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- W3178711293 abstract "The aim of this study was to investigate the existence of different patterns of fetal cardiac hemodynamic remodelling, and their association with the neonatal presentation in tetralogy of Fallot (TOF), through the integration of echocardiographic data by means of interpretable unsupervised machine learning. Third trimester fetal echocardiographic data from 36 healthy and 30 TOF fetuses was used. We defined a composite outcome score indicating the occurrence of any of four adverse neonatal events. We used multiple kernel learning and k-means clustering to reduce data dimensionality, position patients based on similarities and find homogeneous groups of patients with similar hemodynamics. Clustering of the low-dimensional space resulted in 4 clusters (Cl) (figure a), with significant different proportion of TOF patients and composite adverse outcome score. Cl4 has the highest proportion of SGA fetuses (figure c) and compromised fetuses with highest pulmonary and tricuspid A wave peak velocities and reduced and delayed aortic peak velocity (figure b). In contrast, Cl1 includes fetuses with bigger pulmonary valves and lowest pulmonary and tricuspid A wave peak velocities (figure b). Our results serve as a proof-of-concept that interpretable unsupervised machine learning can be useful to explore and understand different fetal patterns of cardiac remodelling in CHD and relate these to neonatal clinical course. Supporting information can be found in the online version of this abstract" @default.
- W3178711293 created "2021-07-19" @default.
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- W3178711293 date "2020-10-01" @default.
- W3178711293 modified "2023-10-06" @default.
- W3178711293 title "VP14.02: Machine learning‐based phenogrouping of third trimester echocardiography can predict the neonatal clinical course in fetuses with tetralogy of Fallot" @default.
- W3178711293 doi "https://doi.org/10.1002/uog.22531" @default.
- W3178711293 hasPublicationYear "2020" @default.
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