Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Case Reports
. 2024 May 24;14(6):559.
doi: 10.3390/jpm14060559.

Artificial Intelligence Assists in the Early Identification of Cardiac Amyloidosis

Courtney R Kenyon  1 , Milagros Pereyra Pietri  1 , Julie L Rosenthal  1 , Reza Arsanjani  1 , Chadi Ayoub  1
Affiliations
Case Reports

Artificial Intelligence Assists in the Early Identification of Cardiac Amyloidosis

Courtney R Kenyon et al. J Pers Med. .

Abstract

A 69-year-old female presented with symptomatic atrial fibrillation. Cardiac amyloidosis was suspected due to an artificial intelligence clinical tool applied to the presenting electrocardiogram predicting a high probability for amyloidosis, and the subsequent unexpected finding of left atrial appendage thrombus reinforced this clinical suspicion. This facilitated an early diagnosis by the biopsy of AL cardiac amyloidosis and the prompt initiation of targeted therapy. This case highlights the utilization of an AI clinical tool and its impact on clinical care, particularly for the early detection of a rare and difficult to diagnose condition where early therapy is critical.

Keywords: MRI; artificial intelligence; cardiac amyloidosis; case report; echocardiography; electrocardiography.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Artificial intelligence results applied to Electrocardiogram (ECG). Panel (A) demonstrates twelve lead ECG with atrial fibrillation with rapid ventricular response (117 beats per minute), low voltage, and pseudoinfarct pattern. Panel (B) shows artificial intelligence (AI) increased probability for cardiac amyloidosis at 93% (red diamond) together with 0.2% probability for hypertrophic cardiomyopathy (panel (C), purple diamond), 17% probability for aortic stenosis (panel (D), purple diamond), and 89% probability of low ejection fraction (panel (E), purple diamond). In panels (B–E), the blue dots represent previous ECG AI prediction for the respective conditions, and the red dotted line represents the cut off increased probability for that condition as predicted by AI.
Figure 2
Figure 2
Imaging with Echocardiography. Panels (A,B) are transthoracic echocardiogram (TTE) apical four chamber views with and without ultrasound enhancing agent, respectively, showing borderline increased left ventricular wall thickness. Panel (C) is a transesophageal echocardiogram image demonstrating left atrial appendage with thrombus (red arrow) and spontaneous echo contrast in the left atrium. Abbreviations: LV—left ventricle, RV—right ventricle, LA—left atrium, LAA—left atrial appendage.
Figure 3
Figure 3
Cardiac Magnetic Resonance Imaging (MRI) findings. Panels (A,B) are delayed-phase images on cardiac MRI in the short-axis, and 4-chamber views, respectively, demonstrating diffuse late gadolinium uptake throughout the left and right ventricular myocardium as well as in the atrial free walls, consistent with infiltrative process such as cardiac amyloid. Abbreviations: LA—left atrium, LV—left ventricle, RA—right atrium, RV—right ventricle.
See this image and copyright information in PMC

References

    1. Fine N.M. Challenges and Strategies in the Diagnosis of Cardiac Amyloidosis. Can. J. Cardiol. 2020;36:441–443. doi: 10.1016/j.cjca.2019.09.017. - DOI - PubMed
    1. Kamel M.A., Abbas M.T., Kanaan C.N., Awad K.A., Baba Ali N., Scalia I.G., Farina J.M., Pereyra M., Mahmoud A.K., Steidley D.E., et al. How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges. J. Cardiovasc. Dev. Dis. 2024;11:118. doi: 10.3390/jcdd11040118. - DOI - PMC - PubMed
    1. Baker K.R. Light Chain Amyloidosis: Epidemiology, Staging, and Prognostication. Methodist. Debakey Cardiovasc. J. 2022;18:27–35. doi: 10.14797/mdcvj.1070. - DOI - PMC - PubMed
    1. Lousada I., Comenzo R.L., Landau H., Guthrie S., Merlini G. Light Chain Amyloidosis: Patient Experience Survey from the Amyloidosis Research Consortium. Adv. Ther. 2015;32:920–928. doi: 10.1007/s12325-015-0250-0. - DOI - PMC - PubMed
    1. Grogan M., Lopez-Jimenez F., Cohen-Shelly M., Dispenzieri A., Attia Z.I., Abou Ezzedine O.F., Lin G., Kapa S., Borgeson D.D., Friedman P.A., et al. Artificial Intelligence-Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis. Mayo Clin. Proc. 2021;96:2768–2778. doi: 10.1016/j.mayocp.2021.04.023. - DOI - PubMed

Publication types

LinkOut - more resources