In her research at Mayo, Pellikka has been involved with the development of AI that looks at the ultrasound images to directly detect disease. The algorithms can pick out radiomic signatures of disease in the image that may not be evident to the human eye. "I think the potential there is enormous," Pellikka said.

She collaborated with the AI vendor Ultromics on its EchoGo algorithms to detect heart failure patients with preserved ejection fraction (HFpEF) by looking at a single apical four-chamber view. HFpEF can be difficult to detect, and this is the first AI cleared to detect it, filling an unmet need. The technology received the FDA's breakthrough device designation and was then cleared by the FDA at the end of 2022.

Based on the AI findings, these patients can be started on medications to help treat their condition much earlier than if they had to wait to schedule a full echo exam, she said. 

Helping improve echo acquisition with novice sonographers

Over the past few years there has been a big increase in the use of point-of-care ultrasound (POCUS) systems in a variety of settings, including clinics, physician offices, emergency departments and ICUs. These echos are being performed by much less experienced sonographers than those in hospital echo labs. This has resulted in a rising number of suboptimal POCUS cardiac exams.

A couple AI vendors have developed algorithms to show POCUS users how to move their probe into the correct position and walk them through how to acquire each of the standard echo views. The AI also tells the operator when they are in the correct position and judges the quality of the images they are acquiring. Many echo experts say this can significantly improve exam diagnostic quality, leading to fewer repeat exams, faster and better diagnosis of patients. These better images also allow for accurate measurements and quantification, which may n to be possible on a suboptimal exam.

"This will extend the reach of cardiovascular ultrasound to places and times when there isn't an experienced cardiac sonographer available to do the imaging. I think the potential for this is extremely exciting," Pellikka explained. 

AI can automate cardiac strain and ejection fraction assessments

AI is also being used to speed up patient assessments by automating echo strain, ejection fractions and other measurements. Pellikka said several vendors have now developed FDA cleared algorithms in these areas. This automation can eliminate the need for a sonographer to perform manual contouring of the ventricles, or manually using calipers to take various measurements. This results in a much faster exam or post-processing of the exam, enabling more patient throughput. 

Importantly, Pellikka and other echo experts say this type of AI automation also reduced the variability in measurements between different sonographers. AI automatically picks the same landmark locations to perform measurements, helping deliver more consistency. This is especially important when monitoring patients over time.

"I think this is just the tip of the iceberg and I think we will be automating many other measurements as well, such as automatic assessment of valvular heart disease. All of this is going to increase the standardization of echocardiography and make it so the communication between one center doing an echo and another is more standardized than it is today," Pellikka said. 

Any AI measurements can be changed by an experienced sonographer if they disagree with where the AI has placed a contour or caliper. 

Pellikka said the concept of AI automated measurements is important because even if a complete clinical echocardiogram exam is suggested based on the POCUS findings, the AI automation gives clinicians a jump start on the care pathway for patients before the AI findings are confirmed on a more detailed exam.

"You can make a judgment earlier without having to wait for the clinical echocardiogram to be performed, which will allow more expeditious care of the patient. This is very important now, because we have a sonographer shortage across the United States, and sometimes it takes quite a while to get an echocardiogram," she said. 

This automation will help increase the speed or workflows and allow more patients to be imaged and assessed by making sonographers more efficient. 

"Another benefit, beyond increasing standardization, it will help us sort out who needs the full echocardiogram and who does not," she said. 

Future AI applications in cardiac ultrasound

Pellikka also noted that AI is being developed now that can detect and quantify amyloid heart failure with preserved ejection fraction, hypertrophic cardiomyopathy, valvular heart disease, pulmonary hypertension and other conditions. This should help provide clinicians with quick, accurate assessments of these diseases so that the right next steps can be taken.