Key result
MiNN-Echo outperforms human readers with ~0.77 AUC differentiating Takotsubo syndrome from acute MI.
Why the study?
The pathophysiology of Takotsubo syndrome (TTS) remains perplexing, motivating the development of a motion-informed neural network (MiNN-Echo) using echocardiograms to elucidate the diastolic mechanics of TTS.
Does a motion-informed neural network (MiNN-Echo) improve differentiation of Takotsubo syndrome from acute anterior myocardial infarction and elucidate diastolic mechanics compared with expert human readers?
Cohort (n=1,142)
Yes
Does a motion-informed neural network (MiNN-Echo) improve differentiation of Takotsubo syndrome from acute anterior myocardial infarction and elucidate diastolic mechanics compared with expert human readers?
A motion-informed deep learning model (MiNN-Echo) outperformed human experts in distinguishing Takotsubo syndrome from anterior MI on echocardiography and identified impaired diastolic relaxation kinematics as the primary distinguishing feature.
Answers are grounded in this paper and cite their sources.
May aid echocardiographic differentiation of Takotsubo syndrome from MI; hypothesis-generating and requires prospective validation before clinical adoption.
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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Zaman et al. (2026) conducted a cohort in Takotsubo syndrome (TTS) and anterior myocardial infarction (n=1,142). Motion-informed neural network (MiNN-Echo) echocardiographic prediction model vs. Human interpretation by 68 expert echocardiographic readers was evaluated on Differentiation of Takotsubo syndrome from acute anterior myocardial infarction (AUC 0.77 vs 0.69). MiNN-Echo achieved an AUC of 0.77 compared with 0.69 for human readers in differentiating Takotsubo syndrome from acute myocardial infarction, driven mainly by diastolic motion features.
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