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

Our platform uses deep learning to segment optical microscopy images of Drosophila hearts, facilitating the quantification of cardiac parameters for aging and dilated cardiomyopathy. Moreover, using a machine learning approach we able to predict cardiac aging with high accuracy.

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Revolutionizing Drosophila Heart Analysis with Deep Learning. In the world of biological research, the Drosophila melanogaster, commonly known as the fruit fly, is a powerful model organism. Its heart function and diseases are particularly intriguing for scientists. However, analyzing heart videos manually is time-consuming and prone to human error. Our team presents innovative methods that leverage machine learning to automate the analysis of Drosophila cardiac videos, significantly enhancing accuracy and efficiency. Our innovative approach discovered that deep segmentation models are remarkably effective in identifying and tracking the heart walls of Drosophila across video frames. These models can accurately recover vital contractile and temporal dynamics in heart-beating patterns, which are crucial for analyzing cardiac health. This method stands out because it reduces the need for human intervention. By automating the process, we minimize errors in marking heart edges during contraction and relaxation, which are essential for measuring key cardiac parameters such as diastolic and systolic diameters and fractional shortening.

Comprehensive Cardiac Physiology and Real-Time Morphological Analysis. Our platform is the first to apply deep learning-assisted segmentation to high-resolution optical microscopy of Drosophila hearts while quantifying all relevant cardiac parameters including statistics. These include diastolic and systolic diameters/intervals, fractional shortening, ejection fraction, heart rate, and heartbeat arrhythmicity. Remarkably, this model works efficiently even on consumer-grade hardware, making it accessible to a wider range of researchers. This advancement means that researchers can achieve high-fidelity, reproducible results more quickly and reliably than ever before. Moreover, using a 2D deep segmentation model, we generate accurate heart-wall segmentations for each video frame, enabling real-time morphological and cardiac statistical analysis. This detailed per-frame analysis allows us to observe changes in heart function due to aging or disease. For instance, our model identified significant reductions in cardiac function and increased dysrhythmia in aging flies. Such detailed analysis was not possible with conventional software, opening new avenues for studying Drosophila cardiac morphologies in high-resolution optical microscopy.

Pioneering Deep Learning for Insights into Cardiac Aging and Disease. Our model also, provides a more comprehensive understanding of heart function by analyzing contractility and temporal dynamics on a beat-by-beat basis. Our research has revealed significant insights into cardiac aging and disease in Drosophila. By examining morphological parameters and contractility on a beat-level basis, we observed that aging flies exhibit reduced stroke volume and cardiac output but little change in temporal beating characteristics. Additionally, we detected increased diastolic intervals during bradycardiac events in aged flies. These findings highlight the potential of our model to uncover subtle physiological changes that are otherwise difficult to detect.

Predicting Fly Age and Cardiac Health. Using experimentally calculated cardiac statistics; our deep-learning models can predict the age of flies with remarkable accuracy. This ability to classify age based on cardiac function indicates that our model captures physiologically significant patterns. Furthermore, we demonstrated that raw video data could be used to predict age, underscoring the model's robustness in capturing morphological and rhythmic patterns in cardiac videos. This capability has profound implications for detecting phenotypes that mimic or delay aging in Drosophila hearts.

Linking OGDH to Dilated Cardiomyopathy. We successfully applied our deep learning methodology to model dilated cardiomyopathy (DCM) in Drosophila. By knocking down the cardiac-specific expression of OGDH (oxoglutarate dehydrogenase), an enzyme crucial for ATP production, we created a DCM model. This disruption mimics energy production deficits seen in human DCM, providing a valuable model for studying the disease. Our findings align with human studies linking OGDH deficiencies to cardiac and neurodegenerative diseases, making this model highly relevant for further research.

Conclusion and Future Directions. Our deep learning approach to analyzing Drosophila cardiac videos marks a significant advancement in the field of cardiac research. By automating the process and providing detailed cardiac statistics, we pave the way for more accurate, efficient, and comprehensive studies of heart function in Drosophila. This method holds tremendous potential not only for understanding aging and disease in fruit flies but also for translating these insights into human cardiovascular research. We believe our models will revolutionize laboratory analysis and drive next-generation studies in Drosophila cardiology and beyond.

The future applications of our methods are vast. We envision using deep learning-assisted studies to explore cardiac mutation models and other small animal models, such as zebrafish and mice. Additionally, our techniques could be adapted for human heart models, providing valuable insights into cardiac health and disease. Incorporating uncertainty quantification methods could further enhance the reliability of our analyses.

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Optical Biological Imaging
Physical Sciences > Physics and Astronomy > Biophysics > Bioanalysis and Bioimaging > Biological Imaging > Optical Biological Imaging
Cardiovascular Physiology
Life Sciences > Biological Sciences > Physiology > Cardiovascular Physiology
Computer Imaging, Vision, Pattern Recognition and Graphics
Mathematics and Computing > Computer Science > Computer Imaging, Vision, Pattern Recognition and Graphics
Machine Learning
Mathematics and Computing > Computer Science > Artificial Intelligence > Machine Learning
Data Integration
Life Sciences > Biological Sciences > Biological Techniques > Computational and Systems Biology > Data Integration

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