ArticleEClinicalMedicine2021
Spatio-temporal hybrid neural networks reduce erroneous human "judgement calls" in the diagnosis of Takotsubo syndrome.
Article in EClinicalMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The roles of quantitative doppler echocardiography in optimizing guideline-directed medical therapy in Post-STEMI patients.The international journal of cardiovascular imaging · 2026Review
- A nomogram for predicting in-hospital death in a multinational cohort of patients with takotsubo syndrome.European journal of clinical investigation · 2026Article
- Deep Learning-Driven Innovations in Echocardiography: Taxonomy, Clinical Impact, Challenges, and Opportunities.Annals of biomedical engineering · 2026Review
- Data Leakage in Deep Learning for Alzheimer's Disease Diagnosis: A Scoping Review of Methodological Rigor and Performance Inflation.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms.Biophysics reviews · 2024Review
- Deep learning from atrioventricular plane displacement in patients with Takotsubo syndrome: lighting up the black-box.European heart journal. Digital health · 2024Article
- Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
backgroundWe investigate whether deep learning (DL) neural networks can reduce erroneous human "judgment calls" on bedside echocardiograms and help distinguish Takotsubo syndrome (TTS) from anterior wall ST segment elevation myocardial infarction (STEMI).
methodsWe developed a single-channel (DCNN[2D SCI]), a multi-channel (DCNN[2D MCI]), and a 3-dimensional (DCNN[2D+
findingsThe DCNN (2D SCI), DCNN (2D MCI), DCNN(2D+
interpretationSpatio-temporal hybrid DL neural networks reduce erroneous human "judgement calls" in distinguishing TTS from anterior wall STEMI based on bedside echocardiographic videos.
fundingUniversity of Iowa Obermann Center for Advanced Studies Interdisciplinary Research Grant, and Institute for Clinical and Translational Science Grant. National Institutes of Health Award (1R01EB025018-01).
Identifiers
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Registered trials
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