Heart Valve DiseasesCAISNCT04445012
Cardiovascular Acoustics and an Intelligent Stethoscope
An observational study of Heart Valve Diseases, run by Papworth Hospital NHS Foundation Trust at 5 sites in United Kingdom.
Trial summary
The aim of the project is to develop an artificial intelligence software capable of analysing heart sounds to provide early diagnosis of a variety heart diseases at an early stage. Since the invention of the stethoscope by Laennec in 1816, the basic design has not changed significantly. Read more about this study
- Who is it for?
- Ages 14 days and older
- Conditions the study is for
- Heart Valve Diseases
- How many people
- 1,150 participants
- Where
- 5 sitesUnited Kingdom
- Study dates
- 24 Oct 2019to 31 Jan 2023
- 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 14 days and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients attending cardiology clinics at the investigation sites who will be having an echocardiogram
Who may be able to join
- Participant willing and able to give informed consent for participation in study
- Participant to undergo an echocardiogram as part of their routine assessment
Who cannot join
- Informed consent is not given
- New York Heart Association (NYHA) functional class = 4
Trial locations and contacts
5 sites in 1 country.
5 sites in 1 country
United Kingdom5 sites, 5 recruiting
- Recruiting
University Hospitals Birmingham NHS Foundation Trust
Birmingham, B15 2TH
Rick Steeds, MA, MBBS, MD, Rick.Steeds@uhb.nhs.uk
- Recruiting
Royal Papworth Hospital NHS Foundation Trust
Cambridge, CB2 0AY
Len Shapiro, MB BChir MD, Len.shapiro@papworth.nhs.uk
- Recruiting
Guy's and St Thomas' NHS Foundation Trust
London, SE1 9RT
Shakeel Qureshi, MBCHB, MRCP, Shakeel.Qureshi@gstt.nhs.uk
- Recruiting
King's College Hospital NHS Foundation Trust
London, SE5 9RS
Mark Monaghan, MSc, PhD, mark.monaghan@nhs.net
- Recruiting
Imperial College Healthcare NHS Trust
London, W2 1NY
Bushra Rana, MB BS, Bushra.Rana@nhs.net
About this study
Official title: Cardiovascular Acoustics and an Intelligent Stethoscope. An Observational Study to Collect Heart Sound Recordings From Patients to Understand the Acoustic Characteristics of Murmurs and Develop an Artificially Intelligent Stethoscope.
The aim of the project is to develop an artificial intelligence software capable of analysing heart sounds to provide early diagnosis of a variety heart diseases at an early stage. Since the invention of the stethoscope by Laennec in 1816, the basic design has not changed significantly. Our software could be coupled with existing electronic stethoscopes to create an 'intelligent' stethoscope that could be used by healthcare assistants or practice nurses to screen for sound producing heart diseases. It could also be used at home by patients who would otherwise go undiagnosed.
The study investigators at Cambridge University Engineering Department (CUED) have developed a proof-of-concept AI algorithm to detect heart murmurs. However, in order to accurately detect the specific pathology and severity underlying the murmur, more heart sound recordings (matched with the ground truth from the patient's echocardiogram) are required. Patients presenting to one of the partner hospitals requiring an echocardiogram as part of their routine care will be invited to consent to this study. Participation will entail recording of a patient's heart sounds using an electronic stethoscope as well as collection of routine clinical data and a routine clinical echocardiogram at a single routine out patient visit.
Read the detailed description
This project will develop an AI algorithm which can be imported into a stethoscope to make it capable of automatically diagnosing any valve disease present and its severity. This will help GPs produce more accurate diagnoses, reduce costs by having fewer unnecessary referrals for echocardiogram, and produce more accurate diagnoses in countries where echocardiograms are not readily available due to their cost. Using a small sample of data as well as some which has been labelled by clinician auscultation, the team has created an award-winning AI algorithm capable of accurate detection of heart murmurs. However, in order to improve the accuracy and capability of this system more heart sound recordings from a range of diseases (matched with echocardiogram diagnosis) are required. The key to the success of this study will be to produce an AI algorithm that is more accurate than different grades of doctors at detecting the specific abnormality and severity underlying a heart murmur. This methodology will also provide a comprehensive study on acoustic characteristics of different heart sounds. So far all the acoustic characteristics of heart sounds taught to medical students are based on subjective opinion. This study will be able to objectively analyse these acoustic characteristics.
- Conditions
- Heart Valve Diseases
- Medical subject headings
- Heart Valve Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Aortic Valve Stenosis (Adults) | 30 patients with mild, 30 with moderate, and 30 with severe mitral regurgitation, using the BSE gradings [Wharton 2014]. | |
| Mitral Valve Regurgitation (Adults) | 30 patients with mild, 30 with moderate, and 30 with severe mitral regurgitation, using the BSE gradings [Wharton 2014]. | |
| Aortic Regurgitation (Adults) | 30 patients with mild, 30 with moderate, and 30 with severe aortic regurgitation, using the BSE gradings [Wharton 2014]. | |
| Mitral Stenosis (Adults) | 30 patients with mild, 30 with moderate, and 30 with severe mitral stenosis, using the BSE gradings [Wharton 2014]. | |
| Mixed Valve Disease (Adults) | 30 patients with mild, 30 with moderate, and 30 with severe mixed valve disease. Overall classification based on the most severe disease using the BSE gradings [Wharton 2014]. | |
| Ventricular Septal Defects (Paediatric Patients) | 36 paediatric patients with mild, 37 with moderate, and 36 with severe ventricular septal defects, using gradings from [Samaan 1970]. | |
| Aortic Stenosis (Paediatric Patients) | 36 paediatric patients with mild, 37 with moderate, and 36 with severe aortic stenosis | |
| Pulmonary Stenosis (Paediatric Patients) | 36 paediatric patients with mild, 37 with moderate, and 36 with severe pulmonary stenosis. | |
| Patent Ductus Arteriosus (Paediatric Patients) | 36 paediatric patients with mild, 37 with moderate, and 36 with severe patent ductus arteriosus, graded using ductal size [Arlettaz 2017]. | |
| No Disease (Paediatric Patients) | 264 paediatric patients with no heart disease. Note that we are only taking recordings from those who have been referred for an echocardiogram with a suspected heart condition but are subsequently found to have no heart disease. |
What is being measured
The primary outcome is the main question the study is designed to answer.
To assess specificity of an algorithm for detecting clinically significant valve disease and congenital heart disease relative to the performance of General Practitioners
We will obtain 4, 15 second heart sound recordings from patients (at the Aortic, Pulmonary, Mitral, and Tricuspid sites) using a Littmann 3200 electronic stethoscope.
Measured: Day 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jun 2020
- Study start24 Oct 2019
- Primary completion30 Mar 2022 (estimated)
- Study completion31 Jan 2023 (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 Heart Valve DiseasesStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,150 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Papworth Hospital NHS Foundation Trust
- Sponsor type
- Other government
- Responsible party
- Sponsor
- Investigators
- Bushra Rana, MB BS, Principal investigator, Imperial College Healthcare NHS Trust
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04445012
- Sponsor's protocol ID
- P02087
- Acronym
- CAIS
- Status last verified
- Sep 2021
- First submitted
- 21 Jun 2020
- Last updated
- 16 Sep 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
McDonald A, Gales M, Rana BS, Shun-Shin M, Lukban BF, Adrego R, Papachristidis A, Hajee F, Shapiro L, Wilson J, Prothero T, Kennedy A, Myerson S, Prendergast B, Bachtiger P, Kelshiker MA, Peters N, Steeds R, Agarwal A. Development and validation of AI-Enhanced auscultation for valvular heart disease screening through a multi-centre study. NPJ Cardiovasc Health. 2026 Feb 10;3:5. doi: 10.1038/s44325-026-00103-y. eCollection 2026.
PubMed 41684548
Study at a glance
From the registry record, last updated by the sponsor on 16 Sep 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Cardiovascular Acoustics and an Intelligent Stethoscope. An Observational Study to Collect Heart Sound Recordings From Patients to Understand the Acoustic Characteristics of Murmurs and Develop an Artificially Intelligent Stethoscope.
- ClinicalTrials.gov ID
- NCT04445012
- Acronym / sponsor ID
- CAIS / P02087
- Conditions
- Heart Valve Diseases
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,150 participants (estimated)
- Lead sponsor
- Papworth Hospital NHS Foundation TrustOther government
- Study start
- 24 Oct 2019 (actual)
- Primary completion
- 30 Mar 2022 (estimated)
- Study completion
- 31 Jan 2023 (estimated)
- First posted
- 24 Jun 2020 (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.
The aim of the project is to develop an artificial intelligence software capable of analysing heart sounds to provide early diagnosis of a variety heart diseases at an early stage. Since the invention of the stethoscope by Laennec in 1816, the basic design has not changed significantly. Our software could be coupled with existing electronic stethoscopes to create an 'intelligent' stethoscope that could be used by healthcare assistants or practice nurses to screen for sound producing heart diseases. It could also be used at home by patients who would otherwise go undiagnosed.
The study investigators at Cambridge University Engineering Department (CUED) have developed a proof-of-concept AI algorithm to detect heart murmurs. However, in order to accurately detect the specific pathology and severity underlying the murmur, more heart sound recordings (matched with the ground truth from the patient's echocardiogram) are required. Patients presenting to one of the partner hospitals requiring an echocardiogram as part of their routine care will be invited to consent to this study. Participation will entail recording of a patient's heart sounds using an electronic stethoscope as well as collection of routine clinical data and a routine clinical echocardiogram at a single routine out patient visit.
Detailed description
This project will develop an AI algorithm which can be imported into a stethoscope to make it capable of automatically diagnosing any valve disease present and its severity. This will help GPs produce more accurate diagnoses, reduce costs by having fewer unnecessary referrals for echocardiogram, and produce more accurate diagnoses in countries where echocardiograms are not readily available due to their cost. Using a small sample of data as well as some which has been labelled by clinician auscultation, the team has created an award-winning AI algorithm capable of accurate detection of heart murmurs. However, in order to improve the accuracy and capability of this system more heart sound recordings from a range of diseases (matched with echocardiogram diagnosis) are required. The key to the success of this study will be to produce an AI algorithm that is more accurate than different grades of doctors at detecting the specific abnormality and severity underlying a heart murmur. This methodology will also provide a comprehensive study on acoustic characteristics of different heart sounds. So far all the acoustic characteristics of heart sounds taught to medical students are based on subjective opinion. This study will be able to objectively analyse these acoustic characteristics.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 10
| Arm | Type | Receives |
|---|---|---|
| Aortic Valve Stenosis (Adults) | – | –30 patients with mild, 30 with moderate, and 30 with severe mitral regurgitation, using the BSE gradings [Wharton 2014]. |
| Mitral Valve Regurgitation (Adults) | – | –30 patients with mild, 30 with moderate, and 30 with severe mitral regurgitation, using the BSE gradings [Wharton 2014]. |
| Aortic Regurgitation (Adults) | – | –30 patients with mild, 30 with moderate, and 30 with severe aortic regurgitation, using the BSE gradings [Wharton 2014]. |
| Mitral Stenosis (Adults) | – | –30 patients with mild, 30 with moderate, and 30 with severe mitral stenosis, using the BSE gradings [Wharton 2014]. |
| Mixed Valve Disease (Adults) | – | –30 patients with mild, 30 with moderate, and 30 with severe mixed valve disease. Overall classification based on the most severe disease using the BSE gradings [Wharton 2014]. |
| Ventricular Septal Defects (Paediatric Patients) | – | –36 paediatric patients with mild, 37 with moderate, and 36 with severe ventricular septal defects, using gradings from [Samaan 1970]. |
| Aortic Stenosis (Paediatric Patients) | – | –36 paediatric patients with mild, 37 with moderate, and 36 with severe aortic stenosis |
| Pulmonary Stenosis (Paediatric Patients) | – | –36 paediatric patients with mild, 37 with moderate, and 36 with severe pulmonary stenosis. |
| Patent Ductus Arteriosus (Paediatric Patients) | – | –36 paediatric patients with mild, 37 with moderate, and 36 with severe patent ductus arteriosus, graded using ductal size [Arlettaz 2017]. |
| No Disease (Paediatric Patients) | – | –264 paediatric patients with no heart disease. Note that we are only taking recordings from those who have been referred for an echocardiogram with a suspected heart condition but are subsequently found to have no heart disease. |
Who may qualify
- Ages
- 14 days and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients attending cardiology clinics at the investigation sites who will be having an echocardiogram
Key inclusion criteria (2 listed)
- Participant willing and able to give informed consent for participation in study
- Participant to undergo an echocardiogram as part of their routine assessment
Key exclusion criteria (2 listed)
- Informed consent is not given
- New York Heart Association (NYHA) functional class = 4
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
To assess specificity of an algorithm for detecting clinically significant valve disease and congenital heart disease relative to the performance of General Practitioners
Time frame: Day 1
We will obtain 4, 15 second heart sound recordings from patients (at the Aortic, Pulmonary, Mitral, and Tricuspid sites) using a Littmann 3200 electronic stethoscope.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Sep 2021.
- Derived from this study: PubMed 41684548McDonald A, Gales M, Rana BS, Shun-Shin M, Lukban BF, Adrego R, Papachristidis A, Hajee F, Shapiro L, Wilson J, Prothero T, Kennedy A, Myerson S, Prendergast B, Bachtiger P, Kelshiker MA, Peters N, Steeds R, Agarwal A. Development and validation of AI-Enhanced auscultation for valvular heart disease screening through a multi-centre study. NPJ Cardiovasc Health. 2026 Feb 10;3:5. doi: 10.1038/s44325-026-00103-y. eCollection 2026.
- 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.
Bushra Rana, MB BS principal investigator
Imperial College Healthcare NHS Trust
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Valve Diseases, run by Papworth Hospital NHS Foundation Trust at 5 sites in United Kingdom.
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 2 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 16 Sep 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in United Kingdom. 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 Nicky Watson, MSc (01223639684, nicky.watson2@nhs.net), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To assess specificity of an algorithm for detecting clinically significant valve disease and congenital heart disease relative to the performance of General Practitioners (time frame: Day 1). 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 NCT04445012, 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 16 Sep 2021.