Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases (EchoNet-Screening)

Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases

Despite rapidly advancing developments in targeted therapeutics and genetic sequencing, persistent limits in the accuracy and throughput of clinical phenotyping has led to a widening gap between the potential and the actual benefits realized by precision medicine.

Recent advances in machine learning and image processing techniques have shown that machine learning models can identify features unrecognized by human experts and more precisely/accurately assess common measurements made in clinical practice.

The investigators have developed an algorithm, termed EchoNet-LVH, to identify cardiac hypertrophy and identify patients who would benefit from additional screening for cardiac amyloidosis and will prospectively evaluate its accuracy in identifying patients whom would benefit from additional screening for cardiac amyloidosis.

Study Overview

• Status: Recruiting
• Conditions: Cardiac Amyloidosis
• Intervention / Treatment: Other: EchoNet-LVH screening for cardiac amyloidosis

Detailed Description

Despite rapidly advancing developments in targeted therapeutics and genetic sequencing, persistent limits in the accuracy and throughput of clinical phenotyping has led to a widening gap between the potential and the actual benefits realized by precision medicine. This conundrum is exemplified by current approaches to assessing morphologic alterations of the heart. If reliably identified, certain cardiac diseases (e.g. cardiac amyloidosis and hypertrophic cardiomyopathy) could avoid misdiagnosis and receive efficient treatment initiation with specific targeted therapies. The ability to reliably distinguish between cardiac disease types of similar morphology but different etiology would also enhance specificity for linking genetic risk variants and determining mechanisms

Recent advances in machine learning and image processing techniques have shown that machine learning models can identify features unrecognized by human experts and more precisely/accurately assess common measurements made in clinical practice. In echocardiography, this ability for precision measurement and detection is important in both disease screening as well as diagnosis of cardiovascular disease.

Echocardiography is routinely and frequently used for diagnosis and prognostication in routine clinical care, however there is often subjectivity in interpretation and heterogeneity in application. Human attention is fatigable and has heterogenous interpretation between providers. AI guided disease screening workflows have been proposed for rare diseases such as cardiac amyloidosis and other diseases with relatively low prevalence but significant human impact with targeted therapies when detected early. This is an area particularly suitable for AI as there are multiple mimics where diseases like hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, and other phenotypes might visually be similar but can be distinguished by AI algorithms. The investigators have developed an algorithm, termed EchoNet-LVH, to identify cardiac hypertrophy and identify patients who would benefit from additional screening for cardiac amyloidosis, hypertrophic cardiomyopathy and other diseases. E

• Study Type: Observational
• Enrollment (Anticipated): 300

Contacts and Locations

Study Locations

• United States
  • California
    • Los Angeles, California, United States, 90048
      • Recruiting
      • Cedars-Sinai Medical Centre (Los Angeles)
      • Contact: David Ouyang, MD; Phone Number: 832-495-1605; Email: david.ouyang@cshs.org

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Patients who have a high suspicion for cardiac amyloidosis by AI algorithm

Description

Inclusion Criteria:

• Patients who have a high suspicion for cardiac amyloidosis by AI algorithm

Exclusion Criteria:

• Patients who decline to be seen at specialty clinic
• Patients who have passed away

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Artificial Intelligence Screening for Cardiac Amyloidosis; An artificial intelligence algorithm will produce a probability of cardiac amyloidosis that will trigger referral to specialty clinic for further evaluation.	Other: EchoNet-LVH screening for cardiac amyloidosis; An AI algorithm identifies LVH, low voltage, and high suspicion for cardiac amyloidosis. The intervention is the suspicion score. Patients with high suspicion score will be referred to specialty clinic for standard of care evaluation, screening, and treatment as determined by physicians.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Number of New Diagnoses of Cardiac Amyloidosis Found	From chart review, identification of patients who have a downstream diagnosis of cardiac amyloidosis	6 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Number of New Diagnoses of TTR Amyloidosis Found	From chart review, identification of patients who have a downstream diagnosis of TTR amyloidosis	6 months
Number of New Diagnoses of AL Amyloidosis Found	From chart review, identification of patients who have a downstream diagnosis of AL amyloidosis	6 months

Collaborators and Investigators

• Sponsor: Cedars-Sinai Medical Center

Publications and helpful links

Helpful Links

• Related Info

Study record dates

Study Major Dates

• Study Start (ACTUAL): November 18, 2021
• Primary Completion (ANTICIPATED): January 1, 2025
• Study Completion (ANTICIPATED): June 1, 2025

Study Registration Dates

• First Submitted: November 18, 2021
• First Submitted That Met QC Criteria: November 30, 2021
• First Posted (ACTUAL): December 1, 2021

Study Record Updates

• Last Update Posted (ACTUAL): February 8, 2023
• Last Update Submitted That Met QC Criteria: February 6, 2023
• Last Verified: February 1, 2023

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Echocardiogram
• Additional Relevant MeSH Terms: Pathologic Processes; Metabolic Diseases; Disease Attributes; Proteostasis Deficiencies; Amyloidosis; Rare Diseases
• Other Study ID Numbers: STUDY00001720

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes