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@article{Lachmann:2022:ehjdh/ztac004,
author = {Lachmann, M and Rippen, E and Rueckert, D and Schuster, T and Xhepa, E and von, Scheidt M and Pellegrini, C and Trenkwalder, T and Rheude, T and Stundl, A and Thalmann, R and Harmsen, G and Yuasa, S and Schunkert, H and Kastrati, A and Joner, M and Kupatt, C and Laugwitz, KL},
doi = {ehjdh/ztac004},
journal = {Eur Heart J Digit Health},
pages = {153--168},
title = {Harnessing feature extraction capacities from a pre-trained convolutional neural network (VGG-16) for the unsupervised distinction of aortic outflow velocity profiles in patients with severe aortic stenosis.},
url = {http://dx.doi.org/10.1093/ehjdh/ztac004},
volume = {3},
year = {2022}
}

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TY  - JOUR
AB  - AIMS: Hypothesizing that aortic outflow velocity profiles contain more valuable information about aortic valve obstruction and left ventricular contractility than can be captured by the human eye, features of the complex geometry of Doppler tracings from patients with severe aortic stenosis (AS) were extracted by a convolutional neural network (CNN). METHODS AND RESULTS: After pre-training a CNN (VGG-16) on a large data set (ImageNet data set; 14 million images belonging to 1000 classes), the convolutional part was employed to transform Doppler tracings to 1D arrays. Among 366 eligible patients [age: 79.8 ± 6.77 years; 146 (39.9%) women] with pre-procedural echocardiography and right heart catheterization prior to transcatheter aortic valve replacement (TAVR), good quality Doppler tracings from 101 patients were analysed. The convolutional part of the pre-trained VGG-16 model in conjunction with principal component analysis and k-means clustering distinguished two shapes of aortic outflow velocity profiles. Kaplan-Meier analysis revealed that mortality in patients from Cluster 2 (n = 40, 39.6%) was significantly increased [hazard ratio (HR) for 2-year mortality: 3; 95% confidence interval (CI): 1-8.9]. Apart from reduced cardiac output and mean aortic valve gradient, patients from Cluster 2 were also characterized by signs of pulmonary hypertension, impaired right ventricular function, and right atrial enlargement. After training an extreme gradient boosting algorithm on these 101 patients, validation on the remaining 265 patients confirmed that patients assigned to Cluster 2 show increased mortality (HR for 2-year mortality: 2.6; 95% CI: 1.4-5.1, P-value: 0.004). CONCLUSION: Transfer learning enables sophisticated pattern recognition even in clinical data sets of limited size. Importantly, it is the left ventricular compensation capacity in the face of increased afterload, and not so much the actual obstruction of the aortic valve, that determines fate after
AU  - Lachmann,M
AU  - Rippen,E
AU  - Rueckert,D
AU  - Schuster,T
AU  - Xhepa,E
AU  - von,Scheidt M
AU  - Pellegrini,C
AU  - Trenkwalder,T
AU  - Rheude,T
AU  - Stundl,A
AU  - Thalmann,R
AU  - Harmsen,G
AU  - Yuasa,S
AU  - Schunkert,H
AU  - Kastrati,A
AU  - Joner,M
AU  - Kupatt,C
AU  - Laugwitz,KL
DO  - ehjdh/ztac004
EP  - 168
PY  - 2022///
SP  - 153
TI  - Harnessing feature extraction capacities from a pre-trained convolutional neural network (VGG-16) for the unsupervised distinction of aortic outflow velocity profiles in patients with severe aortic stenosis.
T2  - Eur Heart J Digit Health
UR  - http://dx.doi.org/10.1093/ehjdh/ztac004
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36713009
VL  - 3
ER  -

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