Three dimensional measurement of minimum joint space width in the knee from stereo radiographs using statistical shape models

IJsseldijk, E.A. van; Valstar, Edward R.; Stoel, Berend C.; Nelissen, Rob G H H; Baka, Nóra; Klooster, Ronald van’t; Kaptein, Bart L

doi:10.1302/2046-3758.58.2000626

Cited by 13 publications

(10 citation statements)

References 27 publications

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“…For example, it is extensively used for organ segmentation, [36][37][38] to extract morphological bio-markers of (diseased) organs, [39][40][41] or for numerous orthopedic applications. [42][43][44][45][46][47] Although few, several studies-mainly in the field of orthopedics-attempted to relate SSM parameterized anatomical shape variation with Finite Element Analysis (FEA). These studies used SSM and FEA to: investigate the relationship between patellofemoral shape and function 48 ; force-displacement behavior of proximal femurs 49 ; to investigate cervical spine loading, 50 or for real-time prediction of joint-mechanics.…”

Section: Discussionmentioning

confidence: 99%

“…The large number of publications in various bio‐medical fields proves that statistical shape modeling is a versatile and widely accepted technique to capture anatomic variation in the population. For example, it is extensively used for organ segmentation, 36‐38 to extract morphological bio‐markers of (diseased) organs, 39‐41 or for numerous orthopedic applications 42‐47 . Although few, several studies—mainly in the field of orthopedics—attempted to relate SSM parameterized anatomical shape variation with Finite Element Analysis (FEA).…”

Section: Discussionmentioning

confidence: 99%

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Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

Hoeijmakers

Waechter-Stehle

Weese

et al. 2020

Numer Methods Biomed Eng

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Background: Advances in medical imaging, segmentation techniques, and high performance computing have stimulated the use of complex, patient-specific, three-dimensional Computational Fluid Dynamics (CFD) simulations. Patient-specific, CFD-compatible geometries of the aortic valve are readily obtained. CFD can then be used to obtain the patient-specific pressure-flow relationship of the aortic valve. However, such CFD simulations are computationally expensive, and real-time alternatives are desired. Aim: The aim of this work is to evaluate the performance of a meta-model with respect to high-fidelity, three-dimensional CFD simulations of the aortic valve. Methods: Principal component analysis was used to build a statistical shape model (SSM) from a population of 74 iso-topological meshes of the aortic valve. Synthetic meshes were created with the SSM, and steady-state CFD simulations at flow-rates between 50 and 650 mL/s were performed to build a metamodel. The meta-model related the statistical shape variance, and flow-rate to the pressure-drop. Results: Even though the first three shape modes account for only 46% of shape variance, the features relevant for the pressure-drop seem to be captured. The three-mode shape-model approximates the pressure-drop with an average error of 8.8% to 10.6% for aortic valves with a geometric orifice area below 150 mm 2. The proposed methodology was least accurate for aortic valve areas above 150 mm 2. Further reduction to a meta-model introduces an additional 3% error.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

Hoeijmakers

Waechter-Stehle

Weese

et al. 2020

Numer Methods Biomed Eng

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show abstract

“…Despite showing some promise, there are still some issues with SSMs, and the accuracy of the method has yet to reach the high standard of RSA. 13,15,33 Currently, a completely markerless RSA is not feasible, and future research efforts should concern this issue.…”

Section: Discussionmentioning

confidence: 99%

Radiostereometric analysis using clinical radiographic views: Validation with model-based radiostereometric analysis for the knee

Yuan

Broberg

Naudie

et al. 2018

Proc Inst Mech Eng H

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Radiostereometric analysis is a sophisticated radiographic technique with high measurement accuracy. In order to improve the accessibility of radiostereometric analysis for clinical use, a modified radiostereometric analysis procedure has been previously proposed that enables clinical radiographic views to be used for radiostereometric analysis. It has been successfully validated for its application to the hip wear study with the conventional bead-based radiostereometric analysis environment using computed radiography. In this study, we describe the implementation and validation of this technique for the knee study with the model-based radiostereometric analysis environment using digital radiography. A knee-joint phantom with 6 degrees of freedom was examined, and the bias and repeatability/reproducibility of the modified radiostereometric analysis approach were investigated following the newly updated ASTM recommendations. The bias parameters (mean ± 95% confidence interval) ranged from 0.008 ± 0.003 mm to 0.027 ± 0.006 mm for translation and from 0.014° ± 0.007° to 0.040° ± 0.020° for rotation. The repeatability standard deviation ranged from 0.004 to 0.020 mm for translation and from 0.005° to 0.015° for rotation. The 95% repeatability limit ranged from 0.011 to 0.055 mm for translation and from 0.014° to 0.041° for rotation. The reproducibility standard deviation ranged from 0.004 to 0.023 mm for translation and from 0.006° to 0.040° for rotation. The 95% reproducibility limit ranged from 0.012 to 0.063 mm for translation and from 0.016° to 0.112° for rotation. The modified procedure allows routine clinical radiographs to be used for radiostereometric analysis, which provides the possibility of adding quantitative measurements to current patient registries.

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“…Heart surgeons are assisted by heart lander, a miniature robot that enters a small incision on the chest to perform mapping and therapy over the surface of the heart. Machine learning has been instrumental in orthopaedics, in terms of understanding biomechanics, orthopaedic implant design [107], prediction of progression of osteoarthritis [108] and robotic surgery [109].…”

Section: The Hf Paradigm Shift To Pppmmentioning

confidence: 99%

Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care

et al. 2019

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Heart failure (HF) is one of the most complex chronic disorders with high prevalence, mainly due to the ageing population and better treatment of underlying diseases. Prevalence will continue to rise and is estimated to reach 3% of the population in Western countries by 2025. It is the most important cause of hospitalisation in subjects aged 65 years or more, resulting in high costs and major social impact. The current “one-size-fits-all” approach in the treatment of HF does not result in best outcome for all patients. These facts are an imminent threat to good quality management of patients with HF. An unorthodox approach from a new vision on care is required. We propose a novel predictive, preventive and personalised medicine approach where patients are truly leading their management, supported by an easily accessible online application that takes advantage of artificial intelligence. This strategy paper describes the needs in HF care, the needed paradigm shift and the elements that are required to achieve this shift. Through the inspiring collaboration of clinical and high-tech partners from North-West Europe combining state of the art HF care, artificial intelligence, serious gaming and patient coaching, a virtual doctor is being created. The results are expected to advance and personalise self-care, where standard care tasks are performed by the patients themselves, in principle without involvement of healthcare professionals, the latter being able to focus on complex conditions. This new vision on care will significantly reduce costs per patient while improving outcomes to enable long-term sustainability of top-level HF care.

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Three dimensional measurement of minimum joint space width in the knee from stereo radiographs using statistical shape models

Cited by 13 publications

References 27 publications

Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

Radiostereometric analysis using clinical radiographic views: Validation with model-based radiostereometric analysis for the knee

Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care

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