Deep Learning Diagnostic and Risk-stratification for IPF and COPD

Summary

Idiopathic pulmonary fibrosis (IPF), non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive, irreversibly incapacitating pulmonary disorders with modest response to therapeutic interventions and poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence (AI)-assisted digital lung auscultation could constitute an alternative to conventional subjective operator-related auscultation to accurately and earlier diagnose these diseases. Moreover, lung ultrasound (LUS), a relevant gold standard for lung pathology, could also benefit from automation by deep learning.

Eligibility

Inclusion Criteria:

* Written informed consent
* age \> 18 years old.
* patients with already-diagnosed IPF (group 1) prior to the consultation (index) date.
* patients with already-diagnosed NSIP (group 2) prior to the consultation (index) date.
* patients with already-diagnosed COPD (group 3) prior to the consultation (index) date.
* Control subjects must be followed-up at the pulmonology outpatient clinic for:

  1. obstructive sleep apnoea.
  2. occupational lung diseases (miners, chemical workers, etc.).
  3. pulmonary nodules (considered benign after 2 years).

Exclusion Criteria:

* patients who cannot be mobilized for posterior auscultation.
* patients known for severe cardiovascular disease with pulmonary repercussion.
* patients known for a concurrent, acute, infectious pulmonary disease (e.g., pneumonia, bronchitis).
* patients known for asthma.
* patients known or suspected of immunodeficiency, alpha-1-antitrypsin deficit, and or under immunotherapy.
* patients with physical inability to follow procedures.
* patients with inability to give informed consent.

Conditions4

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