Background And Purpose: We developed a marker-free automated CT-based spatial analysis (CTSA) method to detect stem-bone migration in consecutive CT datasets and assessed the accuracy and precision in vitro. Our aim was to demonstrate that in vitro accuracy and precision of CTSA is comparable to that of radiostereometric analysis (RSA).

Material And Methods: Stem and bone were segmented in 2 CT datasets and both were registered pairwise. The resulting rigid transformations were compared and transferred to an anatomically sound coordinate system, taking the stem as reference. This resulted in 3 translation parameters and 3 rotation parameters describing the relative amount of stem-bone displacement, and it allowed calculation of the point of maximal stem migration. Accuracy was evaluated in 39 comparisons by imposing known stem migration on a stem-bone model. Precision was estimated in 20 comparisons based on a zero-migration model, and in 5 patients without stem loosening.

Results: Limits of the 95% tolerance intervals (TIs) for accuracy did not exceed 0.28 mm for translations and 0.20° for rotations (largest standard deviation of the signed error (SD(SE)): 0.081 mm and 0.057°). In vitro, limits of the 95% TI for precision in a clinically relevant setting (8 comparisons) were below 0.09 mm and 0.14° (largest SD(SE): 0.012 mm and 0.020°). In patients, the precision was lower, but acceptable, and dependent on CT scan resolution.

Interpretation: CTSA allows detection of stem-bone migration with an accuracy and precision comparable to that of RSA. It could be valuable for evaluation of subtle stem loosening in clinical practice.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4812075PMC
http://dx.doi.org/10.3109/17453674.2015.1123569DOI Listing

Publication Analysis

Top Keywords

accuracy precision
16
ct-based spatial
8
spatial analysis
8
vitro accuracy
8
precision comparable
8
comparable radiostereometric
8
radiostereometric analysis
8
stem-bone migration
8
stem migration
8
migration accuracy
8

Similar Publications

The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks.

View Article and Find Full Text PDF

Development and Validation of KCPREDICT: A Deep Learning Model for Early Detection of Coronary Artery Lesions in Kawasaki Disease Patients.

Pediatr Cardiol

January 2025

Department of Infectious Disease, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, No. 1678 Dongfang Road, Pudong New Area, Shanghai, 200127, China.

Kawasaki disease (KD) is a febrile vasculitis disorder, with coronary artery lesions (CALs) being the most severe complication. Early detection of CALs is challenging due to limitations in echocardiographic equipment (UCG). This study aimed to develop and validate an artificial intelligence algorithm to distinguish CALs in KD patients and support diagnostic decision-making at admission.

View Article and Find Full Text PDF

Flexible Tactile Sensors with Self-Assembled Cilia Based on Magnetoelectric Composites.

ACS Appl Mater Interfaces

January 2025

School of Precision Instrument and Optoelectronics Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, China.

Traditional tactile sensors are single-function, and it is difficult to meet the needs of applications in complex environments. This paper describes the development and applications of flexible tactile sensors with cilia based on magnetoelectric composites made of neodymium iron boron (NdFeB) microparticles with a silver (Ag) nanoshell in polydimethylsiloxane (PDMS). These sensors adopt the inherent magnetism of NdFeB microparticles and the excellent conductivity of the Ag coating.

View Article and Find Full Text PDF

The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights.

View Article and Find Full Text PDF

Serving as a dedicated process analytical technology (PAT) tool for biomass monitoring and control, the capacitance probe, or dielectric spectroscopy, is showing great potential in robust pharmaceutical manufacturing, especially with the growing interest in integrated continuous bioprocessing. Despite its potential, challenges still exist in terms of its accuracy and applicability, particularly when it is used to monitor cells during stationary and decline phases. In this study, data pre-processing methods were first evaluated through cross-validation, where the first-order derivative emerged as the most effective method to diminish variability in prediction accuracy across different training datasets.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!