Results 151 to 160 of about 7,612,198 (301)

Clinical Validation of Artificial Intelligence (AI)‐based Cartilage Segmentation Predicting Knee Replacement

Arthritis Care &Research, Accepted Article.
Objective For cartilage morphology to serve as a scalable endpoint in clinical trials, analyses should be performed automatically without human interaction. To clinically validate artificial intelligence (AI)‐based analysis, we studied cartilage loss from MRI prior to knee replacement.
Felix Eckstein, David J. Hunter, C. Kent Kwoh, Wolfgang Wirth   +3 more
wiley   +1 more source

Acoustic emission in compressed Mg-Li and Al alloys processed by ECAP, HPT and ARB methods

Archives of Acoustics, 2014
In the paper there are presented the results of investigations of the relations between acoustic emission (AE) and the plastic strain mechanisms during channel-die compression of materials, based on both Mg-Li and Al alloys, having ultra fine-grained ...
Andrzej Pawełek, Andrzej Piątkowski, Jan Kuśnierz, Joanna Bogucka, Zdzisław Jasieński, Zbigniew Ranachowski, Przemysław Ranachowski, Jarosław Mizera, Stanislav Kúdela, Stanislaw Kúdela Jr.   +9 more
doaj  

Automated Hand Flexor Tendon–Thickness Measurement in Systemic Sclerosis

Arthritis Care &Research, EarlyView.
Objective Systemic sclerosis (SSc) can affect flexor tendons, contributing to hand function problems and reduced quality of life. Tendon changes are currently assessed with ultrasonography and measured manually, a time‐consuming process prone to interobserver variability.
Mark Greveling, Ties van het Erve, Yvette van de Berg, Simon Mastbergen, Julia Spierings   +4 more
wiley   +1 more source

A Bedside Compression Test to Differentiate Pulsatile Varicose Veins. [PDF]

Clin Case Rep
Ida M, Ueda T.
europepmc   +1 more source

Effect of CNTs Additives on the Energy Balance of Carbon/Epoxy Nanocomposites during Dynamic Compression Test. [PDF]

Polymers (Basel), 2020
Chihi M, Tarfaoui M, Bouraoui C, El Moumen A.   +3 more
europepmc   +1 more source

Unsupervised Machine Learning Identifies Shared Biomechanical and Clinical Variation in Individuals with Knee Osteoarthritis and High BMI: A Secondary Analysis of the IDEA Trial

Arthritis Care &Research, Accepted Article.
Objective High body mass index (BMI≥27 kg/m2) and aberrant gait biomechanics are primary risk factors for knee osteoarthritis (KOA), yet the specific loading patterns linked to clinical disease features remain unclear. Unsupervised machine learning (ML) was used to identify vertical ground reaction force (vGRF) waveform modes of variation and ...
Ashley N. Buck, J. Elyse Borgert, Hyeon Lee, Liubov Arbeeva, Yvonne M. Golightly, Richard F. Loeser, Leigh F. Callahan, Stephen P. Messier, Brian Pietrosimone, Amanda E. Nelson, James S. Marron   +10 more
wiley   +1 more source

Quasi-static compression test dataset of sinusoidally-corrugated multi-cell PLA+ energy absorbers fabricated by fused deposition modeling (FDM). [PDF]

Data Brief
Saber A, Altin M, Acar E, Güler MA.
europepmc   +1 more source

The content and reporting completeness of physical therapist‐led interventions for glenohumeral osteoarthritis: a systematic review

Arthritis Care &Research, Accepted Article.
Objectives This study synthesised the content and reporting completeness of published physical therapist‐led interventions for glenohumeral osteoarthritis (GHOA). Methods This systematic review undertook electronic database searches in PubMed, EMBASE, Cochrane, CINAHL and PEDro (until June 2025) for any peer‐reviewed articles/studies describing ...
Daha G Muhammad, Nadine E Foster, Mickylle Pelaez, Karina O'Leary, Ilana N Ackerman, Jonathan G Quicke   +5 more
wiley   +1 more source

Experimental study of the mechanical behavior of oriented bimrocks under diametral compression test using DIC. [PDF]

Sci Rep
Rostamlo-Jooshin R, Bahaaddini M, Khosravi MH.   +2 more
europepmc   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source