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EMR-Radiological Phenotypes in Diseases of the Optic Nerve and Their Association with Visual Function

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Book cover Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (DLMIA 2017, ML-CDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10553))

Abstract

Multi-modal analyses of diseases of the optic nerve, that combine radiological imaging with other electronic medical records (EMR), improve understanding of visual function. We conducted a study of 55 patients with glaucoma and 32 patients with thyroid eye disease (TED). We collected their visual assessments, orbital CT imaging, and EMR data. We developed an image-processing pipeline that segmented and extracted structural metrics from CT images. We derived EMR phenotype vectors with the help of PheWAS (from diagnostic codes) and ProWAS (from treatment codes). Next, we performed a principal component analysis and multiple-correspondence analysis to identify their association with visual function scores. We found that structural metrics derived from CT imaging are significantly associated with functional visual score for both glaucoma (R2 = 0.32) and TED (R2 = 0.4). Addition of EMR phenotype vectors to the model significantly improved (p < 1E−04) the R2 to 0.4 for glaucoma and 0.54 for TED.

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Acknowledgements

This research was supported by NSF CAREER 1452485 and NIH grants 5R21EY024036. This research was conducted with the support from Intramural Research Program, National Institute on Aging, NIH. This study was in part using the resources of the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University, Nashville, TN. This project was supported in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01, and is now at the National Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-06.

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Authors and Affiliations

  1. Department of Computer Science, Vanderbilt University, Nashville, TN, 37235, USA

    Shikha Chaganti

  2. Vanderbilt University Medical Center, 1211 Medical Center Dr, Nashville, TN, 37232, USA

    Jamie R. Robinson

  3. Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37235, USA

    Camilo Bermudez

  4. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37235, USA

    Thomas Lasko

  5. Vanderbilt Eye Institute, 2311 Pierce Avenue, Nashville, TN, 37232, USA

    Louise A. Mawn

  6. Department of Electrical Engineering, Vanderbilt University, Nashville, TN, 37235, USA

    Bennett A. Landman

Authors
  1. Shikha Chaganti
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  2. Jamie R. Robinson
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  3. Camilo Bermudez
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  4. Thomas Lasko
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  5. Louise A. Mawn
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  6. Bennett A. Landman
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Corresponding author

Correspondence to Shikha Chaganti .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    M. Jorge Cardoso

  2. McGill University, Montreal, Québec, Canada

    Tal Arbel

  3. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

  4. IBM Research - Almaden, San Jose, California, USA

    Tanveer Syeda-Mahmood

  5. Universidade do Porto, Porto, Portugal

    João Manuel R.S. Tavares

  6. IBM Research - Almaden, San Jose, USA

    Mehdi Moradi

  7. University of Queensland, Brisbane, Queensland, Australia

    Andrew Bradley

  8. Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  9. Universidade Estadual Paulista, Bauru, Brazil

    João Paulo Papa

  10. Case Western Reserve University, Cleveland, Ohio, USA

    Anant Madabhushi

  11. Instituto Superior Técnico, Lisboa, Portugal

    Jacinto C. Nascimento

  12. Universidade do Porto, Porto, Portugal

    Jaime S. Cardoso

  13. University of Oxford, Oxford, United Kingdom

    Vasileios Belagiannis

  14. University of South Australia, Adelaide, South Australia, Australia

    Zhi Lu

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Chaganti, S., Robinson, J.R., Bermudez, C., Lasko, T., Mawn, L.A., Landman, B.A. (2017). EMR-Radiological Phenotypes in Diseases of the Optic Nerve and Their Association with Visual Function. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_43

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  • DOI: https://doi.org/10.1007/978-3-319-67558-9_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67557-2

  • Online ISBN: 978-3-319-67558-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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