RespiratoryNCT05268263
Feasibility of AI-based Classification of Normal, Wheeze and Crackle Sounds From Stethoscope in Clinical Settings
A study testing Artificial Intelligence Algorithm in people with Respiratory and Lung, run by Innova Smart Technologies (Pvt.) Ltd at 1 site in Pakistan.
Trial summary
Assessing the feasibility and testing the accuracy of the developed artificial intelligence algorithms for detection of wheezes and crackles in patients with lung pathologies in clinical settings on unseen local patient data acquired through three digital stethoscopes. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Respiratory, Lung
- What is being tested?
- Artificial Intelligence Algorithm
- Type of treatment
- Medical device
- How many people
- 60 participants
- Where
- 1 sitePakistan
- Study dates
- 6 Jan 2022to 22 Feb 2022
- Phase
- Interventional
- 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 Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Ages all
- Written consent provided
Who cannot join
- Subject condition unstable
- Chest wall deformity or wounds in adhesive application areas
- Written consent not provided
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Evaluating the Feasibility of Artificial Intelligence Algorithms in Clinical Settings for Classification of Normal, Wheeze and Crackle Sounds Acquired From a Digital Stethoscope
Assessing the feasibility and testing the accuracy of the developed artificial intelligence algorithms for detection of wheezes and crackles in patients with lung pathologies in clinical settings on unseen local patient data acquired through three digital stethoscopes.
- Conditions
- Respiratory, Lung
- Being tested
- Artificial Intelligence Algorithm
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence AlgorithmDevice
The enrolled population will include patients with a history of lung pathologies. Artificial intelligence-based models are developed for classification of wheezes, crackles and normal lung sounds. These AI models will be tested and assessed on local lung sounds clinical data.
What is being measured
The primary outcome is the main question the study is designed to answer.
Testing the accuracy of artificial intelligence models for detection of wheeze, crackles, and normal lung sounds by measuring the sensitivity and specificity
Artificial intelligence models are trained on lung sounds collected from three different digital stethoscopes named NoaScope, eSteth, and Littmann individually. Data from all three digital stethoscopes is also merged to train separate AI models. These trained AI models will be evaluated based on sensitivity which is the ability to correctly identify wheezes and crackles, and specificity which is the ability to correctly identify normal lung sounds. True positive (TP), true negative (TN), false positive (FP), and false-negative (FN) values will be used to calculate sensitivity \& specificity using the following expressions.
Sensitivity: TP/TP+FN Specificity: TN/TN+FP
Measured: 2 months
Clinical validation of AI models for detection of wheeze, crackles, and normal lung sounds by comparison with gold standard
AI models will be tested for their clinical feasibility through comparison of results obtained from AI models with that of the gold standard by measuring positive and negative agreement (NPA \& PPA). The gold standard is the label given to each lung sound recording by an experienced consultant pulmonologist. The AI model is blinded to these labels and is tested independently for detection of normal lung sounds, wheezes, and crackles
Measured: 2 months
Secondary outcomes (1)
Performance analysis of three digital stethoscopes: Littmann, NoaScope, and eSteth
Performance analysis of three digital stethoscopes NoaScope, eSteth, and Littmann will be evaluated using the sensitivity and specificity achieved by each stethoscope. True positive (TP), true negative (TN), false positive (FP), and false-negative (FN) values will be used to calculate sensitivity \& specificity using the following expressions.
Sensitivity: TP/TP+FN Specificity: TN/TN+FP
Measured: 2 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Mar 2022
- Study start6 Jan 2022
- Primary completion22 Feb 2022
- Study completion22 Feb 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for RespiratoryStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 60 participants (actual)
Sponsor and investigators
- Lead sponsor
- Innova Smart Technologies (Pvt.) Ltd
- Sponsor type
- University, hospital or other
- Collaborators
- Lady Reading Hospital, Pakistan, NOABIO LLC
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05268263
- Sponsor's protocol ID
- Pulmo AI LRH
- Status last verified
- Apr 2023
- First submitted
- 8 Jan 2022
- Last updated
- 6 Apr 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating the Feasibility of Artificial Intelligence Algorithms in Clinical Settings for Classification of Normal, Wheeze and Crackle Sounds Acquired From a Digital Stethoscope
- ClinicalTrials.gov ID
- NCT05268263
- Acronym / sponsor ID
- Pulmo AI LRH
- Conditions
- Respiratory, Lung
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 60 participants (actual)
- Lead sponsor
- Innova Smart Technologies (Pvt.) LtdUniversity, hospital or other
- Collaborators
- Lady Reading Hospital, Pakistan, NOABIO LLC
- Study start
- 6 Jan 2022 (actual)
- Primary completion
- 22 Feb 2022 (actual)
- Study completion
- 22 Feb 2022 (actual)
- First posted
- 7 Mar 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.
Assessing the feasibility and testing the accuracy of the developed artificial intelligence algorithms for detection of wheezes and crackles in patients with lung pathologies in clinical settings on unseen local patient data acquired through three digital stethoscopes.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (2 listed)
- Ages all
- Written consent provided
Key exclusion criteria (3 listed)
- Subject condition unstable
- Chest wall deformity or wounds in adhesive application areas
- Written consent not provided
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 |
|---|---|---|
| Artificial Intelligence AlgorithmThe enrolled population will include patients with a history of lung pathologies. Artificial intelligence-based models are developed for classification of wheezes, crackles and normal lung sounds. These AI models will be tested and assessed on local lung sounds clinical data. | Device | – |
Primary outcome
Testing the accuracy of artificial intelligence models for detection of wheeze, crackles, and normal lung sounds by measuring the sensitivity and specificity
Time frame: 2 months
Artificial intelligence models are trained on lung sounds collected from three different digital stethoscopes named NoaScope, eSteth, and Littmann individually. Data from all three digital stethoscopes is also merged to train separate AI models. These trained AI models will be evaluated based on sensitivity which is the ability to correctly identify wheezes and crackles, and specificity which is the ability to correctly identify normal lung sounds. True positive (TP), true negative (TN), false positive (FP), and false-negative (FN) values will be used to calculate sensitivity \& specificity using the following expressions. Sensitivity: TP/TP+FN Specificity: TN/TN+FP
Clinical validation of AI models for detection of wheeze, crackles, and normal lung sounds by comparison with gold standard
Time frame: 2 months
AI models will be tested for their clinical feasibility through comparison of results obtained from AI models with that of the gold standard by measuring positive and negative agreement (NPA \& PPA). The gold standard is the label given to each lung sound recording by an experienced consultant pulmonologist. The AI model is blinded to these labels and is tested independently for detection of normal lung sounds, wheezes, and crackles
1 secondary outcome
Performance analysis of three digital stethoscopes: Littmann, NoaScope, and eSteth
Time frame: 2 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Apr 2023.
- Individual participant dataSharing planned: No
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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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Intelligence Algorithm in people with Respiratory and Lung, run by Innova Smart Technologies (Pvt.) Ltd at 1 site in Pakistan.
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 2 inclusion and 3 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: finished, as of the sponsor's last update on 6 Apr 2023. 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 Pakistan. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Testing the accuracy of artificial intelligence models for detection of wheeze, crackles, and normal lung sounds by measuring the sensitivity and specificity (time frame: 2 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 6 Apr 2023; AzTrials last refreshed its copy on 7 Nov 2026.