Lung; Disease, Interstitial, With FibrosisDeepBreathNCT05318599
Deep Learning Diagnostic and Risk-stratification for IPF and COPD
An observational study of Lung; Disease, Interstitial, With Fibrosis and Pulmonary Disease, Chronic Obstructive, run by Pediatric Clinical Research Platform at 1 site in Switzerland.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung; Disease, Interstitial, With Fibrosis, Pulmonary Disease, Chronic Obstructive, Artificial Intelligence
- What is being tested?
- Lung auscultation, Lung ultrasound and Quality of Life's questionnaires
- Type of treatment
- Medical device
- How many people
- 160 participants
- Where
- 1 siteSwitzerland
- Study dates
- 1 Apr 2023to 31 Oct 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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:
Show all 9Show fewer
- obstructive sleep apnoea.
- occupational lung diseases (miners, chemical workers, etc.).
- pulmonary nodules (considered benign after 2 years).
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Switzerland1 site, 1 recruiting
- Recruiting
Centre Hospitalier du Valais Romand
Sion, Valais, 1951
Pierre-Olivier Bridevaux, Prof, +41276034678, pierre-olivier.bridevaux@hopitalvs.ch
Johan N. Siebert, MD, +41795534072, Johan.Siebert@hcuge.ch
About this study
Official title: Deep Learning Diagnostic and Risk-stratification for Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease in Digital Lung Auscultations
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.
Read the 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
- Conditions
- Lung; Disease, Interstitial, With Fibrosis, Pulmonary Disease, Chronic Obstructive, Artificial Intelligence
- Being tested
- Lung auscultation, Lung ultrasound, Quality of Life's questionnaires, Pulmonary functional tests
- 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
- Medical subject headings
- Disease, Pulmonary Disease, Chronic Obstructive, Idiopathic Pulmonary Fibrosis, Idiopathic Interstitial Pneumonias, Respiratory Sounds
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Lung auscultationDevice
Digital lung auscultation with the Eko core digital stethoscope (Eko Devices, Inc., CA, USA).
Lung ultrasoundDevice
Lung ultrasonography
Quality of Life's questionnairesOther
Impact of the diseases on subjects' health-related quality of life measured with standardized questionnaires (K-BILD, CAT)
Pulmonary functional testsDiagnostic test
Spirometry, body-plethysmographic parameters and lung diffusion capacity for carbon monoxide will be measured.
| Study group | Role | What they receive |
|---|---|---|
| IPF patients (group 1) | Consenting adult patients >18 years old with with already-diagnosed IPF Receives: Device: Lung auscultation; Device: Lung ultrasound; Other: Quality of Life's questionnaires; Diagnostic Test: Pulmonary functional tests | |
| NSIP patients (group 2) | Consenting adult patients >18 years old with with already-diagnosed non-specific interstitial pneumonia (NSIP) Receives: Device: Lung auscultation; Device: Lung ultrasound; Other: Quality of Life's questionnaires; Diagnostic Test: Pulmonary functional tests | |
| COPD patients (group 3) | Consenting adult patients >18 years old with with already-diagnosed chronic obstructive pulmonary disease (COPD) Receives: Device: Lung auscultation; Device: Lung ultrasound; Other: Quality of Life's questionnaires; Diagnostic Test: Pulmonary functional tests | |
| 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). Receives: Device: Lung auscultation; Device: Lung ultrasound; Other: Quality of Life's questionnaires; Diagnostic Test: Pulmonary functional tests |
What is being measured
The primary outcome is the main question the study is designed to answer.
To differentiate ILD from control subjects based on digital lung sounds recordings and LUS.
To determine the predictive performance of the AI algorithm-evaluated lung auscultation and LUS in the identification and risk stratification of ILD signatures from control subjects described in terms of descriptive statistics, area under the receiver operating characteristic curve, sensitivity, specificity, positive and negative predictive values, and likelihood ratios (95% confidence intervals).
Digital lung sounds will be transformed to Mel Frequency Cepstrum Coefficients. Several data augmentation techniques will be explored. The effect of each pre-processing method will be tested. The best performing approach according to sensitivity and specificity will be reported. This dataset will then be fed into a various deep learning networks with aggregation strategies for binary classification into positive vs negative for diagnostic results for:
- ILD or control subjects
- ILD or COPD
- (If ILD+) IPF or NSIP
The same prediction will also be made using LUS images.
Measured: 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).
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.
To determine the ILD clinical severity predictive performance of the DeepBreath algorithm based on human digital lung sounds recordings and LUS, risk stratification will use multiclass or regression according to grading scales obtained from:
- K-BILD and CAT impact of life questionnaire.
- Lung function tests (Forced Expiratory Volume in 1 sec, Forced vital capacity, Forced Expiratory Volume in 1 sec/Forced vital capacity, Total lung capacity, functional respiratory capacity, Transfer capacity for carbon monoxide, Alveolar Volume).
- High-Resolution Computed Tomography (severity markers that will be used are: traction bronchiectasis, presence of honeycombing, ground glass opacities, reticulation, emphysema. Chest CT-scans will be reviewed independently by two radiologists blinded to each other).
Measured: 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).
Performance of the DeepBreath algorithm to subcategorize ILD by discriminating digital lung sounds recordings and LUS (i.e. physiological parameters).
The performance of the DeepBreath algorithm to determine the subcategories of ILD such as IPF and NSIP based on digital lungs sounds and LUS according to gold standard diagnosis:
- IPF follows the Fleischner Society Consensus criteria.
- NSIP diagnosis follows the American Thoracic Society classification.
Measured: 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 (6)
Performance of human expert-identified acoustic signatures.
Comparison of the predictive performance of human expert-identified acoustic signatures in the predictive tasks described above in the primary outcomes (Kappa coefficient).
Measured: During the data analysis period (i.e., after the 60-minute study intervention period).
Agreement of human labels with objectively clustered pathological sounds by machine learning.
To quantify the agreement of human labels with objectively clustered pathological sounds by machine learning (ie, the DeepBreath AI algorithm).
Measured: During the data analysis period (i.e., after the 60-minute study intervention period).
Diagnostic performance of DeepBreath to detect crackles in IPF patients.
Diagnostic performance of the AI algorithm (DeepBreath) trained to detect crackles in IPF patients.
Measured: During the data analysis period (i.e., after the 60-minute study intervention period).
To test whether performance of DeepBreath could be improved using clinical features (i.e., signs, respiratory symptoms, demographics, medical history and basic paraclinical tests).
To explore the utility of adding clinical data collected at enrolment including demographic information (age and sex), several binary clinical symptoms (respiratory symptoms), medical history and basic paraclinical tests to improve the accuracy of the DeepBreath algorithm in detecting IPF from control subjects or COPD. Clinical data will be explored for their predictive capacity in the above tasks and added to the breath sound analysis either as an Support vector machine or in conditional feature extraction upstream of the neural network.
Measured: During the data analysis period (i.e., after the 60-minute study intervention period)
K-BILD
King's brief Interstitial Lung Disease Health Status: the K-BILD health status questionnaire is a 15 item validated, self-completed heath status questionnaire. It has three domains: breathlessness and activities, psychological and chest symptoms. The K-BILD domain and total score ranges are 0-100, with the higher scores corresponding with better health-related quality of life.
This questionnaire will be used to assess the Impact of ILD on subjects' health-related quality of life. It will take about 3 minutes to complete this questionnaire.
Measured: Baseline
CAT
COPD assessment test: the CAT health status questionnaire is a 8 item validated, self-completed heath status questionnaire. The total CAT score ranges from 0 to 40 where 0 represents no symptoms and 40 very bad symptoms.
This questionnaire will be used to assess the Impact of COPD on subjects' health-related quality of life. It will take about 3 minutes to complete this questionnaire.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Apr 2022
- Study start1 Apr 2023
- Primary completion6 Oct 2024 (estimated)
- Study completion31 Oct 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Lung; Disease, Interstitial, With FibrosisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 160 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Pediatric Clinical Research Platform
- Sponsor type
- University, hospital or other
- Collaborators
- University Hospital, Geneva, Swiss Federal Institute of Technology, Hôpital du Valais
- Responsible party
- Sponsor
- Investigators
- Pierre-Olivier Bridevaux, Prof., Principal investigator, Hôpital du Valais, SwitzerlandJohan N. Siebert, MD, Study director, Geneva University Hospitals, Switzerland
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05318599
- Sponsor's protocol ID
- IPFoscope
- Acronym
- DeepBreath
- Status last verified
- Apr 2024
- First submitted
- 30 Mar 2022
- Last updated
- 12 Apr 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: All pertinent data generated or analysed during this study will be included in the published articles (and supplementary information files). An anonymous copy of the final (anonymized) datasets (but not digitized lung sounds) used and/or analyzed during the current study will be available from the corresponding author (see access criteria).
Publications
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.
PubMed 37264374
Study at a glance
From the registry record, last updated by the sponsor on 12 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Diagnostic and Risk-stratification for Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease in Digital Lung Auscultations
- ClinicalTrials.gov ID
- NCT05318599
- Acronym / sponsor ID
- DeepBreath / IPFoscope
- 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
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 160 participants (estimated)
- Lead sponsor
- Pediatric Clinical Research PlatformUniversity, hospital or other
- Collaborators
- University Hospital, Geneva, Swiss Federal Institute of Technology, Hôpital du Valais
- Study start
- 1 Apr 2023 (actual)
- Primary completion
- 6 Oct 2024 (estimated)
- Study completion
- 31 Oct 2024 (estimated)
- First posted
- 8 Apr 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| IPF patients (group 1) | – | Device: Lung auscultation, Device: Lung ultrasound, Other: Quality of Life's questionnaires, Diagnostic Test: Pulmonary functional testsConsenting adult patients >18 years old with with already-diagnosed IPF |
| NSIP patients (group 2) | – | Device: Lung auscultation, Device: Lung ultrasound, Other: Quality of Life's questionnaires, Diagnostic Test: Pulmonary functional testsConsenting adult patients >18 years old with with already-diagnosed non-specific interstitial pneumonia (NSIP) |
| COPD patients (group 3) | – | Device: Lung auscultation, Device: Lung ultrasound, Other: Quality of Life's questionnaires, Diagnostic Test: Pulmonary functional testsConsenting adult patients >18 years old with with already-diagnosed chronic obstructive pulmonary disease (COPD) |
| Control subjects (group 4) | – | Device: Lung auscultation, Device: Lung ultrasound, Other: Quality of Life's questionnaires, Diagnostic Test: Pulmonary functional testsConsenting 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). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (9 listed)
- 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.
Key exclusion criteria (7 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Lung auscultationDigital lung auscultation with the Eko core digital stethoscope (Eko Devices, Inc., CA, USA). | Device | COPD patients (group 3), Control subjects (group 4), IPF patients (group 1), NSIP patients (group 2) |
| Lung ultrasoundLung ultrasonography | Device | COPD patients (group 3), Control subjects (group 4), IPF patients (group 1), NSIP patients (group 2) |
| Quality of Life's questionnairesImpact of the diseases on subjects' health-related quality of life measured with standardized questionnaires (K-BILD, CAT) | Other | COPD patients (group 3), Control subjects (group 4), IPF patients (group 1), NSIP patients (group 2) |
| Pulmonary functional testsSpirometry, body-plethysmographic parameters and lung diffusion capacity for carbon monoxide will be measured. | Diagnostic test | COPD patients (group 3), Control subjects (group 4), IPF patients (group 1), NSIP patients (group 2) |
Primary outcome
To differentiate ILD from control subjects based on digital lung sounds recordings and LUS.
Time frame: 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).
To determine the predictive performance of the AI algorithm-evaluated lung auscultation and LUS in the identification and risk stratification of ILD signatures from control subjects described in terms of descriptive statistics, area under the receiver operating characteristic curve, sensitivity, specificity, positive and negative predictive values, and likelihood ratios (95% confidence intervals). Digital lung sounds will be transformed to Mel Frequency Cepstrum Coefficients. Several data augmentation techniques will be explored. The effect of each pre-processing method will be tested. The best performing approach according to sensitivity and specificity will be reported. This dataset will then be fed into a various deep learning networks with aggregation strategies for binary classification into positive vs negative for diagnostic results for: * ILD or control subjects * ILD or COPD * (If ILD+) IPF or NSIP The same prediction will also be made using LUS images.
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.
Time frame: 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).
To determine the ILD clinical severity predictive performance of the DeepBreath algorithm based on human digital lung sounds recordings and LUS, risk stratification will use multiclass or regression according to grading scales obtained from: * K-BILD and CAT impact of life questionnaire. * Lung function tests (Forced Expiratory Volume in 1 sec, Forced vital capacity, Forced Expiratory Volume in 1 sec/Forced vital capacity, Total lung capacity, functional respiratory capacity, Transfer capacity for carbon monoxide, Alveolar Volume). * High-Resolution Computed Tomography (severity markers that will be used are: traction bronchiectasis, presence of honeycombing, ground glass opacities, reticulation, emphysema. Chest CT-scans will be reviewed independently by two radiologists blinded to each other).
Performance of the DeepBreath algorithm to subcategorize ILD by discriminating digital lung sounds recordings and LUS (i.e. physiological parameters).
Time frame: 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).
The performance of the DeepBreath algorithm to determine the subcategories of ILD such as IPF and NSIP based on digital lungs sounds and LUS according to gold standard diagnosis: * IPF follows the Fleischner Society Consensus criteria. * NSIP diagnosis follows the American Thoracic Society classification.
6 secondary outcomes
Performance of human expert-identified acoustic signatures.
Time frame: During the data analysis period (i.e., after the 60-minute study intervention period).
Agreement of human labels with objectively clustered pathological sounds by machine learning.
Time frame: During the data analysis period (i.e., after the 60-minute study intervention period).
Diagnostic performance of DeepBreath to detect crackles in IPF patients.
Time frame: During the data analysis period (i.e., after the 60-minute study intervention period).
To test whether performance of DeepBreath could be improved using clinical features (i.e., signs, respiratory symptoms, demographics, medical history and basic paraclinical tests).
Time frame: During the data analysis period (i.e., after the 60-minute study intervention period)
K-BILD
Time frame: Baseline
CAT
Time frame: Baseline
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Apr 2024.
- Derived from this study: PubMed 37264374Siebert 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.
- Individual participant dataSharing planned: Yes. All pertinent data generated or analysed during this study will be included in the published articles (and supplementary information files). An anonymous copy of the final (anonymized) datasets (but not digitized lung sounds) used and/or analyzed during the current study will be available from the corresponding author (see access criteria).
Related trials for Lung; Disease, Interstitial, With Fibrosis
RecruitingPhase 2
Study to Evaluate the Efficacy, Safety, and Tolerability of PIPE 791 in Subjects With Idiopathic Pulmonary Fibrosis
Idiopathic Pulmonary Fibrosis
RecruitingPhase 3
Trial to Evaluate the Efficacy and Safety of LYT-100 (Deupirfenidone) Compared to Pirfenidone in Adults With Idiopathic Pulmonary Fibrosis (IPF)
Idiopathic Pulmonary Fibrosis (IPF)
Not yet recruitingPhase 1
A Study to Assess the Safety, Tolerability and Pharmacokinetics of Repeated Doses of Inhaled CHF10073 in Participants With Idiopathic Pulmonary Fibrosis
Idiopathic Pulmonary Fibrosis
Active, not recruiting
Optimizing the Follow-Up Journey in Interstitial Lung Disease: The OPTIMIZE-ILD-2 Trial
Interstitial Lung Disease (ILD), Fibrotic Interstitial Lung Disease
Active, not recruitingObservational study
Analysis of Specimens From Individuals With Pulmonary Fibrosis
Pulmonary Fibrosis, Healthy Volunteers
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Pierre-Olivier Bridevaux, Prof. principal investigator
Hôpital du Valais, Switzerland
Johan N. Siebert, MD study director
Geneva University Hospitals, Switzerland
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung; Disease, Interstitial, With Fibrosis and Pulmonary Disease, Chronic Obstructive, run by Pediatric Clinical Research Platform at 1 site in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 9 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 12 Apr 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Johan N. Siebert, MD (+41795534072, Johan.Siebert@hcuge.ch), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To differentiate ILD from control subjects based on digital lung sounds recordings and LUS. (time frame: 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).). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05318599, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Apr 2024.