Parameter-based patient-specific restoration of physiological knee morphology for optimized implant design and matching
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Sonja Grothues
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Ann-Kristin Becker
Abstract
Total knee arthroplasty (TKA) patients may present with genetic deformities, such as trochlear dysplasia, or deformities related to osteoarthritis. This pathologic morphology should be corrected by TKA to compensate for related functional deficiencies. Hence, a reconstruction of an equivalent physiological knee morphology would be favorable for detailed preoperative planning and the patient-specific implant selection or design process. A parametric database of 673 knees, each described by 36 femoral parameter values, was used. Each knee was classified as pathological or physiological based on cut-off values from literature. A clinical and a mathematical classification approach were developed to distinguish between affected and unaffected parameters. Three different prediction methods were used for the restoration of physiological parameter values: regression, nearest neighbor search and artificial neural networks. Several variants of the respective prediction model were considered, such as different network architectures. Regarding all methods, the model variant chosen resulted in a prediction error below the parameters’ standard deviation, while the regression yielded the lowest errors. Future analyses should consider other deformities, also of tibia and patella. Furthermore, the functional consequences of the parameter changes should be analyzed.
Acknowledgments
For the study we used a database derived from surface models provided by Conformis, Inc. (Billerica, MA, USA).
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Research funding: “Not applicable”.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: Authors state no conflict of interest.
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Informed consent: “Not applicable”.
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Ethical approval: Ethical approval was not required since the anonymized patient data and surface models analyzed were acquired in the past for surgery and not for the purpose of this retrospective study. The cadaver data and surface models were acquired in the past for training. No donor identifying information was accessed for the conduct of the study.
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Articles in the same Issue
- Frontmatter
- Review
- Research frontiers of electroporation-based applications in cancer treatment: a bibliometric analysis
- Research Articles
- Deep neural network to differentiate internet gaming disorder from healthy controls during stop-signal task: a multichannel near-infrared spectroscopy study
- A low power respiratory sound diagnosis processing unit based on LSTM for wearable health monitoring
- Effective deep learning classification for kidney stone using axial computed tomography (CT) images
- De- and recellularized urethral reconstruction with autologous buccal mucosal cells implanted in an ovine animal model
- The impact of right ventricular hemodynamics on the performance of a left ventricular assist device in a numerical simulation model
- Optimal assist strategy exploration for a direct assist device under stress‒strain dynamics
- Revisiting SFA stent technology: an updated overview on mechanical stent performance
- Parameter-based patient-specific restoration of physiological knee morphology for optimized implant design and matching
- Influences of smart glasses on postural control under single- and dual-task conditions for ergonomic risk assessment
