ArticlebioRxiv : the preprint server for biology2026
EchoVisuALL: From Echocardiography to Gene Discovery.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
5 authors.
Funding
Abstract
Cardiovascular diseases are a major global health burden, demanding phenotyping frameworks that can match the scale and complexity of contemporary mouse genetics. Here, we introduce EchoVisuALL, an AI-enabled pipeline for automated high-throughput transthoracic echocardiography (TTE) coupling deep-learning-based left-ventricular segmentation with data reporting. Across 65,000 recordings from over 18,000 mice, including single-gene knockouts from the International Mouse Phenotyping Consortium, the framework quantified cardiac morphology and function with minimal operator dependency and high reliability, validated against an expert-curated gold standard dataset. By extracting quantitative parameters across the cardiac cycle, EchoVisuALL in combination with multi-dimensional clustering uncovered nonlinear phenotypic relationships and revealed 37 of 715 genes associated with significant cardiac abnormalities, encompassing well-known human disease genes as well as 12 previously unrecognized candidates, including
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.