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Research Study Shows Cardiac Ultrasound AI May Be Superior to Anatomic Pathologists at Predicting COVID-19 Death Risk

WASE-COVID Study also found that use of artificial intelligence technology minimized variability among echocardiogram scan results

Many physicians—including anatomic pathologists—are watching the development of artificial intelligence (AI)-powered diagnostic tools that are intended to analyze images and analyze the data with accuracy comparable to trained doctors. Now comes news of a recent study that demonstrated the ability of an AI tool to analyze echocardiograph images and deliver analyses equal to or better than trained physicians.

Conducted by researchers from the World Alliance Societies of Echocardiography and presented at the latest annual sessions of the American College of Cardiology (ACC), the WASE-COVID Study involved assessing the ability of the AI platform to analyze digital echocardiograph images with the goal of predicting mortality in patients with severe cases of COVID-19.

The findings could have widespread implications for the adoption of AI solutions that assist doctors in analyzing the full range of digital images used by radiologists, pathologists, and other specialist physicians. The researchers published their study in the Journal of the American Society of Echocardiography (JASE), titled, “Echocardiographic Correlates of In-Hospital Death in Patients with Acute COVID-19 Infection: The World Alliance Societies of Echocardiography (WASE-COVID) Study.”

To complete their research, the WASE-COVID Study scientists examined 870 patients with acute COVID-19 infection from 13 medical centers in nine countries throughout Asia, Europe, United States, and Latin America.

Human versus Artificial Intelligence Analysis

Echocardiograms were analyzed with automated, machine learning-derived algorithms to calculate various data points and identify echocardiographic parameters that would be prognostic of clinical outcomes in hospitalized patients. The results were then compared to human analysis.

All patients in the study had previously tested positive for COVID-19 infection using a polymerase chain reaction (PCR) or rapid antigen test (RAT) and received a clinically-indicated echocardiogram upon admission. For those patients ultimately discharged from the hospital, a follow-up echocardiogram was performed after three months.

“What we learned was that the manual tracings were not able to predict mortality,” Federico Asch, MD, FACC, FASE, Director of the Echocardiography Core Lab at MedStar Health Research Institute in Washington, DC, told US Cardiology Review in a video interview describing the WASE-COVID Study findings.

Asch is also Associate Professor of Medicine (Cardiology) at Georgetown University. He added, “But on the same echoes, if the analysis was done by machine—Ultromics EchoGo Core, a software that is commercially available—when we used the measurements obtained through this platform, we were able to predict in-hospital and out-of-hospital mortality both with ejection fraction and left ventricular longitudinal strain.”

Federico Asch, MD

“When compared to the manual reads, the AI algorithms had a much higher predictive value for mortality,” Federico Asch, MD (above), told US Cardiology Review. “Indeed, they were predictive where the manual ones were not.” These findings may have implications in the development and adoption of artificial intelligence driven clinical laboratory diagnostics and for predicting risk of COVID-19 deaths in hospitalized heart patients. Click here to review the entire video interview. (Photo copyright: US Cardiology Review.)

Nearly half of the 870 hospitalized patients were admitted to intensive care units, 27% were placed on ventilators, 188 patients died in the hospital, and 50 additional patients died within three to six months after being released from the hospital.

According to an Ultromics news release:

  • 10 of 13 medical centers performed limited cardiac exams as their primary COVID in-patient practice and three out of the 13 centers performed comprehensive exams.
  • In-hospital mortality rates ranged from 11% in Asia, 19% in Europe, 26% in the US, to 27% in Latin America.
  • Left ventricular longitudinal strain (LVLS), right ventricle free wall strain (RVFWS), as well as a patient’s age, lactic dehydrogenase levels and history of lung disease, were independently associated with mortality. Left ventricle ejection fraction (LVEF) was not.
  • Fully automated quantification of LVEF and LVLS using AI minimized variability.
  • AI-based left ventricular analyses, but not manual, were significant predictors of in-hospital and follow-up mortality.

The WASE-COVID Study also revealed the varying international use of cardiac ultrasound (echocardiography) on COVID-19 patients.

“By using machines, we reduce variability. By reducing variability, we have a better capacity to compare our results with other outcomes, whether that outcome in this case is mortality or it could be changes over time,” Asch stated in the US Cardiology Review video. “What this really means is that we may be able to show associations and comparisons by using AI that we cannot do with manual [readings] because manual has more variation and is less reliable.”

He said the next steps will be to see if the findings hold true when AI is used in other populations of cardiac patients.

COVID-19 Pandemic Increased Need for Swift Analyses

An earlier WASE Study in 2016 set out to answer whether normal left ventricular heart chamber quantifications vary across countries, geographical regions, and cultures. However, the data produced by that study took years to review. Asch said the COVID-19 pandemic created a need for such analysis to be done more quickly.

“When the pandemic began, we knew that the clinical urgency to learn as much as possible about the cardiovascular connection to COVID-19 was incredibly high, and that we had to find a better way of securely and consistently reviewing all of this information in a timely manner,” he said in the Ultromics new release.

Coronary artery disease (CAD) is the most common form of heart disease and affects more than 16.5 million people over the age of 20. By 2035, the economic burden of CAD will reach an estimated $749 billion in the US alone, according to the Ultromics website.

“COVID-19 has placed an even greater pressure on cardiac care and looks likely to have lasting implications in terms of its impact on the heart,” said Ross Upton, PhD, Founder and CEO of Oxford, UK-based Ultromics, in a news release announcing the US Food and Drug Administration’s 510(k) clearance for the EchoGo Pro, which supports clinicians’ diagnosing of CAD. “The healthcare industry needs to quickly pivot towards AI-powered automation to reduce the time to diagnosis and improve patient care.”

Use of AI to analyze digital pathology images is expected to be a fast-growing element in the anatomic pathology profession, particularly in the diagnosis of cancer. As Dark Daily outlined in this free white Paper, “Anatomic Pathology at the Tipping Point? The Economic Case for Adopting Digital Technology and AI Applications Now,” anatomic pathology laboratories can expect adoption of AI and digital technology to gain in popularity among pathologists in coming years.

—Andrea Downing Peck

Related Information:

Echocardiographic Correlates of In-Hospital Death in Patients with Acute COVID-19 Infection: The World Alliance Societies of Echocardiography (WASE-COVID) Study

ACC 2021: Findings from the WASE COVID Study

Artificial Intelligence Predictors of Death from COVID-19

Left Ventricular Diastolic Function in Healthy Adult Individuals: Results of the World Alliance Societies of Echocardiography Normal Values Study

Echocardiographic Correlates of In-Hospital Death in Patients with Acute COVID-19 Infection: The World Alliance Societies of Echocardiography (WASE-COVID) Study

Human vs AI-Based Echocardiography Analysis as Predictor of Mortality in Acute COVID-19 Patients: WASE-COVID Study

Ultromics Receives FDA Clearance for EchoGo Pro; a First-of-Kind Solution to Diagnose CAD

Anatomic Pathology at the Tipping Point: The Economic Case for Adopting Digital Technology and AI Applications Now