Motion-Informed Neural Network Towards Diastolic Mechanics in Takotsubo Syndrome

BACKGROUND: The pathophysiology of Takotsubo syndrome (TTS) remains perplexing. We developed a motion-informed neural network (MiNN-Echo) using echocardiograms to elucidate the diastolic mechanics of TTS. METHODS: We trained and validated MiNN-Echo, a deep convolutional neural network (2D+t) echocardiographic prediction model using multi-vendor echocardiograms from 1142 patients (596 TTS and 546 anterior myocardial infarction) in a multi-institutional cohort. A total of 68 expert echocardiographic readers performed human interpretation. The network’s dual-branch architecture independently learns spatial and temporal features, using 2-dimensional image frames for spatial representation and segmentation, and optical flow with sinusoidal phase encoding for temporal dynamics. The MiNN-Echo model integrates static cardiac anatomic changes with motion details from 2-dimensional echocardiographic videos to produce interpretable disease predictions. RESULTS: The MiNN-Echo model achieved an area under the curve of 0.77, compared with 0.69 among human readers in differentiating TTS from acute myocardial infarction. Myocardial motion features during diastole contributed more (≈71%) to the differential diagnosis than those during systole. Compared with patients with acute myocardial infarction, TTS patients showed increased isovolumic relaxation time (112.5 [interquartile range (IQR), 68.75–165.94] ms versus 87.5 [IQR, 54.17–140] ms, P <0.001; 28% versus 19% of diastolic time, P <0.001), decreased passive ventricular filling (before atrial contraction) time (176.04 [IQR, 114.2–248.12] ms versus 258.62 [IQR, 199.89–331.9] ms, P <0.001; 44% versus 54% of diastolic time, P <0.001), and a different mitral-annulus motion trajectory: lower pixel-wise myocardial displacement in the isovolumic relaxation (0.35 [IQR, 0.26–0.47] versus 0.38 [IQR, 0.28–0.51]; P =0.006), passive (0.45 [IQR, 0.33–0.58] versus 0.50 [IQR, 0.38–0.68]; P <0.001), and active ventricular filling (0.43 [IQR, 0.32–0.56] versus 0.48 [IQR, 0.35–0.63]; P =0.001) phases, and altered rotation angles (away from the horizontal A–V plane) in the passive ventricular filling phase (−70.19 [IQR, −89.62 to −41.33] versus −60.34 [IQR, −87.07 to −30.84]; P =0.020). CONCLUSIONS: MiNN-Echo in echocardiograms elucidates unique myocardial relaxation kinematics to characterize TTS-associated diastolic mechanics.

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Journal
Circulation Cardiovascular Imaging
Published
2026-11-11
DOI
https://doi.org/10.1161/circimaging.126.020258
Primary Topic
Takotsubo Cardiomyopathy and Associated Phenomena
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article
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article

Motion-Informed Neural Network Towards Diastolic Mechanics in Takotsubo Syndrome

Christian Templin, Amanda Chang, My‐Le Nguyen, Tou Kun Chong et al.
Circulation Cardiovascular Imaging
Takotsubo Cardiomyopathy and Associated Phenomena
article

Motion-Informed Neural Network Towards Diastolic Mechanics in Takotsubo Syndrome

Christian Templin, Amanda Chang, My‐Le Nguyen, Tou Kun Chong, Davide Di Vece, Fahim Ahmed Zaman, Frank F. Seghatol, Sándor J. Kovács, Grace Torres-Santos, Talal Asif, Jian Chen, Xiaodong Wu, Crystal Licari, Kan Liu, Sabrina Vaughn, Yuyi Chen, Xiaojie Chen
article en

Abstract

BACKGROUND: The pathophysiology of Takotsubo syndrome (TTS) remains perplexing. We developed a motion-informed neural network (MiNN-Echo) using echocardiograms to elucidate the diastolic mechanics of TTS. METHODS: We trained and validated MiNN-Echo, a deep convolutional neural network (2D+t) echocardiographic prediction model using multi-vendor echocardiograms from 1142 patients (596 TTS and 546 anterior myocardial infarction) in a multi-institutional cohort. A total of 68 expert echocardiographic readers performed human interpretation. The network’s dual-branch architecture independently learns spatial and temporal features, using 2-dimensional image frames for spatial representation and segmentation, and optical flow with sinusoidal phase encoding for temporal dynamics. The MiNN-Echo model integrates static cardiac anatomic changes with motion details from 2-dimensional echocardiographic videos to produce interpretable disease predictions. RESULTS: The MiNN-Echo model achieved an area under the curve of 0.77, compared with 0.69 among human readers in differentiating TTS from acute myocardial infarction. Myocardial motion features during diastole contributed more (≈71%) to the differential diagnosis than those during systole. Compared with patients with acute myocardial infarction, TTS patients showed increased isovolumic relaxation time (112.5 [interquartile range (IQR), 68.75–165.94] ms versus 87.5 [IQR, 54.17–140] ms, P <0.001; 28% versus 19% of diastolic time, P <0.001), decreased passive ventricular filling (before atrial contraction) time (176.04 [IQR, 114.2–248.12] ms versus 258.62 [IQR, 199.89–331.9] ms, P <0.001; 44% versus 54% of diastolic time, P <0.001), and a different mitral-annulus motion trajectory: lower pixel-wise myocardial displacement in the isovolumic relaxation (0.35 [IQR, 0.26–0.47] versus 0.38 [IQR, 0.28–0.51]; P =0.006), passive (0.45 [IQR, 0.33–0.58] versus 0.50 [IQR, 0.38–0.68]; P <0.001), and active ventricular filling (0.43 [IQR, 0.32–0.56] versus 0.48 [IQR, 0.35–0.63]; P =0.001) phases, and altered rotation angles (away from the horizontal A–V plane) in the passive ventricular filling phase (−70.19 [IQR, −89.62 to −41.33] versus −60.34 [IQR, −87.07 to −30.84]; P =0.020). CONCLUSIONS: MiNN-Echo in echocardiograms elucidates unique myocardial relaxation kinematics to characterize TTS-associated diastolic mechanics.

Circulation Cardiovascular Imaging
University of Iowa (US), Washington University in St. Louis (US), Universitätsmedizin Greifswald (DE), California State University, Dominguez Hills (US), Universität Greifswald (DE), National Heart, Lung, and Blood Institute (US), Fifth Affiliated Hospital of Sun Yat-sen University (CN), Kiang Wu Hospital (CN), University of Missouri–Kansas City (US)
Takotsubo Cardiomyopathy and Associated Phenomena
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