Artificial Intelligence in Echocardiography: a Pilot Study of Bay Labs Technology in Image Acquisition, Education, and Analysis

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.