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Accelerating the Diagnosis of Cardiac Amyloidosis with AI

What is Cardiac Amyloidosis?

According to the University of Chicago, “Cardiac amyloidosis is a heart condition in which abnormal proteins build up in the heart muscle, making it stiff and impairing its ability to pump blood. Multiple life-prolonging drug treatments for this condition have recently become available, but without early diagnosis, physicians miss out on opportunities to extend patients’ survival and quality of life.”

“Unfortunately, cardiac amyloidosis can be challenging to diagnose, because it’s often difficult to distinguish from other heart issues without a burdensome amount of testing,” explained co-lead author Jeremy Slivnick, MD, a cardiologist at the University of Chicago Medicine.

The Promise of AI

AI is improving cardiac amyloidosis diagnosis by using algorithms to analyze medical imaging and data, which can lead to earlier and more accurate detection than traditional methods. These tools can automatically analyze echocardiograms, electrocardiograms (ECGs), and scintigraphy scans to identify the disease, which is crucial for timely treatment because new therapies are most effective in the early stages. Some AI models are FDA-cleared and are being implemented in clinical settings.

How AI is used in diagnosing cardiac amyloidosis

  • Echocardiography: AI tools can analyze echocardiogram videos to detect signs of cardiac amyloidosis with high accuracy, outperforming traditional scoring methods.
  • Electrocardiogram (ECG): AI-enhanced ECG models can identify patients at high risk for cardiac amyloidosis, helping to flag them for further evaluation and monitor disease progression during treatment.
  • Scintigraphy: AI systems can analyze nuclear medicine scintigraphy scans to automatically detect cardiac amyloidosis, performing as reliably as medical experts.
  • Infrared Spectroscopy: A new method uses infrared imaging to identify the molecular “fingerprints” of amyloidosis-causing proteins. AI is used to analyze these fingerprints, providing a faster, non-invasive alternative to biopsies.

Benefits of using AI

  • Earlier detection: AI can identify the disease at an earlier stage, which is critical for starting treatment when it is most effective.
  • Improved accuracy: AI tools have shown high sensitivity and specificity in detecting amyloidosis, sometimes outperforming current clinical practices.
  • Increased efficiency: Some AI systems can provide results within minutes, speeding up the diagnostic process and integrating seamlessly into clinical workflows.
  • Reduced costs and invasiveness: AI has the potential to reduce reliance on expensive or invasive procedures, like biopsies, especially when used to screen patients.
  • Broader access: The broad applicability of AI to common tests like echocardiograms and ECGs means it can be implemented in many clinical settings.

Limitations and future directions

  • Need for prospective validation: While promising, AI models still require more testing in clinical practice to understand their full capabilities and limitations.
  • Data and transparency: Challenges remain, including the need for more diverse data and improving the transparency of AI decision-making.
  • Widespread implementation: While some AI tools have received FDA clearance and are being implemented, widespread use in routine care is still a future goal.

Ultromics’ EchoGo Amyloidosis Tool

Researchers from Mayo Clinic and Ultromics, with investigators at the University of Chicago Medicine and collaborators around the world, validated and tested EchoGo Amyloidosis on a large and multiethnic patient population and compared its abilities to other diagnostic methods for cardiac amyloidosis.

“Their findings, published in the European Heart Journal, show that the AI model was highly accurate, with 85% sensitivity (correctly identifying those with the disease) and 93% specificity (correctly identifying those without the disease). Using a single echocardiography videoclip, the model was effective across all major types of cardiac amyloidosis and distinguished it from other conditions with similar characteristics.”

An Example of AI Integrated Into Workflow at the City of Hope

Hear from Dr. Faizi A. Jamal, Chief, Division of Cardiology and Director, Non-Invasive Cardiology Laboratory at the City of Hope National Medical Center who has been at the forefront of evaluating the clinical utility of AI based review of echocardiography in the diagnosis of cardiac amyloidosis. In this video he shares his experience with Ultromics’s EchoGo Amyloidosis, and how the City of Hope has incorporated this into their clinical workflow. He discusses the typical initial reasoning for ordering echocardiograms, which is to understand the severity of issues such as HFpEF or Aortic Stenosis, and how AI can take the analysis further to explore whether cardiac amyloidosis may be an underlying cause of the condition. Dr. Jamal discusses the multiple clinical challenges to diagnosing cardiac amyloidosis and details the numerous benefits experienced from the AI based review of echocardiograms. “EchoGo Amyloidosis is going to be revolutionary, based upon the volume of undiagnosed patients that are out there. It’s going to undoubtedly detect patients and impact morbidity and mortality for this disease.”

 

Sources

AI Tool Helps Improve Detection of Cardiac Amyloidosis, University of Chicago Medicine

AI Tool Improves Accuracy of Diagnosing Cardiac Amyloidosis on MRI, Cleveland Clinic

AI-enhanced echocardiography improves early detection of amyloid buildup in the heart, Mayo Clinic

Detecting cardiac amyloidosis early from a single AI-enhanced echocardiographic video clip, Mayo Clinic

How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges

Kamel MA, Abbas MT, Kanaan CN, Awad KA, Baba Ali N, Scalia IG, Farina JM, Pereyra M, Mahmoud AK, Steidley DE, Rosenthal JL, Ayoub C, Arsanjani R. How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges. J Cardiovasc Dev Dis. 2024 Apr 13;11(4):118. doi: 10.3390/jcdd11040118. PMID: 38667736; PMCID: PMC11050851.

Ultromics EchoGo Amyloidosis   The first FDA-cleared, AI-based screening tool designed to help identify patients at risk of cardiac amyloidosis from an echocardiogram.

Clinical Utility of AI Based Review of Echocardiography for Diagnosis of Cardiac Amyloidosis

Dr. Faizi A. Jamal, Chief, Division of Cardiology and Director, Non-Invasive Cardiology Laboratory at the City of Hope National Medical Center has been at the forefront of evaluating the clinical utility of AI based review of echocardiography in the diagnosis of cardiac amyloidosis. In this video he shares his experience with Ultromics’s EchoGo Amyloidosis, and how the City of Hope has incorporated this into their clinical workflow. He discusses the typical initial reasoning for ordering echocardiograms, which is to understand the severity of issues such as HFpEF or Aortic Stenosis, and how AI can take the analysis further to explore whether cardiac amyloidosis may be an underlying cause of the condition. Dr. Jamal discusses the multiple clinical challenges to diagnosing cardiac amyloidosis and details the numerous benefits experienced from the AI based review of echocardiograms. “EchoGo Amyloidosis is going to be revolutionary, based upon the volume of undiagnosed patients that are out there. It’s going to undoubtedly detect patients and impact morbidity and mortality for this disease.”

Click HERE, or on graphic below to view Dr. Jamal’s video.

Do You Need a Heart Biopsy to Diagnose Cardiac Amyloidosis?

Dr. Ahmad Masri, Director of the Cardiac Amyloidosis Program at Oregon Health & Science University (OHSU), reminds us about the traditional approach to diagnosis of cardiac amyloidosis. Unfortunately, this has not been enough. Thankfully, over the past decade that has all changed. He talks about OSHU’s approach to diagnosis today and what other data is used to arrive at a diagnosis of cardiac amyloidosis, offering four sample patient cases for insights.

Diagnosing AL and ATTR Cardiac Amyloidosis

Dr. Justin Grodin, a cardiologist and co-director of the UT Southwestern Multidisciplinary Amyloidosis Program, goes through the diagnostic process for AL and ATTR cardiac amyloidosis. He discusses key differences between AL and ATTR, and how typing of amyloidosis is paramount to consider in order to subsequently develop a treatment plan. He goes through a diagnostic algorithm to help clinicians arrive at an accurate diagnosis. Finally, he stresses the importance of genetic testing and counseling for ATTR to differentiate between wild-type and hereditary, as well as identifying the specific genetic variant.

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