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Related Concept Videos

Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...

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Related Experiment Video

Updated: May 10, 2026

Performing and Processing FNA of Anterior Fat Pad for Amyloid
09:41

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International Validation of Echocardiographic AI Amyloid Detection Algorithm.

Grant Duffy1, Evan Oikonomou2, Jonathan Hourmozdi3

  • 1Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Medrxiv : the Preprint Server for Health Sciences
|January 7, 2025
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Summary

A new computer vision algorithm, EchoNet-LVH, shows high accuracy in detecting cardiac amyloidosis (CA) from echocardiograms. This tool can aid in earlier and more precise diagnosis of this often-missed condition.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Cardiac amyloidosis (CA) diagnosis is frequently delayed due to its resemblance to other conditions causing left ventricular hypertrophy.
  • Conventional echocardiographic measures like global longitudinal strain (GLS) offer limited specificity for CA detection.

Purpose of the Study:

  • To evaluate the diagnostic performance of EchoNet-LVH, a computer vision algorithm, for detecting cardiac amyloidosis.
  • To assess the algorithm's ability to differentiate CA from other causes of increased left ventricular wall thickness using echocardiogram videos.

Main Methods:

  • A multi-site retrospective case-control study was conducted using echocardiogram videos.
  • EchoNet-LVH, a deep learning algorithm, analyzed parasternal long axis and apical-4-chamber views to detect CA.
  • Performance was measured using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and predictive values.

Main Results:

  • EchoNet-LVH achieved an AUC of 0.896, indicating strong discrimination.
  • The algorithm demonstrated high specificity (0.988) and positive predictive value (0.968), crucial for diagnosing rare diseases like CA.
  • Performance remained consistent across various sites, demographics, and equipment, suggesting broad applicability.

Conclusions:

  • EchoNet-LVH shows significant potential to assist in the earlier and more accurate diagnosis of cardiac amyloidosis.
  • The algorithm's high specificity is vital for maximizing positive predictive value in the context of CA's rarity.
  • Future research will focus on whether early diagnosis facilitated by EchoNet-LVH leads to improved treatment initiation and patient outcomes.