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

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.

Automated evaluation of cardiac contractile dynamics and aging prediction using machine learning in a Drosophila model.

The Drosophila model has proven tremendously powerful for understanding pathophysiological bases of several human disorders including aging and cardiovascular disease. Relevant high-speed imaging and high-throughput lab assays generate large volumes of high-resolution videos, necessitating next-generation methods for rapid analysis. We present a platform for deep learning-assisted segmentation applied to optical microscopy of Drosophila hearts and the first to quantify cardiac physiological parameters during aging. An experimental test dataset is used to validate a Drosophila aging model. We then use two novel methods to predict fly aging: deep-learning video classification and machine-learning classification via cardiac parameters. Both models suggest excellent performance, with an accuracy of 83.3% (AUC 0.90) and 77.1% (AUC 0.85), respectively. Furthermore, we report beat-level dynamics for predicting the prevalence of cardiac arrhythmia. The presented approaches can expedite future cardiac assays for modeling human diseases in Drosophila and can be extended to numerous animal/human cardiac assays under multiple conditions.