A breakthrough discovery from the University of Alabama at Birmingham is revolutionizing the assessment of heart aging and disease utilizing fruit flies. This innovative approach, led by Associate Professor Girish Melkani, employs deep machine learning technology to streamline the evaluation of cardiac health in these tiny insects, paving the way for groundbreaking insights into human heart diseases.
Fruit flies have long been a valuable model for cardiovascular research due to their genetic similarity to humans. Traditionally, measuring heart function in these flies required manual intervention and was a time-consuming process. However, the UAB team’s method, using deep learning and high-speed video microscopy, eliminates the need for human input, dramatically speeding up the process and reducing errors.
By automating the measurement of heart functions like expansion and contraction, researchers can now conduct extensive studies on how genetic and environmental factors impact heart aging and pathology. This approach not only benefits fruit fly models but holds promise for applications in zebrafish, mice, and potentially even human heart models, providing valuable insights into cardiac health and disease.
The team tested their automated model on aging hearts and a fruit fly model of dilated cardiomyopathy, showcasing its ability to accurately predict cardiac aging trends. The code generated by the researchers can compute essential cardiac statistics, such as diastolic and systolic diameters, fractional shortening, ejection fraction, heart rate, and heartbeat arrhythmicity.
This groundbreaking technology is a significant step forward in heart research, offering a more accurate, efficient, and comprehensive way to study heart function not only in fruit flies but potentially in other models and even in human cardiovascular research. The study, published in Communications Biology, highlights the transformative potential of deep learning in advancing our understanding of heart health and disease.
