Surgical Design Optimization of Proximal Junctional Kyphosis
Joint Authors
Zuo, Heng
Zhang, Guangming
Peng, Li
Zhou, Xiaobo
Lan, Lan
Source
Journal of Healthcare Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-21
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Purpose.
The objective of this study was to construct a procedural planning tool to optimize the proximal junction angle (PJA) to prevent postoperative proximal junctional kyphosis (PJK) for each scoliosis patient.
Methods.
Twelve patients (9 patients without PJK and 3 patients with PJK) who have been followed up for at least 2 years after surgery were included.
After calculating the loading force on the cephalad intervertebral disc of upper instrumented vertebra of each patient, the finite-element method (FEM) was performed to calculate the stress of each element.
The stress information was summarized into the difference value before and after operation in different regions of interest.
A two-layer fully connected neural network method was applied to model the relationship between the stress information and the risk of PJK.
Leave-one-out cross-validation and sensitivity analysis were implemented to assess the accuracy and stability of the trained model.
The optimal PJA was predicted based on the learned model by optimization algorithm.
Results.
The mean prediction accuracy was 83.3% for all these cases, and the area under the curve (AUC) of prediction was 0.889.
And the output variance of this model was less than 5% when the important factor values were perturbed in a range of 5%.
Conclusion.
Our approach integrated biomechanics and machine learning to support the surgical decision.
For a new individual, the risk of PJK and optimal PJA can be simultaneously predicted based on the learned model.
American Psychological Association (APA)
Peng, Li& Zhang, Guangming& Zuo, Heng& Lan, Lan& Zhou, Xiaobo. 2020. Surgical Design Optimization of Proximal Junctional Kyphosis. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1186620
Modern Language Association (MLA)
Peng, Li…[et al.]. Surgical Design Optimization of Proximal Junctional Kyphosis. Journal of Healthcare Engineering No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1186620
American Medical Association (AMA)
Peng, Li& Zhang, Guangming& Zuo, Heng& Lan, Lan& Zhou, Xiaobo. Surgical Design Optimization of Proximal Junctional Kyphosis. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1186620
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186620