Evidence map›Paper›PMID 39641685›Full record

ArticleJACC. Cardiovascular imaging2025

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

Joshua Cockrum, Makiya Nakashima, Carl Ammoury, Diane Rizkallah, Joseph Mauch, David Lopez, David Wolinksy, Tae Hyun Hwang, Samir Kapadia, Lars G Svensson and 6 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in JACC. Cardiovascular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Imaging Advances in Light Chain Amyloidosis.Diagnostics (Basel, Switzerland) · 2026
    Review
  2. Review
  3. Article
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  5. Review
  6. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Joshua CockrumUniversity of Michigan Hospitals, Ann Arbor, Michigan, USA.
Makiya NakashimaHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Cardiovascular Innovations Research Center, Cleveland Clinic, Cleveland, Ohio, USA.
Carl AmmouryHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Cardiovascular Innovations Research Center, Cleveland Clinic, Cleveland, Ohio, USA.
Diane RizkallahHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Cardiovascular Innovations Research Center, Cleveland Clinic, Cleveland, Ohio, USA.
Joseph MauchUniversity of Michigan Hospitals, Ann Arbor, Michigan, USA.
David LopezHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Weston, Florida, USA.
David WolinksyHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Weston, Florida, USA.
Tae Hyun HwangDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, Florida, USA.
Samir KapadiaHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Lars G SvenssonHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Richard GrimmHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Diagnostic Services Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Mazen HannaHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA.
W H Wilson TangHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Cardiovascular Innovations Research Center, Cleveland Clinic, Cleveland, Ohio, USA.
Christopher NguyenHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Cardiovascular Innovations Research Center, Cleveland Clinic, Cleveland, Ohio, USA; Diagnostic Services Institute, Cleveland Clinic, Cleveland, Ohio, USA.
David ChenHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Cardiovascular Innovations Research Center, Cleveland Clinic, Cleveland, Ohio, USA.
Deborah KwonHeart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA; Cardiovascular Innovations Research Center, Cleveland Clinic, Cleveland, Ohio, USA; Diagnostic Services Institute, Cleveland Clinic, Cleveland, Ohio, USA. Electronic address: kwond@ccf.org.

Funding

Improving Phenotypic Classification and Prediction of Treatment Outcomes in Patients with Non-ischemic Cardiomyopathy and Functional Mitral RegurgitationR01HL170090 · NHLBI · CLEVELAND CLINIC LERNER COM-CWRU · PI DEBORAH KWON · 2023 to 2026
$3.1M
NHLBI NIH HHS R01 HL170090
6 · The paper itself

Abstract

backgroundCardiac magnetic resonance (CMR) imaging is an important diagnostic tool for diagnosis of cardiac amyloidosis (CA). However, discrimination of CA from other etiologies of myocardial disease can be challenging.

objectivesThe aim of this study was to develop and rigorously validate a deep learning (DL) algorithm to aid in the discrimination of CA using cine and late gadolinium enhancement CMR imaging.

methodsA DL model using a retrospective cohort of 807 patients who were referred for CMR for suspicion of infiltrative disease or hypertrophic cardiomyopathy (HCM) was developed. Confirmed definitive diagnosis was as follows: 252 patients with CA, 290 patients with HCM, and 265 with neither CA or HCM (other). This cohort was split 70/30 into training and test sets. A vision transformer (ViT) model was trained primarily to identify CA. The model was validated in an external cohort of 157 patients also referred for CMR for suspicion of infiltrative disease or HCM (51 CA, 49 HCM, and 57 other).

resultsThe ViT model achieved a diagnostic accuracy (84.1%) and an area under the curve of 0.954 in the internal testing data set. The ViT model further demonstrated an accuracy of 82.8% and an area under the curve of 0.957 in the external testing set. The ViT model achieved an accuracy of 90% (n = 55 of 61), among studies with clinical reports with moderate/high confidence diagnosis of CA, and 61.1% (n = 22 of 36) among studies with reported uncertain, missing, or incorrect diagnosis of CA in the internal cohort. DL accuracy of this cohort increased to 79.1% when studies with poor image quality, dual pathologies, or ambiguity of clinically significant CA diagnosis were removed.

conclusionsA 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.

Indexed as

AmyloidosisCardiomyopathiesCardiomyopathy, HypertrophicDecision Support TechniquesDeep LearningImage Interpretation, Computer-AssistedMagnetic Resonance Imaging, CineAgedContrast MediaDiagnosis, DifferentialFemaleHumansMaleMiddle AgedMyocardiumPredictive Value of TestsContrast Mediaartificial intelligencecardiac amyloidosiscardiac imaginghypertrophic cardiomyopathy

Identifiers

PMID39641685
PMCPMC11985257

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.