Validation of an Artificial Intelligence Algorithm Identifying Echocardiographic Reference Views. Ultrasound - Cardiac Acquisition Guide

NCT ID: NCT05265585

Last Updated: 2022-03-21

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 75 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-06-19
• Study Completion Date: 2020-08-11

Brief 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 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.

Conditions

Cardiac Disease

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: PROSPECTIVE

Study Groups

Echocardiography group

Ultrasound - Cardiac Acquisition

Intervention Type: DIAGNOSTIC_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)

Interventions

Ultrasound - Cardiac Acquisition

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)

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• 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.

Exclusion Criteria

• 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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University Hospital, Bordeaux

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Stéphane LAFITTE, MD PhD

Role: PRINCIPAL_INVESTIGATOR

University Hospital, Bordeaux

Bertrand MOAL

Role: STUDY_CHAIR

DESKi

Locations

Bordeaux University Hospital

Pessac, , France

Countries

France

Other Identifiers

CHUBX 2020/02

Identifier Type: -

Identifier Source: org_study_id