Automated assessment of cardiac dynamics in aging and dilated cardiomyopathy Drosophila models using machine learning

Bibliographic Details
Title: Automated assessment of cardiac dynamics in aging and dilated cardiomyopathy Drosophila models using machine learning
Authors: Yash Melkani, Aniket Pant, Yiming Guo, Girish C. Melkani
Source: Communications Biology, Vol 7, Iss 1, Pp 1-11 (2024)
Publisher Information: Nature Portfolio, 2024.
Publication Year: 2024
Collection: LCC:Biology (General)
Subject Terms: Biology (General), QH301-705.5
More Details: Abstract The Drosophila model is pivotal in deciphering the pathophysiological underpinnings of various human ailments, notably aging and cardiovascular diseases. Cutting-edge imaging techniques and physiology yield vast high-resolution videos, demanding advanced analysis methods. Our platform leverages deep learning to segment optical microscopy images of Drosophila hearts, enabling the quantification of cardiac parameters in aging and dilated cardiomyopathy (DCM). Validation using experimental datasets confirms the efficacy of our aging model. We employ two innovative approaches deep-learning video classification and machine-learning based on cardiac parameters to predict fly aging, achieving accuracies of 83.3% (AUC 0.90) and 79.1%, (AUC 0.87) respectively. Moreover, we extend our deep-learning methodology to assess cardiac dysfunction associated with the knock-down of oxoglutarate dehydrogenase (OGDH), revealing its potential in studying DCM. This versatile approach promises accelerated cardiac assays for modeling various human diseases in Drosophila and holds promise for application in animal and human cardiac physiology under diverse conditions.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2399-3642
Relation: https://doaj.org/toc/2399-3642
DOI: 10.1038/s42003-024-06371-7
Access URL: https://doaj.org/article/9a2735648bfc4162997dade9bf85d443
Accession Number: edsdoj.9a2735648bfc4162997dade9bf85d443
Database: Directory of Open Access Journals
More Details
ISSN:23993642
DOI:10.1038/s42003-024-06371-7
Published in:Communications Biology
Language:English