Supervised Machine Learning
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Improving data acquisition speed and accuracy in sport using neural networks.

Journal of sports sciences Oct 14, 2020
Video analysis is used in sport to derive kinematic variables of interest but often relies on time-consuming tracking operations. The purpose of this study was to determine speed, accuracy and reliability of 2D body landmark digitisation by a neural ...
Time and Motion Studies Biomechanical Phenomena Male Hip Joint Knee Joint Reproducibility of Results Humans Adolescent Shoulder Joint Swimming Young Adult Neural Networks, Computer Adult Female
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