An Approach to Developing Customized Total Knee Replacement Implants

Li, Xinyu; Wang, Changjiang; Guo, Yuan; Chen, Weiyi

doi:10.1155/2017/9298061

Cited by 14 publications

(4 citation statements)

References 39 publications

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“…Knowledge of the internal musculoskeletal forces acting on the knee joint during dynamic functional movements has significant potential for informing injury and degenerative disease prevention strategies, 48 improving the outcomes of orthopedic treatments, 31 enhancing implant designs, 3,24 and validating computational model predictions. 18,38 Since the 1970s, multibody musculoskeletal models of increasing complexity have been proposed to predict such internal knee joint loading conditions.…”

Section: Introductionmentioning

confidence: 99%

The Capacity of Generic Musculoskeletal Simulations to Predict Knee Joint Loading Using the CAMS-Knee Datasets

et al. 2020

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Musculoskeletal models enable non-invasive estimation of knee contact forces (KCFs) during functional movements. However, the redundant nature of the musculoskeletal system and uncertainty in model parameters necessitates that model predictions are critically evaluated. This study compared KCF and muscle activation patterns predicted using a scaled generic model and OpenSim static optimization tool against in vivo measurements from six patients in the CAMS-knee datasets during level walking and squatting. Generally, the total KCFs were under-predicted (RMS: 47.55%BW, R 2 : 0.92) throughout the gait cycle, but substiantially over-predicted (RMS: 105.7%BW, R 2 : 0.81) during squatting. To understand the underlying etiology of the errors, muscle activations were compared to electromyography (EMG) signals, and showed good agreement during level walking. For squatting, however, the muscle activations showed large descrepancies especially for the biceps femoris long head. Errors in the predicted KCF and muscle activation patterns were greatest during deep squat. Hence suggesting that the errors mainly originate from muscle represented at the hip and an associated muscle co-contraction at the knee. Furthermore, there were substaintial differences in the ranking of subjects and activities based on peak KCFs in the simulations versus measurements. Thus, future simulation study designs must account for subject-specific uncertainties in musculoskeletal predictions.

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Section: Introductionmentioning

confidence: 99%

The Capacity of Generic Musculoskeletal Simulations to Predict Knee Joint Loading Using the CAMS-Knee Datasets

et al. 2020

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“…The FE construction procedure includes reconstructing the knee bone surfaces through image segmentation techniques on patient-specific pre-operational Computed Tomography (CT) data (Benos et al, 2020;Stanev et al, 2020). Many studies, like Woiczinski et al (2016), Li et al (2017), Moewis et al (2018), Nikolopoulos et al (2020), Su et al (2020), and Xu et al (2020) that aim at developing patient-specific FE models, adopt this method. These 3D bone models were later aligned with the knee joint's implant components by using segmentation software on post-operative CT images.…”

Section: Methodsmentioning

confidence: 99%

“…As suggested in a lot of studies (Kiapour et al, 2014;Marra et al, 2015;Woiczinski et al, 2016;Li et al, 2017;Moewis et al, 2018;Benos et al, 2020;Nikolopoulos et al, 2020;Stanev et al, 2020;Su et al, 2020;Xu et al, 2020), the best way to obtain a patient-specific 3D representation of the knee joint bones is to segment them from MRI or CT image data obtained from the subject under examination. For doing so, a segmentation method was performed with 3D Slicer (Kikinis et al, 2014) to extract the geometries of the knee bones before TKA, using subject-specific pre-operational CT scans obtained from the 6th GCC data set.…”

Section: Segmentation and Alignment Of Implant-bone Geometriesmentioning

confidence: 99%

“…Especially, studies focus on stresses at the joint, i.e., between the femoral component and tibial insert to reveal the effects of implant materials, sagittal radius, flexion angle (Shashishekar and Ramesh, 2007), various implant designs (Usman and Huang, 2018), surgical decisions (Su et al, 2020), or misalignment (Pang et al, 2016;Moewis et al, 2018) on the function of the knee joint. Other scientific articles on FEA focus on developing custom-made knee implants (Sun et al, 2015;Li et al, 2017) or conducting "what if " studies about implant positioning during TKA (Mou et al, 2018). Moreover, studies, such as Stanev et al (2016Stanev et al ( , 2020 and Benos et al (2020) that use musculoskeletal and finite element (FE) models of the knee to predict surgery's effect in a pre-operative manner might be an interesting extension of this work for TKR.…”

Section: Introductionmentioning

confidence: 99%

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Total Knee Replacement: Subject-Specific Modeling, Finite Element Analysis, and Evaluation of Dynamic Activities

Loi

Stanev

2021

Front. Bioeng. Biotechnol.

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This study presents a semi-automatic framework to create subject-specific total knee replacement finite element models, which can be used to analyze locomotion patterns and evaluate knee dynamics. In recent years, much scientific attention was attracted to pre-clinical optimization of customized total knee replacement operations through computational modeling to minimize post-operational adverse effects. However, the time-consuming and laborious process of developing a subject-specific finite element model poses an obstacle to the latter. One of this work's main goals is to automate the finite element model development process, which speeds up the proposed framework and makes it viable for practical applications. This pipeline's reliability was ratified by developing and validating a subject-specific total knee replacement model based on the 6th SimTK Grand Challenge data set. The model was validated by analyzing contact pressures on the tibial insert in relation to the patient's gait and analysis of tibial contact forces, which were found to be in accordance with the ones provided by the Grand Challenge data set. Subsequently, a sensitivity analysis was carried out to assess the influence of modeling choices on tibial insert's contact pressures and determine possible uncertainties on the models produced by the framework. Parameters, such as the position of ligament origin points, ligament stiffness, reference strain, and implant-bone alignment were used for the sensitivity study. Notably, it was found that changes in the alignment of the femoral component in reference to the knee bones significantly affect the load distribution at the tibiofemoral joint, with an increase of 206.48% to be observed at contact pressures during 5° internal rotation. Overall, the models produced by this pipeline can be further used to optimize and personalize surgery by evaluating the best surgical parameters in a simulated manner before the actual surgery.

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Health monitoring jeopardy prophylaxis model based on machine learning in fog computing

Suggala

Krishna

Swain

2022

Trans Emerging Tel Tech

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Cloud‐based fog computing was developed in recent years to make it easier for patients to monitor distant health and discover mosquito‐borne syndromes at an initial period, allowing to track the mosquito‐borne illnesses. Many obstacles have been confronted by the technology, including high latency, mobility, overhead connectivity, and location consciousness. Due to the security issue and wrong prediction of the diseases, users are affected by getting wrong alert messages from fog which increase the disease spreading. Hence a novel Prevention Technique based on Fog Computing has been introduced to prevent the epidemic syndrome at an initial period and safeguard the people around the world. Initially, each user register their personal information in Pristine Database through IoT device. These information are encrypted with 16 round of key‐dependent operation via new Blowfish Encryption Algorithm. Subsequently, the mosquito spawned disease is diagnosed by measuring the resemblance factor between the user and disease through the Hybrid Endemic Halsen Classifier and the Resemblance Coefficient are used to diagnose and categorize users as infected or unaffected. The mosquito‐borne disease is detected using this method by assessing the similarity factor between the user and the disease with less computing time. As a result, if a new symptom is found rather than one from the given medical dataset, the information will be saved in the Pristine Database, and the classification process will be faster with less computing time in the future if the same symptom is identified. Finally, the novel Temporal Social Network Analysis was developed to assess the likelihood of a disease breakout also analyze the infected users and send the awareness message to take the precautionary measures in the cloud processing layer. Thus, the proposed work effectively predicts and prevents the spreading of epidemic outbreaks with accuracy of 97% which is 7.2% higher than the existing approaches, and reduced latency of 0.18 second.

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An Approach to Developing Customized Total Knee Replacement Implants

Cited by 14 publications

References 39 publications

The Capacity of Generic Musculoskeletal Simulations to Predict Knee Joint Loading Using the CAMS-Knee Datasets

The Capacity of Generic Musculoskeletal Simulations to Predict Knee Joint Loading Using the CAMS-Knee Datasets

Total Knee Replacement: Subject-Specific Modeling, Finite Element Analysis, and Evaluation of Dynamic Activities

Health monitoring jeopardy prophylaxis model based on machine learning in fog computing

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