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

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.

Eligibility

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

Conditions2

Locations1 site

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