AI Advances Heart Research

Columbia University, New York, NY

Understanding heart function and disease, as well as testing new drugs for heart conditions, has long been a complex and time-consuming task. A promising way to study disease and test new drugs is to use cellular and engineered tissue models in a dish, but existing methods to study heart cell contraction and calcium handling require a good deal of manual work, are prone to errors, and need expensive specialized equipment.

Researchers at Columbia Engineering unveiled a groundbreaking new tool today that addresses these challenges head-on. BeatProfiler is a software that automates the analysis of heart cell function from video data and is the first system to integrate the analysis of different heart function indicators, such as contractility, calcium handling, and force output, into one tool — speeding up the process significantly and reducing the chance for errors. BeatProfiler enabled the researchers to not only distinguish between different diseases and levels of their severity but also to rapidly and objectively test drugs that affect heart function.

“This is truly a transformative tool,” said Professor and Project Leader Gordana Vunjak-Novakovic. “It’s fast, comprehensive, automated, and compatible with a broad range of computer platforms so it is easily accessible to investigators and clinicians.”

The team elected not to file a patent application, and instead is offering the AI software as open source, so it can be directly used — for free — by any lab. They believe that this is important for disseminating the results of their research, as well as for getting feedback from users in academic, clinical, and commercial labs that can help the team to further refine the software.

As the lab was making more and more cardiac tissues through innovations such as milliPillar and multi-organ tissue models, the increased capabilities of the tissues required the researchers to develop a method to more rapidly quantify the function of cardiomyocytes (heart muscle cells) and tissues to enable studies exploring genetic cardiomyopathies, cosmic radiation, immune-mediated inflammation, and drug discovery.

In the last year and a half, Lead Author Youngbin Kim and his co-authors developed a graphical user interface on top of the code so that biomedical researchers with no coding expertise could easily analyze the data with just a few clicks. This brought together experts in software development, machine learning, signal processing, engineering, and user experience by lab members.

The study showed that BeatProfiler could accurately analyze cardiomyocyte function, outperforming existing tools by being faster — up to 50 times in some cases — and more reliable. It detected subtle changes in engineered heat tissue force response that other tools might miss.

“This level of analysis speed and versatility is unprecedented in cardiac research,” said Kim. “Using machine learning, the functional measurements analyzed by BeatProfiler helped us to distinguish between diseased and healthy heart cells with high accuracy and even to classify different cardiac drugs based on how they affect the heart.”

The team is working to expand Beat-Profiler’s capabilities for new applications in heart research, including a full spectrum of diseases that affect the pumping of the heart, and drug development. To ensure that BeatProfiler can be applied to a wide variety of research questions, they are testing and validating its performance across additional in vitro cardiac models, including different engineered heart tissue models. They are also refining their machine-learning algorithm to extend and generalize its use to a variety of heart diseases and drug effect classification.

The long-term goal is to adapt BeatProfiler to pharmaceutical settings to speed up the testing of hundreds of thousands of candidate drugs at once.

For more information, contact Holly Evarts at This email address is being protected from spambots. You need JavaScript enabled to view it.; 212-854-3206.

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