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

IF 3.9 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS European heart journal. Digital health Pub Date : 2023-12-06 eCollection Date: 2024-03-01 DOI:10.1093/ehjdh/ztad077
Fahim Zaman, Nicholas Isom, Amanda Chang, Yi Grace Wang, Ahmed Abdelhamid, Arooj Khan, Majesh Makan, Mahmoud Abdelghany, Xiaodong Wu, Kan Liu
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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.

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利用塔克次氏综合征患者房室平面位移进行深度学习:点亮黑盒。
目的:时空深度卷积神经网络(DCNN)有助于减少超声心动图读者对塔克氏综合征(TTS)的错误 "判断"。本研究旨在提高时空深度卷积神经网络的可解释性,以发现与 TTS 病理生理学相关的潜在成像特征:我们应用梯度加权类激活图谱分析法对基于超声心动图视频建立的时空DCNN进行可视化分析,以区分TTS(150例患者)和前壁ST段抬高型心肌梗死(STEMI,150例患者)。48 位人类专家对相同的超声心动图视频进行了解读,并对心肌上的感兴趣区进行了优先区分。根据可视化结果,我们完成了光学血流测量、心肌应变和多普勒/组织多普勒超声心动图研究,以调查区域心肌的时间动态和舒缩。在 TTS 患者中,人类读者的可视化主要集中在心尖,而 DCNN 颞臂的突出可视化则集中在心脏底部,尤其是房室平面。与 STEMI 患者相比,TTS 患者在基底内侧(收缩期:2.15 ± 1.41 vs. 3.10 ± 1.66,P < 0.001;舒张期:2.36 ± 1.71 vs. 2.97 ± 1.69,P = 0.004)和基底前外侧(收缩期:2.70 ± 1.96 vs. 3.44 ± 2.13,P = 0.003;舒张期:2.73 ± 1.70 vs. 3.45 ± 2.20,P = 0.002)节段,基底部下(-8.5 ± 3.8% vs. -9.9 ± 4.1%,P = 0.013)和基底部前外侧(-8.6 ± 4.2% vs. -10.4 ± 4.1%,P = 0.006)节段的心肌纵向应变较差。同时,TTS 患者的舒张力学表现比 STEMI 患者差(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)。6.8±1.4厘米/秒,P<0.001;E'/外侧:6.0±1.4厘米/秒 vs. 7.9±1.6厘米/秒,P<0.001;S'/外侧:6.3±1.4厘米/秒 vs. 7.3±1.5厘米/秒,P<0.001;E/E':15.5 ± 5.6 vs. 12.5 ± 3.5,P < 0.001):时空 DCNN 突出可视化有助于识别心肌的时空动态模式,并为区域心肌力学的量化提供导航。TTS 患者房室平面位移减少可能与舒张力学受损有关。
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