Abstract：
:Currently, there are no clinically available tools or applications which could predict osteoarthritis development. Some computational models have been presented to simulate cartilage degeneration, but they are not clinically feasible due to time required to build subject-specific knee models. Therefore, the objective of this study was to develop a template-based modeling method for rapid prediction of knee joint cartilage degeneration. Knee joint models for 21 subjects were constructed with two different template approaches (multiple templates and one template) based on the MRI data. Geometries were also generated by manual segmentation. Evaluated volumes of cartilage degeneration for each subject, as assessed with the degeneration algorithm, were compared with experimentally observed 4 year follow up Kellgren-Lawrence (KL) grades. Furthermore, the effect of meniscus was tested by generating models with subject-specific meniscal supporting forces and those with the average meniscal supporting force from all models. All tested models were able to predict most severe cartilage degeneration to those subjects who had the highest KL grade after 4 year follow up. Surprisingly, in terms of statistical significance, the best result was obtained with one template approach and average meniscal support. This model was fully able to categorize all subjects to their experimentally defined groups (KL0, KL2 and KL3) based on the 4 year follow-up data. The results suggest that a template- or population-based approach, which is much faster than fully subject-specific, could be applied as a clinical prediction tool for osteoarthritis.
journal_name
Ann Biomed Engjournal_title
Annals of biomedical engineeringauthors
Mononen ME,Liukkonen MK,Korhonen RKdoi
10.1007/s10439-018-02184-ysubject
Has Abstractpub_date
2019-03-01 00:00:00pages
813-825issue
3eissn
0090-6964issn
1573-9686pii
10.1007/s10439-018-02184-yjournal_volume
47pub_type
杂志文章abstract：:Instrumentation systems for breath-to-breath analysis of respiratory gas exchange have been faulted by phase lags between various flow and composition signals and by difficulties in gathering and processing large amounts of data. The system described here represents an attempt to overcome these problems. Phase delays ...
journal_title：Annals of biomedical engineering
pub_type： 杂志文章
doi：10.1007/BF02367493
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
pub_type： 杂志文章
doi：10.1114/1.1349698
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journal_title：Annals of biomedical engineering
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doi：10.1007/s10439-010-0108-1
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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doi：10.1007/s10439-010-0120-5
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journal_title：Annals of biomedical engineering
pub_type： 杂志文章
doi：10.1007/BF02368460
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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journal_title：Annals of biomedical engineering
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doi：10.1114/1.110
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journal_title：Annals of biomedical engineering
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更新日期：2004-08-01 00:00:00
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journal_title：Annals of biomedical engineering
pub_type： 杂志文章
doi：10.1114/1.1616932
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