NCT05139797

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

Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 300 (estimated)
• Sponsor: Cedars-Sinai Medical Center · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

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.

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

Conditions

• Cardiac Amyloidosis

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2021-11-18
• Primary completion: 2026-01-01
• Completion: 2027-06-01
• First posted: 2021-12-01
• Last updated: 2025-06-27

Locations

1 site across 1 country: United States

Regulatory

• FDA-regulated device study