Deep learning model to diagnose cardiac amyloidosis from haematoxylin/eosin-stained myocardial tissue.

ABSTRACT:

Aims

Amyloid deposition in myocardial tissue is a definitive feature for diagnosing cardiac amyloidosis, though less invasive imaging modalities such as bone tracer cardiac scintigraphy and cardiac magnetic resonance imaging have been established as first steps for its diagnosis. This study aimed to develop a deep learning model to support the diagnosis of cardiac amyloidosis from haematoxylin/eosin (HE)-stained myocardial tissue.

Methods and results

This single-centre retrospective observational study enrolled 166 patients who underwent myocardial biopsies between 2008 and 2022, including 76 patients diagnosed with cardiac amyloidosis and 90 with other diagnoses. A deep learning model was developed to output the probabilities of cardiac amyloidosis for all the small patches

SUBMITTER: Tohyama T 

PROVIDER: S-EPMC11728699 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature