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

Autor: Yash Melkani, Aniket Pant, Yiming Guo, Girish C. Melkani
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Communications Biology, Vol 7, Iss 1, Pp 1-11 (2024)
Druh dokumentu: article
ISSN: 2399-3642
DOI: 10.1038/s42003-024-06371-7
Popis: 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.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje