Evidence map›Paper›PMID 36712167›Full record

ArticleEuropean heart journal. Digital health2022

Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy

Jessica Torres Soto, J Weston Hughes, Pablo Amador Sanchez, Marco Perez, David Ouyang, Euan A Ashley

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
9.8field-weighted citation impact, top 1% of its field
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

35 citing papers in PubMed, 65 citations in OpenAlex.

  1. Article
  2. Article
  3. Deep learning to predict left ventricular hypertrophy from the electrocardiogram.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026
    Article
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  5. Deep learning in the early diagnosis of acute aortic dissection.Frontiers in cardiovascular medicine · 2026
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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

6 authors at 2 institutions in 1 country.

Jessica Torres SotoDepartment of Biomedical Data Science, Stanford University, USA.ORCID https://orcid.org/0000-0003-0490-4448
J Weston HughesDepartment of Computer Science, Stanford University, USA.ORCID https://orcid.org/0000-0002-7042-4031
Pablo Amador SanchezDepartment of Medicine, Division of Cardiology, Stanford University, Stanford, California, USA.
Marco PerezDepartment of Medicine, Division of Cardiology, Stanford University, Stanford, California, USA.
David OuyangDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, USA.
Euan A AshleyDepartment of Medicine, Division of Cardiology, Stanford University, Stanford, California, USA.
Stanford University · USCedars-Sinai Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Determining the aetiology of left ventricular hypertrophy (LVH) can be challenging due to the similarity in clinical presentation and cardiac morphological features of diverse causes of disease. In particular, distinguishing individuals with hypertrophic cardiomyopathy (HCM) from the much larger set of individuals with manifest or occult hypertension (HTN) is of major importance for family screening and the prevention of sudden death. We hypothesized that an artificial intelligence method based joint interpretation of 12-lead electrocardiograms and echocardiogram videos could augment physician interpretation. Methods and results: We chose not to train on proximate data labels such as physician over-reads of ECGs or echocardiograms but instead took advantage of electronic health record derived clinical blood pressure measurements and diagnostic consensus (often including molecular testing) among physicians in an HCM centre of excellence. Using more than 18 000 combined instances of electrocardiograms and echocardiograms from 2728 patients, we developed LVH-fusion. On held-out test data, LVH-fusion achieved an F1-score of 0.71 in predicting HCM, and 0.96 in predicting HTN. In head-to-head comparison with human readers LVH-fusion had higher sensitivity and specificity rates than its human counterparts. Finally, we use explainability techniques to investigate local and global features that positively and negatively impact LVH-fusion prediction estimates providing confirmation from unsupervised analysis the diagnostic power of lateral T-wave inversion on the ECG and proximal septal hypertrophy on the echocardiogram for HCM. Conclusion: These results show that deep learning can provide effective physician augmentation in the face of a common diagnostic dilemma with far reaching implications for the prevention of sudden cardiac death.

Indexed as

Artificial IntelligenceEchocardiogramElectrocardiogramHypertensionHypertrophic cardiomyopathyMultimodal data

Identifiers

PMID36712167
PMCPMC9707995
OpenAlexW4281400187

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.