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Bukas, C. ; Galter, I. ; da Silva Buttkus, P. ; Fuchs, H. ; Maier, H. ; Gailus-Durner, V. ; Müller, C.L. ; Hrabě de Angelis, M. ; Piraud, M. ; Spielmann, N.

Echo2Pheno: A deep-learning application to uncover echocardiographic phenotypes in conscious mice.

Mamm. Genome 34, 200-215 (2023)
DOI PMC
Creative Commons Lizenzvertrag
Open Access Green as soon as Postprint is submitted to ZB.
Echocardiography, a rapid and cost-effective imaging technique, assesses cardiac function and structure. Despite its popularity in cardiovascular medicine and clinical research, image-derived phenotypic measurements are manually performed, requiring expert knowledge and training. Notwithstanding great progress in deep-learning applications in small animal echocardiography, the focus has so far only been on images of anesthetized rodents. We present here a new algorithm specifically designed for echocardiograms acquired in conscious mice called Echo2Pheno, an automatic statistical learning workflow for analyzing and interpreting high-throughput non-anesthetized transthoracic murine echocardiographic images in the presence of genetic knockouts. Echo2Pheno comprises a neural network module for echocardiographic image analysis and phenotypic measurements, including a statistical hypothesis-testing framework for assessing phenotypic differences between populations. Using 2159 images of 16 different knockout mouse strains of the German Mouse Clinic, Echo2Pheno accurately confirms known cardiovascular genotype-phenotype relationships (e.g., Dystrophin) and discovers novel genes (e.g., CCR4-NOT transcription complex subunit 6-like, Cnot6l, and synaptotagmin-like protein 4, Sytl4), which cause altered cardiovascular phenotypes, as verified by H&E-stained histological images. Echo2Pheno provides an important step toward automatic end-to-end learning for linking echocardiographic readouts to cardiovascular phenotypes of interest in conscious mice.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Mitochondrial; Identification; Slc6a15; Risk
ISSN (print) / ISBN 0938-8990
e-ISSN 1432-1777
Journal Mammalian Genome
Quellenangaben Volume: 34, Issue: 2, Pages: 200-215 Article Number: , Supplement: ,
Publisher Springer
Publishing Place One New York Plaza, Suite 4600, New York, Ny, United States
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Experimental Genetics (IEG)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of Computational Biology (ICB)
Grants German Center for Diabetes Research (DZD) (MHdA)
German Federal Ministry of Education and Research
Projekt DEAL