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PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping

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Listed:
  • Brian G Booth
  • Eva Hoefnagels
  • Toon Huysmans
  • Jan Sijbers
  • Noël L W Keijsers

Abstract

Quantitative analyses of plantar pressure images typically occur at the group level and under the assumption that individuals within each group display homogeneous pressure patterns. When this assumption does not hold, a personalized analysis technique is required. Yet, existing personalized plantar pressure analysis techniques work at the image level, leading to results that can be unintuitive and difficult to interpret. To address these limitations, we introduce PAPPI: the Personalized Analysis of Plantar Pressure Images. PAPPI is built around the statistical modelling of the relationship between plantar pressures in healthy controls and their demographic characteristics. This statistical model then serves as the healthy baseline to which an individual’s real plantar pressures are compared using statistical parametric mapping. As a proof-of-concept, we evaluated PAPPI on a cohort of 50 hallux valgus patients. PAPPI showed that plantar pressures from hallux valgus patients did not have a single, homogeneous pattern, but instead, 5 abnormal pressure patterns were observed in sections of this population. When comparing these patterns to foot pain scores (i.e. Foot Function Index, Manchester-Oxford Foot Questionnaire) and radiographic hallux angle measurements, we observed that patients with increased pressure under metatarsal 1 reported less foot pain than other patients in the cohort, while patients with abnormal pressures in the heel showed more severe hallux valgus angles and more foot pain. Also, incidences of pes planus were higher in our hallux valgus cohort compared to the modelled healthy controls. PAPPI helped to clarify recent discrepancies in group-level plantar pressure studies and showed its unique ability to produce quantitative, interpretable, and personalized analyses for plantar pressure images.

Suggested Citation

  • Brian G Booth & Eva Hoefnagels & Toon Huysmans & Jan Sijbers & Noël L W Keijsers, 2020. "PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-22, February.
  • Handle: RePEc:plo:pone00:0229685
    DOI: 10.1371/journal.pone.0229685
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    References listed on IDEAS

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    1. Uli Niemann & Myra Spiliopoulou & Thorsten Szczepanski & Fred Samland & Jens Grützner & Dominik Senk & Antao Ming & Juliane Kellersmann & Jan Malanowski & Silke Klose & Peter R Mertens, 2016. "Comparative Clustering of Plantar Pressure Distributions in Diabetics with Polyneuropathy May Be Applied to Reveal Inappropriate Biomechanical Stress," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-12, August.
    2. Davide Castelvecchi, 2016. "Can we open the black box of AI?," Nature, Nature, vol. 538(7623), pages 20-23, October.
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