Dataset Information

Anatomical Variation of the Tibia - a Principal Component Analysis.

ABSTRACT: Conventional anatomically contoured plates do not adequately fit most tibiae. This emphasizes the need for a more thorough morphological study. Statistical shape models are promising tools to display anatomical variations within a population. Herein, we aim to provide a better insight into the anatomical variations of the tibia and tibia plateau. Seventy-nine CT scans of tibiae were segmented, and a principal component analysis was performed. Five morphologically important parameters were measured on the 3D models of the mean tibial shapes as well as the -3SD and +3?SD tibial shapes of the first five components. Longer, wider tibiae are related to a more rounded course of the posterior column, a less prominent tip of the medial malleolus, and a more posteriorly directed fibular notch. Varus/valgus deformations and the angulation of the posterior tibia plateau represent only a small percentage of the total variation. Right and left tibiae are not always perfectly symmetrical, especially not at the level of the tibia plateau. The largest degree of anatomical variation of the tibia is found in its length and around the tibia plateau. Because of the large variation in the anatomy, a more patient-specific approach could improve implant fit, anatomical reduction, biomechanical stability and hardware-related complications.

SUBMITTER: Quintens L

PROVIDER: S-EPMC6529455 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Anatomical Variation of the Tibia - a Principal Component Analysis.

Quintens Liselore L Herteleer Michiel M Vancleef Sanne S Carette Yannick Y Duflou Joost J Nijs Stefaan S Sloten Jos Vander JV Hoekstra Harm H

Scientific reports 20190521 1

Conventional anatomically contoured plates do not adequately fit most tibiae. This emphasizes the need for a more thorough morphological study. Statistical shape models are promising tools to display anatomical variations within a population. Herein, we aim to provide a better insight into the anatomical variations of the tibia and tibia plateau. Seventy-nine CT scans of tibiae were segmented, and a principal component analysis was performed. Five morphologically important parameters were measur ...[more]

PMID: 31114000

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Project description:BackgroundPrincipal component analysis is used to summarize matrix data, such as found in transcriptome, proteome or metabolome and medical examinations, into fewer dimensions by fitting the matrix to orthogonal axes. Although this methodology is frequently used in multivariate analyses, it has disadvantages when applied to experimental data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the data set. Second, the method is sensitive to experimental noise and bias between sample groups. It cannot reflect the experimental design that is planned to manage the noise and bias; rather, it estimates the same weight and independence to all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. First, the principal axes were identified using training data sets and shared across experiments. These training data reflect the design of experiments, and their preparation allows noise to be reduced and group bias to be removed. Second, the center of the rotation was determined in accordance with the experimental design. Third, the resulting components were scaled to unify their size unit.ResultsThe effects of these options were observed in microarray experiments, and showed an improvement in the separation of groups and robustness to noise. The range of scaled scores was unaffected by the number of items. Additionally, unknown samples were appropriately classified using pre-arranged axes. Furthermore, these axes well reflected the characteristics of groups in the experiments. As was observed, the scaling of the components and sharing of axes enabled comparisons of the components beyond experiments. The use of training data reduced the effects of noise and bias in the data, facilitating the physical interpretation of the principal axes.ConclusionsTogether, these introduced options result in improved generality and objectivity of the analytical results. The methodology has thus become more like a set of multiple regression analyses that find independent models that specify each of the axes.

Dataset Information

Anatomical Variation of the Tibia - a Principal Component Analysis.

Publications

Anatomical Variation of the Tibia - a Principal Component Analysis.

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