deep learning diagnostic and risk-stratification for ipf and copd

Conditions: Lung; Disease, Interstitial, With Fibrosis; Pulmonary Disease, Chronic Obstructive; Artificial Intelligence

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.

aim: to develop and determine the predictive power of an ai (deep learning) algorithm in identifying the acoustic and lus signatures of ipf, nsip and copd in an adult population and discriminating them from age-matched, never smoker, control subjects with normal lung function. methodology: a single-center, prospective, population-based case-control study that will be carried out in subjects with ipf, nsip and copd. a total of 120 consecutive patients aged ≥ 18 years and meeting ipf, nsip or copd international criteria, and 40 age-matched controls, will be recruited in a swiss pulmonology outpatient clinic with a total of approximately 7000 specialized consultations per year, starting from august 2022. at inclusion, demographic and clinical data will be collected. additionally, lung auscultation will be recorded with a digital stethoscope and lus performed. a deep learning algorithm (deepbreath) using various deep learning networks with aggregation strategies will be trained on these audio recordings and lung images to derive an automated prediction of diagnostic (i.e., positive vs negative) and risk stratification categories (mild to severe). secondary outcomes will be to measures the association of analysed lung sounds with clinical, functional and radiological characteristics of ipf, nsip and copd diagnosis. patients' quality of life will be measured with the standardized dedicated king's brief interstitial lung disease (k-bild) and the copd assessment test (cat) questionnaires. expected results: this study seeks to explore the synergistic value of several point-of-care-tests for the detection and differential diagnosis of ild and copd as well as estimate severity to better guide care management in adults

Eligibility criteria:
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.

Keywords: lung; disease, interstitial, with fibrosis idiopathic pulmonary fibrosis idiopathic interstitial pneumonias pulmonary disease, chronic obstructive artificial intelligence deep learning respiratory sounds; auscultation ultrasonography diagnostic imaging