Spatio-temporal hybrid neural networks reduce erroneous human “judgement calls” in the diagnosis of Takotsubo syndrome

Fahim Zaman, Rakesh Ponnapureddy, Yi Grace Wang, Amanda Chang, Linda M. Cadaret, Ahmed Abdelhamid, Shubha D. Roy, Majesh Makan, Ruihai Zhou, Manju B. Jayanna, Eric Gnall, Xuming Dai, Avneet Singh, Jingsheng Zheng, Venkata S. Boppana, Feng Wang, Pahul Singh, Xiaodong Wu, Kan Liu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Background: We 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). Methods: We developed a single-channel (DCNN[2D SCI]), a multi-channel (DCNN[2D MCI]), and a 3-dimensional (DCNN[2D+t]) deep convolution neural network, and a recurrent neural network (RNN) based on 17,280 still-frame images and 540 videos from 2-dimensional echocardiograms in 10 years (1 January 2008 to 1 January 2018) retrospective cohort in University of Iowa (UI) and eight other medical centers. Echocardiograms from 450 UI patients were randomly divided into training and testing sets for internal training, testing, and model construction. Echocardiograms of 90 patients from the other medical centers were used for external validation to evaluate the model generalizability. A total of 49 board-certified human readers performed human-side classification on the same echocardiography dataset to compare the diagnostic performance and help data visualization. Findings: The DCNN (2D SCI), DCNN (2D MCI), DCNN(2D+t), and RNN models established based on UI dataset for TTS versus STEMI prediction showed mean diagnostic accuracy 73%, 75%, 80%, and 75% respectively, and mean diagnostic accuracy of 74%, 74%, 77%, and 73%, respectively, on the external validation. DCNN(2D+t) (area under the curve [AUC] 0·787 vs. 0·699, P = 0·015) and RNN models (AUC 0·774 vs. 0·699, P = 0·033) outperformed human readers in differentiating TTS and STEMI by reducing human erroneous judgement calls on TTS. Interpretation: Spatio-temporal hybrid DL neural networks reduce erroneous human “judgement calls” in distinguishing TTS from anterior wall STEMI based on bedside echocardiographic videos. Funding: University 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).

Original languageEnglish
Article number101115
StatePublished - Oct 2021


  • Anterior wall ST segment elevation myocardial infarction
  • Bedside echocardiographic videos
  • Deep convolution neural network
  • Judgment calls
  • Recurrent neural network
  • Spatiotemporal information
  • Takotsubo syndrome


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