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Anthropometric Landmarking for Diagnosis of Cranial Deformities: Validation of an Automatic Approach and Comparison with Intra- and Interobserver Variability

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Abstract

Shape analysis of infant’s heads is crucial to diagnose cranial deformities and evaluate head growth. Currently available 3D imaging systems can be used to create 3D head models, promoting the clinical practice for head evaluation. However, manual analysis of 3D shapes is difficult and operator-dependent, causing inaccuracies in the analysis. This study aims to validate an automatic landmark detection method for head shape analysis. The detection results were compared with manual analysis in three levels: (1) distance error of landmarks; (2) accuracy of standard cranial measurements, namely cephalic ratio (CR), cranial vault asymmetry index (CVAI), and overall symmetry ratio (OSR); and (3) accuracy of the final diagnosis of cranial deformities. For each level, the intra- and interobserver variability was also studied by comparing manual landmark settings. High landmark detection accuracy was achieved by the method in 166 head models. A very strong agreement with manual analysis for the cranial measurements was also obtained, with intraclass correlation coefficients of 0.997, 0.961, and 0.771 for the CR, CVAI, and OSR. 91% agreement with manual analysis was achieved in the diagnosis of cranial deformities. Considering its high accuracy and reliability in different evaluation levels, the method showed to be feasible for use in clinical practice for head shape analysis.

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Acknowledgments

This work was funded by projects “NORTE-01-0145-FEDER-000045” and “NORTE-01-0145-FEDER-000059”, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). This project was also funded by national funds (PIDDAC), through the FCT – Fundação para a Ciência e Tecnologia and FCT/MCTES under the scope of the projects UIDB/05549/2020, UIDP/05549/2020 and Lasi-LA/P/0104/2020. The authors also acknowledge support from FCT and the European Social Found, through Programa Operacional Capital Humano (POCH), in the scope of the PhD grant SFRH/BD/136670/2018, SFRH/BD/136721/2018, and SFRH/BD/131545/2017.

Conflict of interest

The authors declare no conflict of interest.

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Authors and Affiliations

  1. 2Ai – School of Technology, IPCA, Barcelos, Portugal

    Helena R. Torres, Pedro Morais, Bruno Oliveira, Fernando Veloso & João L. Vilaça

  2. Algoritmi Center, School of Engineering, University of Minho, Campus de Azurém, 4804-533, Guimarães, Portugal

    Helena R. Torres, Bruno Oliveira & Jaime C. Fonseca

  3. Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal

    Helena R. Torres, Bruno Oliveira & Fernando Veloso

  4. ICVS/3B’s - PT Government Associate Laboratory, Braga/Guimarães, Portugal

    Helena R. Torres, Bruno Oliveira & Fernando Veloso

  5. Department for Neonatology and Pediatric Intensive Care, Medical Faculty of TU, Children’s Hospital, Dresden, Germany

    Anne Fritze, Wolfram Burkhardt, Maxi Kaufmann & Mario Rüdiger

  6. Department of Mechanical Engineering, School of Engineering, University of Minho, Guimarães, Portugal

    Fernando Veloso

  7. Institute for Radiological Diagnostics, Universitätsklinikum Carl Gustav Carus Dresden, Technische Universität, Dresden, Germany

    Gabriele Hahn

Authors
  1. Helena R. Torres
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  2. Pedro Morais
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  3. Anne Fritze
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  4. Wolfram Burkhardt
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  6. Bruno Oliveira
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  7. Fernando Veloso
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  8. Gabriele Hahn
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  9. Mario Rüdiger
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  10. Jaime C. Fonseca
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  11. João L. Vilaça
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Corresponding author

Correspondence to Helena R. Torres.

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Associate Editor Ka-Wai Kwok oversaw the review of this article.

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Torres, H.R., Morais, P., Fritze, A. et al. Anthropometric Landmarking for Diagnosis of Cranial Deformities: Validation of an Automatic Approach and Comparison with Intra- and Interobserver Variability. Ann Biomed Eng 50, 1022–1037 (2022). https://doi.org/10.1007/s10439-022-02981-6

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