Deep learning from atrioventricular plane displacement in patients with Takotsubo syndrome: lighting up the black-box

Fahim Zaman; Nicholas Isom; Amanda Chang; Yi Grace Wang; Ahmed Abdelhamid; Arooj Khan; Majesh Makan; Mahmoud Abdelghany; Xiaodong Wu; Kan Liu

doi:10.1093/ehjdh/ztad077

Deep learning from atrioventricular plane displacement in patients with Takotsubo syndrome: lighting up the black-box

Eur Heart J Digit Health. 2023 Dec 6;5(2):134-143. doi: 10.1093/ehjdh/ztad077. eCollection 2024 Mar.

Authors

Fahim Zaman¹, Nicholas Isom², Amanda Chang², Yi Grace Wang³, Ahmed Abdelhamid², Arooj Khan², Majesh Makan⁴, Mahmoud Abdelghany⁵, Xiaodong Wu¹, Kan Liu^{2

4}

Affiliations

¹ Department of Electrical and Computer Engineering, University of Iowa, 103 S. Capitol St., 3318 SC, Iowa City, IA 52242, USA.
² Division of Cardiology, Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA.
³ Department of Mathematics, California State University Dominguez Hills, 1000 E. Victoria Street, Carson, CA 90747, USA.
⁴ Division of Cardiology, Department of Internal Medicine, Washington University, 4940 Parkview Place, St Louis, MO 63110, USA.
⁵ Department of Cardiovascular Medicine, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195, USA.

Abstract

Aims: The spatiotemporal deep convolutional neural network (DCNN) helps reduce echocardiographic readers' erroneous 'judgement calls' on Takotsubo syndrome (TTS). The aim of this study was to improve the interpretability of the spatiotemporal DCNN to discover latent imaging features associated with causative TTS pathophysiology.

Methods and results: We applied gradient-weighted class activation mapping analysis to visualize an established spatiotemporal DCNN based on the echocardiographic videos to differentiate TTS (150 patients) from anterior wall ST-segment elevation myocardial infarction (STEMI, 150 patients). Forty-eight human expert readers interpreted the same echocardiographic videos and prioritized the regions of interest on myocardium for the differentiation. Based on visualization results, we completed optical flow measurement, myocardial strain, and Doppler/tissue Doppler echocardiography studies to investigate regional myocardial temporal dynamics and diastology. While human readers' visualization predominantly focused on the apex of the heart in TTS patients, the DCNN temporal arm's saliency visualization was attentive on the base of the heart, particularly at the atrioventricular (AV) plane. Compared with STEMI patients, TTS patients consistently showed weaker peak longitudinal displacement (in pixels) in the basal inferoseptal (systolic: 2.15 ± 1.41 vs. 3.10 ± 1.66, P < 0.001; diastolic: 2.36 ± 1.71 vs. 2.97 ± 1.69, P = 0.004) and basal anterolateral (systolic: 2.70 ± 1.96 vs. 3.44 ± 2.13, P = 0.003; diastolic: 2.73 ± 1.70 vs. 3.45 ± 2.20, P = 0.002) segments, and worse longitudinal myocardial strain in the basal inferoseptal (-8.5 ± 3.8% vs. -9.9 ± 4.1%, P = 0.013) and basal anterolateral (-8.6 ± 4.2% vs. -10.4 ± 4.1%, P = 0.006) segments. Meanwhile, TTS patients showed worse diastolic mechanics than STEMI patients (E'/septal: 5.1 ± 1.2 cm/s vs. 6.3 ± 1.5 cm/s, P < 0.001; S'/septal: 5.8 ± 1.3 cm/s vs. 6.8 ± 1.4 cm/s, P < 0.001; E'/lateral: 6.0 ± 1.4 cm/s vs. 7.9 ± 1.6 cm/s, P < 0.001; S'/lateral: 6.3 ± 1.4 cm/s vs. 7.3 ± 1.5 cm/s, P < 0.001; E/E': 15.5 ± 5.6 vs. 12.5 ± 3.5, P < 0.001).

Conclusion: The spatiotemporal DCNN saliency visualization helps identify the pattern of myocardial temporal dynamics and navigates the quantification of regional myocardial mechanics. Reduced AV plane displacement in TTS patients likely correlates with impaired diastolic mechanics.

Keywords: Atrioventricular plane; Diastolic dysfunction; ST-segment elevation myocardial infarction; Saliency visualization; Spatiotemporal deep convolutional neural networks; Takotsubo syndrome.