Cardiac DiseaseU-CAGNCT05265585
Validation of an Artificial Intelligence Algorithm Identifying Echocardiographic Reference Views. Ultrasound - Cardiac Acquisition Guide
An observational study of Cardiac Disease, run by University Hospital, Bordeaux at 1 site in France.
Trial summary
Echocardiography is the examination of choice for the study of cardiac pathologies. Beyond its use by cardiologists, the interest of echocardiography for other medical specialties has already been demonstrated, in particular in intensive care in the case of haemodynamic failure, or in intra and extra hospital… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cardiac Disease
- What is being tested?
- Ultrasound - Cardiac Acquisition
- How many people
- 75 participants
- Where
- 1 siteFrance
- Study dates
- 19 Jun 2020to 11 Aug 2020
- 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: The patients included in the study are adult patients having an echocardiography programmed in the echocardiography laboratory of the Bordeaux University Hospital.
Who may be able to join
- Patients (male or female) over 18 years of age,
- Patient having an echocardiography examination scheduled at the echocardiography laboratory of the Bordeaux University Hospital,
- Patient having given his non-opposition to participate in the research (at the latest on the day of inclusion and before any examination required by the research),
- Subjects affiliated to or benefiting from a social security scheme,
- Women of childbearing age benefiting from effective contraception.
Who cannot join
- Person subject to a legal protection measure (safeguard of justice, guardianship or curators),
- Person deprived of liberty by judicial or administrative decision,
- Person who is unable to give his/her non-opposition,
- Pregnant or breastfeeding women.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Prospective Validation of an Artificial Intelligence Algorithm Identifying Echocardiographic Reference Views. Ultrasound - Cardiac Acquisition Guide
Echocardiography is the examination of choice for the study of cardiac pathologies. Beyond its use by cardiologists, the interest of echocardiography for other medical specialties has already been demonstrated, in particular in intensive care in the case of haemodynamic failure, or in intra and extra hospital emergency medicine for the initial assessment of chest pain or dyspnoea.
Echocardiography also plays a major role in screening for heart disease, particularly valvular heart disease. In countries with very limited access to echocardiography, there is a major under-diagnosis of heart valve disease, including rheumatic fever, which affects 30 million people and causes 305,000 deaths worldwide. As this is a global public health problem, recommendations were drafted in 2012 to organise and facilitate echocardiographic screening of populations at risk.
Read more
The expansion of the use of echocardiography has been catalysed by the miniaturisation of ultrasound systems and the reduction in their price. Recently, probes directly connected to a tablet or phone have been developed at a limited cost.
It is therefore possible to consider these ultrasound scanners as the new stethoscope that could be used by any health professional.
In order to be effective, the last limit to this democratisation is the training, and in particular that of non-specialists (i.e. non-cardiologists).
Echocardiography remains an examination that requires anatomical knowledge and practice. Performing an echocardiogram involves visualising the heart from different points on the chest. The three main points are in the left paraspinal area, at the apex of the heart and under the sternum. From these areas, the operator must obtain several reference views which are strictly defined in order to be able to correctly observe the different cardiac structures and make comparable measurements from one examination and clinician to another.
It is therefore necessary first of all to learn how to handle the probe and to be able to obtain the reference views. The morphology of the patient, the shape of the thorax, the exact position of the heart, the movements of the heart according to the position of the patient and his breathing are all elements to be taken into account and make each examination different from the previous one.
Read the detailed description
In response to this problem, several teams have taken advantage of advances in deep learning, particularly in the field of computer vision, to help non-specialists obtain these reference views. Using convolutional neural networks, several teams have developed algorithms for identifying and distinguishing these views. The objective of this work is to provide assistance to the operator by identifying in real time the ultrasound image obtained as a reference view.
Since January 2019, the echocardiography laboratory headed by Prof. Stéphane Lafitte and DESKi, a Bordeaux-based start-up specialising in deep learning and medical imaging, have been working on this type of solution. In particular, they were able to develop an algorithm based on retrospective data that classifies images according to 7 reference views (parasternal long axis, parasternal short axis, apical 4-3-2 cavities, sub costal 4 cavities and the inferior vena cava) and a class representing ultrasound images that do not correspond to any of these views.
The particularities of this work lie mainly in the fact that the algorithm only detects views with sufficient quality (echogenicity and visible anatomical part in the image) for a reliable analysis and that the architecture of the neural network is compatible with a real time use on a smart phone.
Independently of the teams, all these algorithms were built and validated from retrospective data, i.e. loops of ultrasound images recorded by cardiologists during standard echocardiography examinations. In these recordings, the cardiologists keep only the images corresponding to the reference views. The cardiologist then attaches each image loop or acquisition to a reference view. From these recordings labelled by the cardiologists and by a learning method, the algorithms learn to detect and distinguish these reference views. The algorithms are then validated on a sample that has not been used for training, by comparing their results with the labelling performed by the cardiologists.
The limitation of this validation is that it takes little account of the behaviour of the algorithm when confronted with images that are not reference views.
Indeed, before recording these reference views, the operator searches for the position of the probe that offers the best view by moving it over the patient's torso. This whole scanning phase is performed during the standard examination without being recorded. None of these solutions has therefore been validated prospectively on acquisitions including the scanning phase of the reference views.
Through a prospective monocentric study, the objective of the research is to compare the labelling carried out by the algorithm and that carried out by cardiologists from acquisitions including the recording of the reference view search phase and obtained as part of routine care in the echocardiography laboratory.
- Conditions
- Cardiac Disease
- Being tested
- Ultrasound - Cardiac Acquisition
- Medical subject headings
- Heart Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Ultrasound - Cardiac AcquisitionDiagnostic test
The evaluation of the algorithm takes place on patients with an indication for echocardiography.
This examination is done in a standard way with the potential specific explorations related to the indication of the examination. During the echocardiography, the operator records the search phase for the following reference views:
- Parasternal window (Parasternal long axis, Parasternal minor axis)
- Apical window (Apical 4 cavities, Apical 3 cavities, Apical 2 cavities
- Sub costal window, Sub costal 4 cavities, Inferior vena cava)
| Study group | Role | What they receive |
|---|---|---|
| Echocardiography group | Not described Receives: Diagnostic Test: Ultrasound - Cardiac Acquisition |
What is being measured
The primary outcome is the main question the study is designed to answer.
Assessment of algorithm - cardiologist concordance
Measured by the percentage of images for which the labelling of the algorithm and the labelling of the cardiologist are identical. This percentage is calculated per examination.
Measured: Day 0
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Mar 2022
- Study start19 Jun 2020
- Primary completion11 Aug 2020
- Study completion11 Aug 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cardiac DiseaseStudies 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 only
- Time perspective
- Prospective
- Enrolment
- 75 participants (actual)
Sponsor and investigators
- Lead sponsor
- University Hospital, Bordeaux
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Stéphane LAFITTE, MD PhD, Principal investigator, University Hospital, BordeauxBertrand MOAL, Study chair, DESKi
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05265585
- Sponsor's protocol ID
- CHUBX 2020/02
- Acronym
- U-CAG
- Status last verified
- Mar 2022
- First submitted
- 22 Feb 2022
- Last updated
- 21 Mar 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Mar 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Validation of an Artificial Intelligence Algorithm Identifying Echocardiographic Reference Views. Ultrasound - Cardiac Acquisition Guide
- ClinicalTrials.gov ID
- NCT05265585
- Acronym / sponsor ID
- U-CAG / CHUBX 2020/02
- Conditions
- Cardiac Disease
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 75 participants (actual)
- Lead sponsor
- University Hospital, BordeauxUniversity, hospital or other
- Study start
- 19 Jun 2020 (actual)
- Primary completion
- 11 Aug 2020 (actual)
- Study completion
- 11 Aug 2020 (actual)
- First posted
- 3 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.
Echocardiography is the examination of choice for the study of cardiac pathologies. Beyond its use by cardiologists, the interest of echocardiography for other medical specialties has already been demonstrated, in particular in intensive care in the case of haemodynamic failure, or in intra and extra hospital emergency medicine for the initial assessment of chest pain or dyspnoea.
Echocardiography also plays a major role in screening for heart disease, particularly valvular heart disease. In countries with very limited access to echocardiography, there is a major under-diagnosis of heart valve disease, including rheumatic fever, which affects 30 million people and causes 305,000 deaths worldwide. As this is a global public health problem, recommendations were drafted in 2012 to organise and facilitate echocardiographic screening of populations at risk.
The expansion of the use of echocardiography has been catalysed by the miniaturisation of ultrasound systems and the reduction in their price. Recently, probes directly connected to a tablet or phone have been developed at a limited cost.
It is therefore possible to consider these ultrasound scanners as the new stethoscope that could be used by any health professional.
In order to be effective, the last limit to this democratisation is the training, and in particular that of non-specialists (i.e. non-cardiologists).
Echocardiography remains an examination that requires anatomical knowledge and practice. Performing an echocardiogram involves visualising the heart from different points on the chest. The three main points are in the left paraspinal area, at the apex of the heart and under the sternum. From these areas, the operator must obtain several reference views which are strictly defined in order to be able to correctly observe the different cardiac structures and make comparable measurements from one examination and clinician to another.
It is therefore necessary first of all to learn how to handle the probe and to be able to obtain the reference views. The morphology of the patient, the shape of the thorax, the exact position of the heart, the movements of the heart according to the position of the patient and his breathing are all elements to be taken into account and make each examination different from the previous one.
Detailed description
In response to this problem, several teams have taken advantage of advances in deep learning, particularly in the field of computer vision, to help non-specialists obtain these reference views. Using convolutional neural networks, several teams have developed algorithms for identifying and distinguishing these views. The objective of this work is to provide assistance to the operator by identifying in real time the ultrasound image obtained as a reference view.
Since January 2019, the echocardiography laboratory headed by Prof. Stéphane Lafitte and DESKi, a Bordeaux-based start-up specialising in deep learning and medical imaging, have been working on this type of solution. In particular, they were able to develop an algorithm based on retrospective data that classifies images according to 7 reference views (parasternal long axis, parasternal short axis, apical 4-3-2 cavities, sub costal 4 cavities and the inferior vena cava) and a class representing ultrasound images that do not correspond to any of these views.
The particularities of this work lie mainly in the fact that the algorithm only detects views with sufficient quality (echogenicity and visible anatomical part in the image) for a reliable analysis and that the architecture of the neural network is compatible with a real time use on a smart phone.
Independently of the teams, all these algorithms were built and validated from retrospective data, i.e. loops of ultrasound images recorded by cardiologists during standard echocardiography examinations. In these recordings, the cardiologists keep only the images corresponding to the reference views. The cardiologist then attaches each image loop or acquisition to a reference view. From these recordings labelled by the cardiologists and by a learning method, the algorithms learn to detect and distinguish these reference views. The algorithms are then validated on a sample that has not been used for training, by comparing their results with the labelling performed by the cardiologists.
The limitation of this validation is that it takes little account of the behaviour of the algorithm when confronted with images that are not reference views.
Indeed, before recording these reference views, the operator searches for the position of the probe that offers the best view by moving it over the patient's torso. This whole scanning phase is performed during the standard examination without being recorded. None of these solutions has therefore been validated prospectively on acquisitions including the scanning phase of the reference views.
Through a prospective monocentric study, the objective of the research is to compare the labelling carried out by the algorithm and that carried out by cardiologists from acquisitions including the recording of the reference view search phase and obtained as part of routine care in the echocardiography laboratory.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Echocardiography group | – | Diagnostic Test: Ultrasound - Cardiac Acquisition |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The patients included in the study are adult patients having an echocardiography programmed in the echocardiography laboratory of the Bordeaux University Hospital.
Key inclusion criteria (5 listed)
- Patients (male or female) over 18 years of age,
- Patient having an echocardiography examination scheduled at the echocardiography laboratory of the Bordeaux University Hospital,
- Patient having given his non-opposition to participate in the research (at the latest on the day of inclusion and before any examination required by the research),
- Subjects affiliated to or benefiting from a social security scheme,
- Women of childbearing age benefiting from effective contraception.
Key exclusion criteria (4 listed)
- Person subject to a legal protection measure (safeguard of justice, guardianship or curators),
- Person deprived of liberty by judicial or administrative decision,
- Person who is unable to give his/her non-opposition,
- Pregnant or breastfeeding women.
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 |
|---|---|---|
| Ultrasound - Cardiac AcquisitionThe evaluation of the algorithm takes place on patients with an indication for echocardiography. This examination is done in a standard way with the potential specific explorations related to the indication of the examination. During the echocardiography, the operator records the search phase for the following reference views: * Parasternal window (Parasternal long axis, Parasternal minor axis) * Apical window (Apical 4 cavities, Apical 3 cavities, Apical 2 cavities * Sub costal window, Sub costal 4 cavities, Inferior vena cava) | Diagnostic test | Echocardiography group |
Primary outcome
Assessment of algorithm - cardiologist concordance
Time frame: Day 0
Measured by the percentage of images for which the labelling of the algorithm and the labelling of the cardiologist are identical. This percentage is calculated per examination.
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 21 Mar 2022.
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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.
Stéphane LAFITTE, MD PhD principal investigator
University Hospital, Bordeaux
Bertrand MOAL study chair
DESKi
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 Cardiac Disease, run by University Hospital, Bordeaux at 1 site in France.
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 5 inclusion and 4 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 21 Mar 2022. 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 France. 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?
Assessment of algorithm - cardiologist concordance (time frame: Day 0). 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 21 Mar 2022.