Heart failure & cardiomyopathies

Leveraging a Vision Transformer Model to Improve Diagnostic Accuracy of Cardiac Amyloidosis With Cardiac Magnetic Resonance

JACC Cardiovasc ImagingPublished Diagnostic accuracy study

Authors' conclusion

A ViT model using only cine and late gadolinium enhancement CMR images can achieve high accuracy in differentiating CA from other underlying etiologies of suspected cardiomyopathy, especially in cases when reported human diagnostic confidence was uncertain in both a large single state health system and in an external CA cohort.

Study context

Population
Patients referred for cardiac magnetic resonance (CMR) for suspicion of infiltrative disease or hypertrophic cardiomyopathyexternal cohort n = 157
Participants
807 patients
Comparison
Vision transformer (ViT) deep learning model for discrimination of cardiac amyloidosis on CMR
Outcome
Diagnostic accuracy and AUC for CA
Methodological detailsretrospective cohort, external validation
  • Retrospective cohort
  • External validation

Performance

As reported in the abstract

Discrimination of CA
Test or modelAccuracy (internal test set)AUC (internal test set)Accuracy (external test set)AUC (external test set)
ViT model84.1%0.95482.8%0.957

ViT model

Accuracy (internal test set)84.1%
AUC (internal test set)0.954
Accuracy (external test set)82.8%
AUC (external test set)0.957

Further findings

Accuracy by clinical report confidence of cardiac amyloidosis (CA) diagnosis (internal cohort)

Moderate/high confidence diagnosis of CA: accuracy (55 of 61)
90%
Uncertain, missing or incorrect diagnosis of CA: accuracy (22 of 36)
61.1%

Accuracy after removing studies with poor image quality, dual pathologies or ambiguous CA diagnosis

Deep learning (DL) accuracy of this cohort increased to
79.1%

Original publication

Leveraging a Vision Transformer Model to Improve Diagnostic Accuracy of Cardiac Amyloidosis With Cardiac Magnetic Resonance. JACC Cardiovasc Imaging. Published 4 December 2024. doi:10.1016/j.jcmg.2024.09.010

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