Artificial Intelligence Detects Cardiac Amyloidosis Before Clinical Diagnosis
Yuan, V.; Kalagara, S.; IEKI, H.; Bhatt, A. S.; Ambrosy, A. P.; Elezaby, A.; Stern, L. K.; Cheng, P.; Ouyang, D.
Show abstract
The approval of novel disease-modifying treatments for cardiac amyloidosis (CA) offers an avenue to stabilize disease progression. Timely diagnosis of CA is imperative so that patients can be connected to treatment earlier in their course. Echocardiography is a widely available, non-invasive modality to screen for CA. However, diagnosis of CA is often delayed due to phenotypic mimickers. Artificial intelligence (AI) applied to echocardiography may facilitate early detection and connect patients to more definitive diagnostic testing. In this case report, we analyzed the frequency of echocardiography prior to clinical diagnosis of amyloidosis and deployed an externally validated AI model in 349 patients with documented amyloidosis at Cedars-Sinai Medical Center. On average, AI detection was positive in patients with documented CA 218 days prior to clinical diagnosis and 209 days in all-comers. When integrated with clinical histories, AI may facilitate earlier detection of CA and connect patients to more timely management.
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