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Revolutionising Heart Research with AI and ML

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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. Still, existing methods to study heart cell contraction and calcium handling require a good deal of manual work, are prone to errors, and need expensive specialised equipment. There clearly is a critical medical need for a more efficient, accurate, and accessible way to study heart function using a methodology based on artificial intelligence (AI) and machine learning.

Researchers at Columbia Engineering unveiled a new tool today that addresses these challenges head-on. BeatProfiler is a comprehensive software that automates the analysis of heart cell function from video data and is the first system to integrate the study 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 project leader Gordana Vunjak-Novakovic, University Professor and the Mikati Foundation Professor of Biomedical Engineering, Medical Sciences, and Dental Medicine at Columbia. “It is fast, comprehensive, automated, and compatible with a broad range of computer platforms, so it is easily accessible to investigators and clinicians.”

The team, which included Barry Fine, assistant professor of medicine (in Cardiology) at Columbia University Irving Medical Centre, elected to refrain from filing a patent application. Instead, they are offering the AI software as open source so that it can be directly used by any lab. 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 refine the software further.

This project was driven, like much of Vunjak-Novakovic’s research, by a clinical need to diagnose heart diseases more quickly and accurately. This was a project that was several years in the making, and the team added different features piece by piece. While the overarching need was to develop a tool that could better capture the function of the cardiac models that the team was building to study cardiac diseases and assess the efficacy of potential therapeutics, the researchers had an urgent need to quickly and accurately assess the function of their cardiac models in real-time.

As the lab was making more and more cardiac tissues through innovations such as milliPillar and multiorgan 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 coauthors developed a graphical user interface (GUI) on top of the code so that biomedical researchers with no coding expertise could easily analyse 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 analyse cardiomyocyte function, outperforming existing tools by being faster and more reliable. It detected subtle changes in engineered heat tissue force response that other tools might miss.

Currently, the team is working on expanding BeatProfiler’s capabilities for new applications in heart research, including a full spectrum of diseases that affect the pumping of the heart, as well as 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 generalise its use to a variety of heart diseases and drug effect classifications. 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.

BeatProfiler represents a step forward in the study of heart function and disease, offering a more efficient, accurate, and accessible way to analyse heart cell function. By harnessing the power of AI and machine learning, BeatProfiler has the potential to accelerate research in this critical area and improve the lives of millions of people affected by heart conditions.


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