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. Significance Current analysis of Drosophila cardiac recordings is capable of limited cardiac physiological parameters and are error-prone and time-consuming. We present the first deep-learning pipeline for high-fidelity automatic modeling of Drosophila contractile dynamics. We present methods for automatically calculating all relevant parameters for diagnosing cardiac performance in aging model. Using the machine and deep learning age-classification approach, we can predict aging hearts with an accuracy of 83.3% (AUC 0.90) and 77.1% (AUC 0.85), respectively.