Artificial Intelligence in Echocardiography

• ClinicalTrials.gov Identifier: NCT03936413
• Recruitment Status: Withdrawn
• First Posted: May 3, 2019
• Last Update Posted: December 17, 2021
• Sponsor: New York Presbyterian Hospital
• Collaborator: Caption Health, Inc.
• Information provided by (Responsible Party): Kerry Esquitin, New York Presbyterian Hospital

Study Description

Brief Summary:

The goal of this study is to determine whether the Bay Labs artificial intelligence (AI) system can be used by minimally trained operators to obtain diagnostic quality echocardiographic images.

Condition or disease	Intervention/treatment	Phase
Cardiovascular Diseases	Device: Bay Labs EchoGPS Echcoardiogram; Device: Native Terason Echocardiogram	Not Applicable

Detailed Description:

Echocardiography is a common and essential tool in the diagnosis of cardiovascular disease. Using ultrasound, this technique allows for non-invasive assessment of cardiac function, including systolic function, diastolic function, heart chamber quantification, and diagnosis and quantification of valvular abnormalities. Usage of echocardiography has increased each year; over 7 million echocardiograms were performed in 2011 in the Medicare Population alone. Cardiovascular disease remains the leading cause of death worldwide, and allowing for widespread usage of echocardiography could result in earlier diagnosis and treatment of cardiovascular disease and potential reduction in healthcare disparities.

The procedure of an echocardiogram first requires image acquisition which is then followed by image analysis and interpretation. Image acquisition is traditionally performed by cardiac sonographers (technicians) or physicians. Image review and interpretation is performed by specialist physicians, typically cardiologists or radiologists. Each step in the process historically requires a high level of training and specialized equipment which limits its use in under-resourced areas. However, given the high level of skill required to operate this equipment there remains a need for additional technologies to aid in the acquisition and interpretation of imaging. As ultrasound technology has improved, however, costs and size of equipment have decreased and use of bedside ultrasound to guide clinical decision making has become increasingly common. This bedside ultrasound is focused on specific questions, such as diagnosis of a pleural or pericardial effusion, and can be readily taught to non-experts.

One of the potential tools to overcome these limitations is artificial intelligence (AI). AI is the use of computer programs to mimic the cognitive function of the human mind in order to learn and solve problems. Echocardiology is a particularly ripe field that may benefit from the use of artificial intelligence. Due to anatomical differences and dynamic clinical situations, the process of image acquisition and analysis can vary widely between patients. Although simple computer algorithms fail to integrate these differences, artificial intelligence may allow for machine-assisted image acquisition and analysis. In the last several years, there have been numerous studies of computer-assisted analysis of echocardiography. The use of this technology may speed the acquisition of echocardiographic images, reduce the amount of training needed to acquire and analyze images, improve diagnostic quality, and reduce interobserver variability in the analysis of echocardiographic images.

By integrating artificial intelligence-assisted image acquisition and analysis with ultrasound technology, it may be possible for minimally trained operators in underserved areas to use echocardiography to accurately diagnose cardiovascular disease.

This study will be supervised by the Echocardiography Laboratory and the Internal Medicine Residency Program in the Department of Medicine at the NewYork Presbyterian Columbia University Medical Center. The study will take place on the medical resident inpatient cardiology ward services which are primarily housed in the Milstein Hospital 5 Garden South ward. The primary subjects of the study are the medical residents in the Department of Medicine who are rotating through the inpatient cardiology ward services.

During this study, the medical residents will undergo a training session introducing them to the basic concepts of echocardiography and the usage of either the Bay Labs echocardiology AI ultrasound system or a standard echocardiography system (a native Terason echocardiography machine). In this protocol, the echocardiographic images they obtain using these tools will be called a "study echocardiogram." This is in contrast to a "formal echocardiogram" that is performed by cardiac sonographers, cardiology fellows, or cardiology attendings and analyzed by cardiology attendings who specialize in echocardiography. The medical residents will use either the Bay Labs system or the native Terason system to perform echocardiograms on patients admitted to the cardiology ward teams who either have undergone or are planned to undergo a formal echocardiogram within 1 day of the study echocardiogram. The explicit goals of this project are (1) to determine if it is possible for the medical residents as novice users to acquire and interpret echocardiographic images and (2) whether the use of the Bay Labs system aids in education over the standard system. The Bay Labs echocardiogram that is performed as part of this study is not FDA approved to aid in clinical decision making, and as such the results of the study echocardiograms will not be used to change clinical management.

There are a total of 4 cardiology resident teams on the inpatient cardiology wards, and the teams are designated using a letter system: A, B, C, and D. Each of these teams is comprised of a first-year medical resident and a third-year medical resident. The A and C teams form one team dyad and the B and D teams form a second team dyad. The teams are covered at night by a separate team of medical residents. Each dyad is primarily supervised by a pair of cardiology attendings who oversee the care of the general cardiac patients. In addition to the service cardiology attendings, the teams may manage patients under the care of heart failure cardiologists or other private cardiologists. Each team can carry a total of 10 patients. The admitting structure of the teams allows for 7 new patients to be admitted to the cardiology ward services per 24 hour period. During the day, 3 patients can be admitted to the long-call team and 1 patient to the short-call team. At night, 3 patients can be admitted to the overnight (long-call) team.

Prior to the initiation of the study, a schedule will be created where each 4 week cardiology inpatient ward rotation block will be assigned to use Bay Labs technology or the native Terason system. At the start of their cardiology inpatient ward rotation, the medical residents will be approached to consent to take place in the study. The medical residents who consent to the study will undergo a pre-test asking them about their comfort with the performance of and interpretation of echocardiography. They will then undergo a training session introducing them to the basics of echocardiography, the study protocol, and the operation of either the Bay Labs echocardiography system or the native Terason echocardiography system. After receiving this training, they will be able to start performing echocardiograms in the study.

Each morning, each medical resident will able to designate between 1-3 patients on their service to undergo an echocardiogram. The inclusion and exclusion criteria are listed in the protocol. The patients will be approached by the an investigator or research assistant to consent them for the study. Participants will then undergo an echocardiogram performed by the medical residents and images will be stored for later analysis. After each echocardiogram, the medical residents will fill out a short form documenting the echocardiogram. Questions on this form will include demographics, clinical features, technical aspects about the test (such as whether specific views could be obtained), and ask basic questions about operator estimated cardiac function that was observed during the test.

At the end of the rotation, the participating residents will take a post-test on their comfort with the performance of and interpretation of echocardiography. They will be offered the opportunity to provide feedback on the study. The medical residents will be offered feedback on their echocardiograms during and after the study period to aid in learning.

After the end of the study period, the data will be analyzed. Results from the pre-test and post-test will be analyzed to determine if the medical residents had improvement in their knowledge of echocardiography during the rotation. The echocardiograms will be analyzed and compared with the formal echocardiograms by the study investigators and attending echocardiographers to determine if the designated image views could be obtained, the time needed to obtain the images, and the quality of the images in comparison to the formal echocardiogram. The left ventricular systolic function visually observed by the medical residents during the study echocardiogram, the autoEF calculated by the Bay Labs software in those patients in those groups, and the left ventricular ejection fraction from the formal echocardiogram will be compared.

This study is of no more than minimal risk to the patients enrolled. The test itself (a transthoracic echocardiogram) is noninvasive and has no radiation exposure. In addition, the medical teams will be prohibited from making clinical decisions based on the Bay Labs echocardiogram which will instead be based on the formal echocardiogram. At the end of the study, the data will be permanently de-identified.

Study Design

• Study Type: Interventional (Clinical Trial)
• Actual Enrollment: 0 participants
• Allocation: Non-Randomized
• Intervention Model: Sequential Assignment
• Intervention Model Description: See protocol
• Masking: None (Open Label)
• Primary Purpose: Diagnostic
• Official Title: Artificial Intelligence in Echocardiography: a Pilot Study of Bay Labs Technology in Image Acquisition, Education, and Analysis
• Estimated Study Start Date: January 13, 2020
• Actual Primary Completion Date: January 13, 2020
• Actual Study Completion Date: January 13, 2020

Arms and Interventions

Arm	Intervention/treatment
Experimental: Bay Labs EchoGPS group; In this arm, medical residents will use the Bay Labs EchoGPS system to perform an echocardiogram.	Device: Bay Labs EchoGPS Echcoardiogram; An echocardiogram will be performed in this arm using the Bay Labs EchoGPS. The Bay Labs EchoGPS system is an ultrasound system which uses the techniques of computer vision to analyze echocardiography images in real time. It then provides feedback to the user to optimize the images, and once they meet a specific level of quality it automatically records the images.
Active Comparator: Native Terason group; In this arm, medical residents will use the native Terason machine to perform an echocardiogram.	Device: Native Terason Echocardiogram; An echocardiogram will be performed in this arm using a native Terason echocardiography system. This system will not have any artificial intelligence assistance in image optimization or selection.

Outcome Measures

Primary Outcome Measures :

1. Echocardiogram image acquisition quality [ Time Frame: immediately after the intervention ]

   Echocardiogram image acquisition quality. View quality will be characterized by the following system: adequate (American College of Emergency Physicians (ACEP) score 5), mildly limited (ACEP score 4), moderately limited (ACEP score 3), and severely limited (ACEP score 1 and 2). This will graded for each echocardiogram view as below and for the study as a whole.

   • Parasternal long axis
   • Parasternal short axis - aortic valve level
   • Parasternal short axis - mid ventricle
   • Apical 4 chamber
   • Apical 2 chamber
   • Subcostal - 4 chamber
   • Subcostal - inferior vena cava
2. Educational outcome [ Time Frame: 1 month ]

   The medical resident's comfort with echocardiography will be established using the following questionnaire. The answers to each question are (1) very uncomfortable, (2) somewhat uncomfortable, (3) somewhat comfortable, and (4) very comfortable. These answers will be reported separately and in aggregate.

   How comfortable do you feel in your knowledge of the indications for ordering an echocardiogram? How comfortable do you feel in understanding echocardiographic reports as it applies to your patients? How comfortable do you feel in obtaining routine echocardiographic views using an ultrasound machine? How comfortable do you feel in interpreting echocardiographic images after the images have already been obtained? In an emergency situation, how comfortable would you feel in performing an echocardiogram using an ultrasound machine and interpreting the images to rule out serious cardiac pathology such as severe left ventricular systolic dysfunction or a large pericardial effusion?

Eligibility Criteria

• Ages Eligible for Study: 18 Years and older (Adult, Older Adult)
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

Criteria

Inclusion Criteria:

• Patients must be admitted to the resident cardiology ward service under a cardiology attending (general, heart failure, or private)
• The patient must have either have undergone or be planned to undergo a formal echocardiogram within 1 day of the study echocardiogram
• The patient must consent to the study
• The patient's inpatient attending physician must give permission for the patient to be approached for consent

Exclusion Criteria:

• Patient refusal
• No recent or planned echocardiogram within 1 day of the study echocardiogram
• Clinical need for an emergent echocardiogram that will immediately impact clinical decision making that should instead trigger obtaining a formal echocardiogram (for example, concern for cardiac tamponade or acute myocardial infarction).

Contacts and Locations

Sponsors and Collaborators

New York Presbyterian Hospital

Caption Health, Inc.

Investigators

• Principal Investigator: Kerry A Esquitin, MD; Columbia University

More Information

Publications:

Virnig BA, Shippee ND, O'Donnell B, Zeglin J, Parashuram S. Trends in the use of echocardiography, 2007 to 2011: Data Points #20. 2014 May 13. Data Points Publication Series [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2011-. Available from http://www.ncbi.nlm.nih.gov/books/NBK208663/

Gandhi S, Mosleh W, Shen J, Chow CM. Automation, machine learning, and artificial intelligence in echocardiography: A brave new world. Echocardiography. 2018 Sep;35(9):1402-1418. doi: 10.1111/echo.14086. Epub 2018 Jul 5. Review.

• Responsible Party: Kerry Esquitin, Assistant Professor of Medicine, New York Presbyterian Hospital
• ClinicalTrials.gov Identifier: NCT03936413
• Other Study ID Numbers: NYPresbyterianH
• First Posted: May 3, 2019
• Last Update Posted: December 17, 2021
• Last Verified: December 2021

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: No

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: Yes
• Product Manufactured in and Exported from the U.S.: No

Keywords provided by Kerry Esquitin, New York Presbyterian Hospital:

imaging; echocardiography; artificial intelligence

Additional relevant MeSH terms:

Cardiovascular Diseases