Deep Learning Diagnostic and Risk-stratification for IPF and COPD

NCT ID: NCT05318599

Last Updated: 2024-04-12

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

160 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-04-01

Study Completion Date

2024-10-31

Brief Summary

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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

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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

Conditions

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

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

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IPF patients (group 1)

Consenting adult patients \>18 years old with with already-diagnosed IPF

Lung auscultation

Intervention Type DEVICE

Digital lung auscultation with the Eko core digital stethoscope (Eko Devices, Inc., CA, USA).

Lung ultrasound

Intervention Type DEVICE

Lung ultrasonography

Quality of Life's questionnaires

Intervention Type OTHER

Impact of the diseases on subjects' health-related quality of life measured with standardized questionnaires (K-BILD, CAT)

Pulmonary functional tests

Intervention Type DIAGNOSTIC_TEST

Spirometry, body-plethysmographic parameters and lung diffusion capacity for carbon monoxide will be measured.

NSIP patients (group 2)

Consenting adult patients \>18 years old with with already-diagnosed non-specific interstitial pneumonia (NSIP)

Lung auscultation

Intervention Type DEVICE

Digital lung auscultation with the Eko core digital stethoscope (Eko Devices, Inc., CA, USA).

Lung ultrasound

Intervention Type DEVICE

Lung ultrasonography

Quality of Life's questionnaires

Intervention Type OTHER

Impact of the diseases on subjects' health-related quality of life measured with standardized questionnaires (K-BILD, CAT)

Pulmonary functional tests

Intervention Type DIAGNOSTIC_TEST

Spirometry, body-plethysmographic parameters and lung diffusion capacity for carbon monoxide will be measured.

COPD patients (group 3)

Consenting adult patients \>18 years old with with already-diagnosed chronic obstructive pulmonary disease (COPD)

Lung auscultation

Intervention Type DEVICE

Digital lung auscultation with the Eko core digital stethoscope (Eko Devices, Inc., CA, USA).

Lung ultrasound

Intervention Type DEVICE

Lung ultrasonography

Quality of Life's questionnaires

Intervention Type OTHER

Impact of the diseases on subjects' health-related quality of life measured with standardized questionnaires (K-BILD, CAT)

Pulmonary functional tests

Intervention Type DIAGNOSTIC_TEST

Spirometry, body-plethysmographic parameters and lung diffusion capacity for carbon monoxide will be measured.

Control subjects (group 4)

Consenting age-matched (+/- 2.5 years) never smokers patients with normal lung function (spirometry, lung volume and Transfer Factor for Carbon Monoxide (TLCO)) followed in the pulmonology outpatient clinic with similar quality of electronic medical records but for diseases other than the outcome of interest, namely:

1. patients with obstructive sleep apnea.
2. patients followed-up for occupational lung diseases (miners, chemical workers, etc.).
3. patients followed-up for pulmonary nodules (considered benign after 2 years).

Lung auscultation

Intervention Type DEVICE

Digital lung auscultation with the Eko core digital stethoscope (Eko Devices, Inc., CA, USA).

Lung ultrasound

Intervention Type DEVICE

Lung ultrasonography

Quality of Life's questionnaires

Intervention Type OTHER

Impact of the diseases on subjects' health-related quality of life measured with standardized questionnaires (K-BILD, CAT)

Pulmonary functional tests

Intervention Type DIAGNOSTIC_TEST

Spirometry, body-plethysmographic parameters and lung diffusion capacity for carbon monoxide will be measured.

Interventions

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Lung auscultation

Digital lung auscultation with the Eko core digital stethoscope (Eko Devices, Inc., CA, USA).

Intervention Type DEVICE

Lung ultrasound

Lung ultrasonography

Intervention Type DEVICE

Quality of Life's questionnaires

Impact of the diseases on subjects' health-related quality of life measured with standardized questionnaires (K-BILD, CAT)

Intervention Type OTHER

Pulmonary functional tests

Spirometry, body-plethysmographic parameters and lung diffusion capacity for carbon monoxide will be measured.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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University Hospital, Geneva

OTHER

Sponsor Role collaborator

Swiss Federal Institute of Technology

OTHER

Sponsor Role collaborator

Hôpital du Valais

OTHER

Sponsor Role collaborator

Pediatric Clinical Research Platform

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Pierre-Olivier Bridevaux, Prof.

Role: PRINCIPAL_INVESTIGATOR

Hôpital du Valais, Switzerland

Johan N. Siebert, MD

Role: STUDY_DIRECTOR

Geneva University Hospitals, Switzerland

Locations

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Centre Hospitalier du Valais Romand

Sion, Valais, Switzerland

Site Status RECRUITING

Countries

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Switzerland

Central Contacts

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Johan N. Siebert, MD

Role: CONTACT

+41795534072

Pierre-Olivier Bridevaux, Prof.

Role: CONTACT

+41276034678

Facility Contacts

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Pierre-Olivier Bridevaux, Prof

Role: primary

+41276034678

Johan N. Siebert, MD

Role: backup

+41795534072

References

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Siebert JN, Hartley MA, Courvoisier DS, Salamin M, Robotham L, Doenz J, Barazzone-Argiroffo C, Gervaix A, Bridevaux PO. Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study. BMC Pulm Med. 2023 Jun 2;23(1):191. doi: 10.1186/s12890-022-02255-w.

Reference Type DERIVED
PMID: 37264374 (View on PubMed)

Other Identifiers

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IPFoscope

Identifier Type: -

Identifier Source: org_study_id

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