Biomechanical model of knee collateral ligament injury with six degrees of freedom

Özada, Neriman

doi:10.1007/s11517-015-1373-9

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…Eklem momentlerinin hesaplanmasında kullanılan bir diğer yaklaşım simülasyon yazılımlarından faydalanmaktır. Bunlar Anybody, OpenSim [3] ve LifeModeler [4] gibi kas-iskelet sistemi analizleri için özel geliştirilmiş yazılımların yanı sıra MATLAB [5] ve ADAMS [6] gibi genel amaçlı yazımlar olabilmektedir. Bu yazımları kullanarak eklem momentlerini analiz etmek özel bir uzmanlık gerektirmektedir.…”

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Diz Eklemi Momentinin Tahmini İçin Makine Öğrenmesi Yöntemlerinin İncelenmesi

Serbest¹,

Kılıç²

2021

acperpro

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İnsan uzuvlarında meydana gelen eklem momentlerinin belirlenmesi değişik uygulamalar için önemlidir. Önceki çalışmalarda eklem momentleri matematiksel yöntemler, bu alana özgü yazılımlar veya yapay zeka yöntemleri ile belirlenmiştir. Yapay zeka teknikleri ile yapılan tahminlerde çoğunlukla elektromiyografi (EMG) sinyalinden faydalanılmaktadır. Ancak EMG ile çalışmanın bazı dezavantajları bulunmaktadır. Bu çalışmada kütle ve atalet özelliklerine dayalı olarak elde edilen veriler ile farklı makine öğrenmesi algoritmaları kullanılarak diz eklemi momenti tahmin edilmiştir. Oturup kalkma hareketinin analizine yönelik yapılan klinik çalışmamıza 10 adet sağlıklı denek katılmıştır. Deneklerden elde edilen veriler doğrusal regresyon, destek vektör makineleri, polinom regresyonu, rassal orman, XGboost, Light GBM ve yapay sinir ağı algoritmalarının eğitiminde kullanılmıştır. Sonuçlara bakıldığında diz eklemi momentini tahmin etmede en yüksek başarı XGBoost algoritmasında (%94.0), en düşük başarı ise destek vektör makineleri algoritmasında (%71.5) elde edilmiştir.

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Diz Eklemi Momentinin Tahmini İçin Makine Öğrenmesi Yöntemlerinin İncelenmesi

Serbest¹,

Kılıç²

2021

acperpro

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Advanced computational workflow for the multi-scale modeling of the bone metabolic processes

Dao

2016

Med Biol Eng Comput

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Multi-scale modeling of the musculoskeletal system plays an essential role in the deep understanding of complex mechanisms underlying the biological phenomena and processes such as bone metabolic processes. Current multi-scale models suffer from the isolation of sub-models at each anatomical scale. The objective of this present work was to develop a new fully integrated computational workflow for simulating bone metabolic processes at multi-scale levels. Organ-level model employs multi-body dynamics to estimate body boundary and loading conditions from body kinematics. Tissue-level model uses finite element method to estimate the tissue deformation and mechanical loading under body loading conditions. Finally, cell-level model includes bone remodeling mechanism through an agent-based simulation under tissue loading. A case study on the bone remodeling process located on the human jaw was performed and presented. The developed multi-scale model of the human jaw was validated using the literature-based data at each anatomical level. Simulation outcomes fall within the literature-based ranges of values for estimated muscle force, tissue loading and cell dynamics during bone remodeling process. This study opens perspectives for accurately simulating bone metabolic processes using a fully integrated computational workflow leading to a better understanding of the musculoskeletal system function from multiple length scales as well as to provide new informative data for clinical decision support and industrial applications.

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Regenerative Cells for the Management of Osteoarthritis and Joint Disorders: A Concise Literature Review

Block

Garza

2017

Aesthetic Surgery Journal

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As the global population ages, the prevalence of osteoarthritis (OA) and joint disorders represent a major cause of disability and a significant public health burden. As current approaches for the management of OA focus on slowing the progression of disease, without repairing the underlying damage, novel treatments are necessary to improve outcomes. Over the past decade, autologous cell-based therapies using regenerative cells from fat or bone marrow have become a major focus of research into new approaches for the treatment of osteoarthritis and joint disorders. This review is intended to summarize findings in existing literature and identify gaps in knowledge that should be addressed in order to advance the field. We acknowledge that some findings may appear inconsistent, but show that apparent inconsistency in the literature may be attributable to variation in source of cells, stage of disease, method of delivery, follow-up time, evaluation method, and a number of other idiosyncrasies of individual studies. Still, a number of themes emerge from the data and some broader conclusions may be drawn that can be used to guide future studies. Ultimately, we conclude that there is overwhelming evidence demonstrating the safety of the autologous cell-based therapies. Furthermore, the data support the claim that regenerative cells are capable of reversing progression of OA. Regenerative cells, and especially those from adipose tissue, represent a promising new approach for the treatment of OA. Future work should include appropriate controls, and focus on answering questions related to dose required, appropriate delivery vehicle, and the impact of multiple treatments. Additionally, future studies should look at short and long-term effects of the treatments, and use functional as well as radiologic methods to evaluate efficacy.

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Biomechanical model of knee collateral ligament injury with six degrees of freedom

Cited by 4 publications

References 24 publications

Diz Eklemi Momentinin Tahmini İçin Makine Öğrenmesi Yöntemlerinin İncelenmesi

Diz Eklemi Momentinin Tahmini İçin Makine Öğrenmesi Yöntemlerinin İncelenmesi

Advanced computational workflow for the multi-scale modeling of the bone metabolic processes

Regenerative Cells for the Management of Osteoarthritis and Joint Disorders: A Concise Literature Review

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