Clinical trial NCT05318599: Deep Learning Diagnostic and Risk-stratification for IPF and COPD
Official title: Deep Learning Diagnostic and Risk-stratification for Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease in Digital Lung Auscultations
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Lung; Disease, Interstitial, With Fibrosis
Pulmonary Disease, Chronic Obstructive
Artificial Intelligence
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
Lead sponsor: Pediatric Clinical Research Platform
Sponsor class: OTHER
Collaborators: name: University Hospital, Geneva
name: Swiss Federal Institute of Technology
name: Hôpital du Valais
First posted: 2022-04-08
Enrollment: 160
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Cases: 120 patients (80 ILD \[40 IPF, 40 NSIP\], 40 COPD) will be recruited from an outpatient pulmonology clinic in Switzerland in daily clinical practice on the day of intervention.

Probable and definitive IPF diagnosis will be made according to the Fleischner Society Consensus, NSIP diagnosis with the American Thoracic Society classification, and COPD with the Global Initiative for Chronic Obstructive Lung Disease criteria.

Controls: 40 age-matched (+/- 2.5 years) never smokers with normal lung function (spirometry, lung volume and transfer factor for carbon monoxide) followed in the pulmonology outpatient clinic with similar quality of electronic medical records but for diseases other than the outcome of interest (see eligibility criteria) will serve as the 1:1 control group.

Brief 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.

Detailed description: 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.

Primary outcomes:
measure: To differentiate ILD from control subjects based on digital lung sounds recordings and LUS.
timeFrame: During lung auscultation (10 minutes). Each patient will provide 10 recordings of 30 seconds. LUS images and 5 second video clips of each anatomic region (10 regions represented).
measure: Predictive performance of the DeepBreath algorithm to stratify ILD severity based on human digital lung sounds recordings and LUS (i.e. physiological parameters) compared to grading scales.
timeFrame: During lung auscultation (10 minutes). Each patient will provide 10 recordings of 30 seconds. LUS images and 5 second video clips of each anatomic region (10 regions represented).
measure: Performance of the DeepBreath algorithm to subcategorize ILD by discriminating digital lung sounds recordings and LUS (i.e. physiological parameters).
timeFrame: During lung auscultation (10 minutes). Each patient will provide 10 recordings of 30 seconds. LUS images and 5 second video clips of each anatomic region (10 regions represented).
Secondary outcomes:
measure: Performance of human expert-identified acoustic signatures.
timeFrame: During the data analysis period (i.e., after the 60-minute study intervention period).
measure: Agreement of human labels with objectively clustered pathological sounds by machine learning.
timeFrame: During the data analysis period (i.e., after the 60-minute study intervention period).
measure: Diagnostic performance of DeepBreath to detect crackles in IPF patients.
timeFrame: During the data analysis period (i.e., after the 60-minute study intervention period).
measure: To test whether performance of DeepBreath could be improved using clinical features (i.e., signs, respiratory symptoms, demographics, medical history and basic paraclinical tests).
timeFrame: During the data analysis period (i.e., after the 60-minute study intervention period)
measure: K-BILD
timeFrame: Baseline
measure: CAT
timeFrame: Baseline
Other outcomes:
Locations:
city: Sion
country: Switzerland
facility: Centre Hospitalier du Valais Romand
state: Wallis
Study officials:
affiliation: Hôpital du Valais, Switzerland
name: Pierre-Olivier Bridevaux, Prof.
role: PRINCIPAL_INVESTIGATOR
affiliation: Geneva University Hospitals, Switzerland
name: Johan N. Siebert, MD
role: STUDY_DIRECTOR
References: