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Allegra, A., Mirabile, G., Tonacci, A., Genovese, S., Pioggia, G. and Gangemi, S. (2023) Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis. International Journal of Molecular Sciences, 24, Article 5680.
https://doi.org/10.3390/ijms24065680

has been cited by the following article:

• TITLE: Diagnosis and Management of Cardiac Amyloidosis: The Role of Multimodal Imaging—A Mini Review

  AUTHORS: Camila Ponce-Acosta, Maria Jose Santa-Ana-Bayona, Gilberto H. Acosta-Gutiérrez, Pavel Martinez-Dominguez, Santiago Luna-Alcala, Alfonso Gonzalez-Trejo, Hugo A. Valencia-Hernandez, Enrique C. Guerra, Nilda Espinola-Zavaleta

  KEYWORDS: Amyloid, Echocardiography, Diastolic Dysfunction, Cardiac Infiltration

  JOURNAL NAME: World Journal of Cardiovascular Diseases, Vol.15 No.5, May 23, 2025

  ABSTRACT: Cardiac amyloidosis is a disease characterized by the deposition of misfolded proteins in the myocardium, leading to restrictive cardiomyopathy. It comprises two subtypes: immunoglobulin light chain cardiac amyloidosis (AL) and transthyretin cardiac amyloidosis (ATTR). ATTR is classified as wild-type (wtATTR) and hereditary (hATTR), depending on the presence or absence of a transthyretin (TTR) genetic mutation. The diagnosis of cardiac amyloidosis is challenging due to nonspecific symptoms with other cardiac conditions, often resulting in misdiagnosis. This mini review provides an in-depth analysis of cardiac amyloidosis, focusing on its subtypes, clinical manifestations, diagnostic multimodal imaging, and recent therapeutic advancements.