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3D Patellar Shape is Associated with Patellar Dislocation: an Automated Coordinate Algorithm and Statistical Shape

Yichen Yan1, Jie Yao1, Zifan Liu1

  • 1Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology, National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.

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PubMed
Summary
This summary is machine-generated.

Patellar dislocation (PD) is linked to specific 3D knee cap shape patterns, not overall size. An automated system and statistical shape model (SSM) offer new ways to study these variations and predict PD risk.

Keywords:
3D Patellar MorphologyAutomated Coordinate AlgorithmMorphological Risk FactorsPatellar DislocationPatellar InstabilityStatistical Shape Model

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Area of Science:

  • Orthopedics
  • Biomedical Engineering
  • Medical Imaging

Background:

  • Patellar dislocation (PD) is a common orthopedic issue.
  • Understanding the 3D morphology of the patella is crucial for diagnosing and treating PD.
  • Existing methods for patellar analysis lack standardization and quantitative precision.

Purpose of the Study:

  • To develop an automated, landmark-based coordinate system for the patella.
  • To create a patellar statistical shape model (SSM) for analyzing 3D morphology.
  • To identify and quantify 3D morphological variations associated with patellar dislocation (PD).

Main Methods:

  • Reconstructed patellar surface models from CT/MRI scans of 54 participants (33 PD, 21 controls).
  • Established and validated an automated patellar coordinate system.
  • Utilized principal component analysis (PCA) to build an SSM and extract major 3D shape modes.
  • Assessed demographic/morphometric risk factors and evaluated a logistic regression classifier.

Main Results:

  • The automated coordinate system demonstrated high repeatability.
  • No significant differences in patellar linear dimensions or centroid size were found between PD and control groups.
  • Two principal components (PC4 and PC7) representing thickness/facet and facet-edge morphology, respectively, significantly differentiated PD from controls.
  • A cross-validated classifier achieved good in-cohort discrimination (mean AUC ≈ 0.91).

Conclusions:

  • Patellar dislocation is associated with distinct 3D articular surface shape patterns, including a prominent medial facet and flattened posterolateral facet, independent of overall patellar dimensions.
  • The automated coordinate system and SSM provide a reproducible method for quantitative patellar phenotyping.
  • Identified shape modes offer insights into PD pathomechanics and a basis for future risk modeling.