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
Thermo Fisher Scientific - Direct Effect Media

Download Mobile App




Events

ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.

New AI Tool Can Identify and Distinguish Between Difficult-to-Diagnose Life-Threatening Heart Conditions

By HospiMedica International staff writers
Posted on 24 Feb 2022
Print article
Image: AI Algorithm Spots Difficult-to-Diagnose Cardiac Conditions (Photo courtesy of Unsplash)
Image: AI Algorithm Spots Difficult-to-Diagnose Cardiac Conditions (Photo courtesy of Unsplash)

For the first time, a team of physician-scientists has developed an algorithm that can spot difficult-to-diagnose cardiac conditions.

Physician-scientists in the Smidt Heart Institute at Cedars-Sinai (Los Angeles, CA, USA) have created an artificial intelligence (AI) tool that can effectively identify and distinguish between two life-threatening heart conditions that are often easy to miss: hypertrophic cardiomyopathy and cardiac amyloidosis. The two-step, novel algorithm was used on over 34,000 cardiac ultrasound videos. When applied to these clinical images, the algorithm identified specific features - related to the thickness of heart walls and the size of heart chambers - to efficiently flag certain patients as suspicious for having the potentially unrecognized cardiac diseases.

Without comprehensive testing, cardiologists find it challenging to distinguish between similar appearing diseases and changes in heart shape and size that can sometimes be thought of as a part of normal aging. This algorithm accurately distinguishes not only abnormal from normal, but also between which underlying potentially life-threatening cardiac conditions may be present - with warning signals that are now detectable well before the disease clinically progresses to the point where it can impact health outcomes. Getting an earlier diagnosis enables patients to begin effective treatments sooner, prevent adverse clinical events, and improve their quality of life.

Cardiac amyloidosis, often called “stiff heart syndrome,” is a disorder caused by deposits of an abnormal protein (amyloid) in the heart tissue. As amyloid builds up, it takes the place of healthy heart muscle, making it difficult for the heart to work properly. Cardiac amyloidosis often goes undetected because patients might not have any symptoms, or they might experience symptoms only sporadically. The disease tends to affect older, Black men or patients with cancer or diseases that cause inflammation. Many patients belong to underserved communities, making the study results an important tool in improving healthcare equity.

Hypertrophic cardiomyopathy is a disease that causes the heart muscle to thicken and stiffen. As a result, it's less able to relax and fill with blood, resulting in damage to heart valves, fluid buildup in the lungs, and abnormal heart rhythms. Although separate and distinct conditions, cardiac amyloidosis and hypertrophic cardiomyopathy often look very similar to each other on an echocardiogram, the most commonly used cardiac imaging diagnostic. Importantly, in the very early stages of disease, each of these cardiac conditions can also mimic the appearance of a non-diseased heart that has progressively changed in size and shape with aging.

The new AI technology can be used to identify patients from very early on in their disease course. That’s because clinicians know that earlier is always better for getting the most benefit from therapies that are available today and that can be very effective for preventing the worst possible outcomes, such as heart failure, hospitalizations, and sudden death. Researchers plan to soon launch clinical trials for patients flagged by the AI algorithm for suspected cardiac amyloidosis. A clinical trial for patients flagged by the algorithm for suspected hypertrophic cardiomyopathy has just started at Cedars-Sinai.

“Our AI algorithm can pinpoint disease patterns that can’t be seen by the naked eye, and then use these patterns to predict the right diagnosis,” said David Ouyang, MD, a cardiologist in the Smidt Heart Institute and senior author of the study. “The algorithm identified high-risk patients with more accuracy than the well-trained eye of a clinical expert. This is because the algorithm picks up subtle cues on ultrasound videos that distinguish between heart conditions that can often look very similar to more benign conditions, as well as to each other, on initial review.”

Related Links:
Cedars-Sinai 


Print article

Channels

Critical Care

view channel
Image: Triage Cardiac Panel is a rapid, POC fluorescence immunoassay used with Triage MeterPro (Photo courtesy of Quidel)

Quidel Triage Cardiac Panel Facilitates Rapid POC Diagnosis of Chest Pain Patients in ED

Chest and abdominal pain are the most common reasons that persons aged 15 years and over visit the emergency department (ED). Because both emergency and non-emergency care are provided, symptoms vary widely... Read more

Surgical Techniques

view channel
Image: A minimally invasive diagnostic biomarker strategy has proven successful in detecting early esophageal cancer (Photo courtesy of Pexels)

Minimally Invasive Device Combined With Molecular Biomarkers Detects Early Esophageal Cancer

Esophageal cancer remains the sixth-most common cause of cancer death worldwide, claiming the lives of more than half a million people annually. Esophageal squamous cell carcinoma (ESCC) comprises 80%... Read more

Health IT

view channel
Image: AI can reveal a patient`s heart health (Photo courtesy of Mayo Clinic)

AI Trained for Specific Vocal Biomarkers Could Accurately Predict Coronary Artery Disease

Earlier studies have examined the use of voice analysis for identifying voice markers associated with coronary artery disease (CAD) and heart failure. Other research groups have explored the use of similar... Read more
Copyright © 2000-2022 Globetech Media. All rights reserved.