Spatio-temporal hybrid neural networks reduce erroneous human "judgement calls" in the diagnosis of Takotsubo syndrome.
washington; everett; prmc
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+
Findings: The DCNN (2D SCI), DCNN (2D MCI), DCNN(2D+
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).
Zaman, Fahim; Ponnapureddy, Rakesh; Wang, Yi Grace; Chang, Amanda; Cadaret, Linda M; Abdelhamid, Ahmed; Roy, Shubha D; Makan, Majesh; Zhou, Ruihai; Jayanna, Manju B; Gnall, Eric; Dai, Xuming; Singh, Avneet; Zheng, Jingsheng; Boppana, Venkata S; Wang, Feng; Singh, Pahul; Wu, Xiaodong; and Liu, Kan, "Spatio-temporal hybrid neural networks reduce erroneous human "judgement calls" in the diagnosis of Takotsubo syndrome." (2021). Articles, Abstracts, and Reports. 5296.