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Predicting Osteoarthritis of the Temporomandibular Joint Using Random Forest with Privileged Information

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Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging (EPIMI 2022, ML-CDS 2022, TDA4BiomedicalImaging 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13755))

Abstract

Osteoarthritis of the temporomandibular joint (TMJ OA) is the most common disorder of the TMJ. A clinical decision support (CDS) system designed to detect TMJ OA could function as a useful screening tool as part of regular check-ups to detect early onset. This study implements a CDS concept model based on Random Forest and dubbed RF\(^+\) to predict TMJ OA with the hypothesis that a model which leverages high-resolution radiological and biomarker data in training only can improve predictions compared with a baseline model which does not use privileged information. We found that the RF\(^+\) model can outperform the baseline model even when privileged features are not of gold standard quality. Additionally, we introduce a novel method for post-hoc feature analysis, finding shortRunHighGreyLevelEmphasis of the lateral condyles and joint distance to be the most important features from the privileged modalities for predicting TMJ OA.

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Acknowledgements

E.W. and A.R. are supported by NIH Grant R37-CA214955. E.W. was supported by T32GM070449 as well. This study was supported by NIDCR R01DE024450 and AAOF Graber Family Teaching and Research Award and by Research Enhancement Award Activity 141 from the University of the Pacific, School of Dentistry.

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Authors and Affiliations

  1. University of Michigan, Ann Arbor, MI, 48109, USA

    Elisa Warner, Najla Al-Turkestani, Marcela Lima Gurgel, Lucia Cevidanes & Arvind Rao

  2. King Abdulaziz University, Jeddah, 22254, Saudi Arabia

    Najla Al-Turkestani

  3. University of the Pacific, San Francisco, CA, 94103, USA

    Jonas Bianchi

Authors
  1. Elisa Warner
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  2. Najla Al-Turkestani
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  3. Jonas Bianchi
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  4. Marcela Lima Gurgel
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  5. Lucia Cevidanes
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  6. Arvind Rao
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Corresponding authors

Correspondence to Lucia Cevidanes or Arvind Rao .

Editor information

Editors and Affiliations

  1. Université de Rennes 1, Rennes, France

    John S. H. Baxter

  2. Imperial College London, London, UK

    Islem Rekik

  3. Western University, London, ON, Canada

    Roy Eagleson

  4. University of Sydney, Sydney, NSW, Australia

    Luping Zhou

  5. IBM Almaden Research Center, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  6. IBM Almaden Research Center, San Jose, CA, USA

    Hongzhi Wang

  7. University of San Francisco, San Francisco, CA, USA

    Mustafa Hajij

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Warner, E., Al-Turkestani, N., Bianchi, J., Gurgel, M.L., Cevidanes, L., Rao, A. (2022). Predicting Osteoarthritis of the Temporomandibular Joint Using Random Forest with Privileged Information. In: Baxter, J.S.H., et al. Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging. EPIMI ML-CDS TDA4BiomedicalImaging 2022 2022 2022. Lecture Notes in Computer Science, vol 13755. Springer, Cham. https://doi.org/10.1007/978-3-031-23223-7_7

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  • DOI: https://doi.org/10.1007/978-3-031-23223-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23222-0

  • Online ISBN: 978-3-031-23223-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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