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Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data

Author

Listed:
  • Xueyan Mei

    (Icahn School of Medicine at Mount Sinai)

  • Zelong Liu

    (Icahn School of Medicine at Mount Sinai)

  • Ayushi Singh

    (Icahn School of Medicine at Mount Sinai)

  • Marcia Lange

    (Icahn School of Medicine at Mount Sinai)

  • Priyanka Boddu

    (Icahn School of Medicine at Mount Sinai)

  • Jingqi Q. X. Gong

    (Icahn School of Medicine at Mount Sinai)

  • Justine Lee

    (Icahn School of Medicine at Mount Sinai)

  • Cody DeMarco

    (Icahn School of Medicine at Mount Sinai)

  • Chendi Cao

    (Icahn School of Medicine at Mount Sinai)

  • Samantha Platt

    (Icahn School of Medicine at Mount Sinai)

  • Ganesh Sivakumar

    (Icahn School of Medicine at Mount Sinai)

  • Benjamin Gross

    (Icahn School of Medicine at Mount Sinai)

  • Mingqian Huang

    (Icahn School of Medicine at Mount Sinai)

  • Joy Masseaux

    (Icahn School of Medicine at Mount Sinai)

  • Sakshi Dua

    (Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai)

  • Adam Bernheim

    (Icahn School of Medicine at Mount Sinai)

  • Michael Chung

    (Icahn School of Medicine at Mount Sinai)

  • Timothy Deyer

    (Cornell Medicine
    East River Medical Imaging)

  • Adam Jacobi

    (Icahn School of Medicine at Mount Sinai)

  • Maria Padilla

    (Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai)

  • Zahi A. Fayad

    (Icahn School of Medicine at Mount Sinai
    Icahn School of Medicine at Mount Sinai)

  • Yang Yang

    (Icahn School of Medicine at Mount Sinai
    Icahn School of Medicine at Mount Sinai
    University of California)

Abstract

For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient’s 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis.

Suggested Citation

  • Xueyan Mei & Zelong Liu & Ayushi Singh & Marcia Lange & Priyanka Boddu & Jingqi Q. X. Gong & Justine Lee & Cody DeMarco & Chendi Cao & Samantha Platt & Ganesh Sivakumar & Benjamin Gross & Mingqian Hua, 2023. "Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37720-5
    DOI: 10.1038/s41467-023-37720-5
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    References listed on IDEAS

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