We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App




AI More Accurate than Sonographers in Assessing and Diagnosing Cardiac Function

By HospiMedica International staff writers
Posted on 03 Sep 2022
Print article
Image: A study showed that AI was more accurate in diagnosing cardiac function than sonographers (Photo courtesy of Unsplash)
Image: A study showed that AI was more accurate in diagnosing cardiac function than sonographers (Photo courtesy of Unsplash)

Accurate assessment of left ventricular ejection fraction (LVEF) is essential for diagnosing cardiovascular disease and making treatment decisions. However, human assessment is often based on a small number of cardiac cycles that can result in high inter-observer variability. Now, a new study has found that preliminary assessment by artificial intelligence (AI) is superior to initial sonographer assessment in patients undergoing echocardiographic evaluation of cardiac function.

EchoNet-Dynamic is a deep learning algorithm that was trained on echocardiogram videos to assess cardiac function and was previously shown to assess LVEF with a mean absolute error of 4.1-6.0%. The algorithm uses information across multiple cardiac cycles to minimize error and produce consistent results. Researchers at the Smidt Heart Institute at Cedars-Sinai (Los Angeles, CA, USA) tested whether AI or sonographer assessment of LVEF is more frequently adjusted by a reviewing cardiologist. The standard clinical workflow for determining LVEF by echocardiography is that a sonographer scans the patient; the sonographer provides an initial assessment of LVEF; and then a cardiologist reviews the assessment to provide a final report of LVEF. In this clinical trial, the sonographer’s scan was randomly allocated 1:1 to AI initial assessment or sonographer initial assessment, after which blinded cardiologists reviewed the assessment and provided a final report of LVEF.

The researchers compared how much cardiologists changed the initial assessment by AI to how much they changed the initial assessment by sonographer. The primary endpoint was the frequency of a greater than 5% change in LVEF between the initial assessment (AI or sonographer) and the final cardiologist report. The trial was designed to test for non-inferiority, with a secondary objective of testing for superiority. The study included 3,495 transthoracic echocardiograms performed on adults for any clinical indication. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference -10.4%, 95% confidence interval [CI] -13.2% to -7.7%, p<0.001 for non-inferiority, p<0.001 for superiority). The safety endpoint was the difference between the final cardiologist report and a historical cardiologist report. The mean absolute difference was 6.29% in the AI group and 7.23% in the sonographer group (difference -0.96%, 95% CI -1.34% to -0.54%, p<0.001 for superiority).

“There has been much excitement about the use of AI in medicine, but the technologies are rarely assessed in prospective clinical trials,” said Dr. David Ouyang of the Smidt Heart Institute at Cedars-Sinai. “We previously developed one of the first AI technologies to assess cardiac function (left ventricular ejection fraction; LVEF) in echocardiograms and in this blinded, randomized trial, we compared it head to head with sonographer tracings. This trial was powered to show non-inferiority of the AI compared to sonographer tracings, and so we were pleasantly surprised when the results actually showed superiority with respect to the pre-specified outcomes.”

“We learned a lot from running a randomized trial of an AI algorithm, which hasn’t been done before in cardiology,” added Dr. Ouyang. “First, we learned that this type of trial is highly feasible in the right setting, where the AI algorithm can be integrated into the usual clinical workflow in a blinded fashion. Second, we learned that blinding really can work well in this situation. We asked our cardiologist over-readers to guess if they thought the tracing they had just reviewed was performed by AI or by a sonographer, and it turns out that they couldn’t tell the difference – which both speaks to the strong performance of the AI algorithm as well as the seamless integration into clinical software. We believe these are all good signs for future trial research in the field.”

“We are excited by the implications of the trial. What this means for the future is that certain AI algorithms, if developed and integrated in the right way, could be very effective at not only improving the quality of echo reading output but also increasing efficiencies in time and effort spent by sonographers and cardiologists by simplifying otherwise tedious but important tasks,” concluded Dr. Ouyang. “Embedding AI into clinical workflows could potentially provide more precise and consistent evaluations, thereby enabling earlier detection of clinical deterioration or response to treatment.”

Related Links:
Cedars-Sinai 

Gold Member
12-Channel ECG
CM1200B
Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
Silver Member
Wireless Mobile ECG Recorder
NR-1207-3/NR-1207-E
New
Digital Radiography Generator
meX+20BT lite

Print article

Channels

Critical Care

view channel
Image: The permeable wearable electronics developed for long-term biosignal monitoring (Photo courtesy of CityUHK)

Super Permeable Wearable Electronics Enable Long-Term Biosignal Monitoring

Wearable electronics have become integral to enhancing health and fitness by offering continuous tracking of physiological signals over extended periods. This monitoring is crucial for understanding an... Read more

Surgical Techniques

view channel
Image: NTT and Olympus have begun the world\'s first joint demonstration experiment of a cloud endoscopy system (Photo courtesy of Olympus)

Cloud Endoscopy System Enables Real-Time Image Processing on the Cloud

Endoscopes, which are flexible tubes inserted into the body's natural openings for internal examination and biopsy collection, are becoming increasingly vital in medical diagnostics. Their minimal invasiveness... Read more

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The PATHFAST hs-cTnI-II high-sensitivity troponin assay has been developed for the PATHFAST Biomarker Analyzer (Photo courtesy of Polymedco)

POC Myocardial Infarction Test Delivers Results in 17 Minutes

Chest pain is the second leading cause of emergency department (ED) visits by adults in the United States, generating over 7 million visits annually. In the event of a suspected heart attack, physicians... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.