Decoding the Heart's Secrets: Applications of AI in Cardiovascular Imaging

Illustration: Lee Sauer

Artificial intelligence (AI) has become an integral part of our daily lives, influencing various fields including cardiovascular medicine. AI has demonstrated exceptional potential to revolutionize the field of diagnostic imaging. The remarkable design of computer systems equipped with the human-like capacity to perform repetitive tasks without fatigue has positioned AI as a transformative force in cardiovascular imaging. Through meticulous training of advanced algorithms, these digital marvels analyze medical images with unparalleled precision, providing healthcare professionals with the means to swiftly diagnose and effectively treat heart conditions. This blog post is intended to demystify the ascent of AI in diagnostic cardiovascular imaging, shedding light on its remarkable trajectory.

Unlocking the Enigma: Exploring the World of AI

Before diving into AI's role in cardiovascular imaging, let's take a step back to understand what AI is and how it works. 

I recently had the opportunity to participate in a thought-provoking webinar that encouraged attendees to reflect on their initial impressions of AI. The discussion centered around the first word that comes to mind when thinking about AI. The top two responses were "robot" and "scary." These findings didn't come as a surprise considering the prevalence of futuristic movies that have permeated our screens and become deeply ingrained in our culture. Such portrayals of new technology can understandably evoke a sense of unease, particularly when they delve into automation that rivals human intelligence. Consequently, we find ourselves in a captivating and somewhat unsettling space filled with thought-provoking questions. The breadth and complexity of AI can evoke fear, fascination and a wide range of emotional responses.

What is AI?

AI is a fascinating technology that helps computers perform tasks that usually require human intelligence. It allows machines to learn, reason and make decisions like we do. AI is used in many areas of our lives, from virtual assistants like Siri and Alexa to self-driving cars to healthcare technology.

AI employs algorithmic instructions that tell a computer what to do. These algorithms are designed to learn from the data they receive and make predictions or decisions based on that information. In other words, AI can learn from experience and improve its performance over time.

What is Machine Learning?

One use of AI is image recognition. Computers can be trained to recognize objects or patterns in images through a process called machine learning. Machine learning is like teaching a computer to learn from experience, get better at solving specific problems and make informed decisions on its own, just like humans do. In machine learning, we have a computer program or algorithm and we give it lots of examples (called “training data”) of what we want it to learn.

The algorithm looks at these examples and tries to find patterns and rules on its own. It tries to identify which characteristics of the training data are important for completion of a given task. It uses the data to adjust its internal settings and make better predictions or decisions based on feedback.

What is Deep Learning?

Deep learning is a type of artificial intelligence inspired by the human brain. It helps computers understand and learn from complex patterns and information. Deep learning works by breaking down a big picture or concept into its composite parts. It uses artificial neural networks that are made up of many tiny, interconnected parts, like brain cells (neurons). Each neuron takes in some information, processes it and passes it to the next neuron.

In deep learning, these neural networks have many layers. The information goes through these layers one by one and each layer looks for different patterns and features. Once the deep learning model has learned from many examples, the model will take in new data, go through its layers, analyze the patterns and make a prediction like the human brain.

How Does AI Enhance Diagnostic Cardiovascular Imaging?

AI provides automated tools to enhance accuracy and efficiency in diagnostic cardiovascular imaging. Let's explore how AI achieves this:

1. Image Analysis: AI algorithms can analyze large volumes of medical images quickly and accurately. For example, AI can be used in echocardiography to analyze the heart's structure, function and blood flow patterns from ultrasound images. This helps clinicians reading the echocardiograms to detect abnormalities or signs of diseases that may be missed by the human eye.
2. Pattern Recognition: AI algorithms excel at recognizing patterns, which is a vital function in the diagnosis of cardiovascular diseases. By training AI models with thousands of images, they can learn to identify specific patterns associated with certain conditions. For example, by analyzing CT, MRI or angiography images, an AI algorithm can quickly identify blockages in blood vessels, which helps healthcare providers to make timely decisions regarding the next steps for medical therapy or intervention.
3. Decision Support: AI can provide doctors with valuable decision support tools. AI algorithms can evaluate a patient’s medical history, symptoms and imaging results to suggest potential diagnoses or treatment options. This assists healthcare professionals in providing context, highlighting nuances, allowing for more informed decision-making and ultimately improving patient outcomes.
4. Risk Prediction: AI can also assess a patient's risk of developing cardiovascular diseases based on various factors, such as age, gender, lifestyle and medical history. By analyzing these data points, AI algorithms can estimate the likelihood of a person experiencing a heart condition in the future. This information can help individuals take proactive measures to reduce their risk through lifestyle modifications or medical interventions.

What are the Challenges and Ethical Considerations When Using AI?

While AI brings immense potential to advance cardiovascular care, there are challenges and ethical considerations that must be addressed:

1. Data Privacy: AI algorithms rely on vast amounts of patient data for training. Ensuring the privacy and security of this sensitive information is necessary to protecting patient rights and maintaining confidentiality.
2. Bias and Fairness: AI algorithms are only as good as the original data. The AI models may produce inaccurate or discriminatory results if the training data is biased or unrepresentative. The development of unbiased and diverse training datasets is essential to ensure fairness and avoid disparities in diagnoses and treatment recommendations.
3. Human Oversight: Although AI algorithms can assist healthcare professionals, they should not replace human expertise and judgment. It is crucial to have human oversight to interpret AI-generated results and make final decisions. Think of this as "collaborative intelligence" where humans have context for how to use the tools.

How Can AI/Echocardiography be Used to Detect Aortic Stenosis?

A good example of how AI is being used in cardiac ultrasound is with Echo IQ. Its practicing medical team realized that AI could overcome the shortcomings of the traditional structural heart diagnostic process, particularly when automatically calculating the mortality risk associated with the whole-heart phenotype of various structural heart conditions. The team realized that they use AI to address the challenge of defining the risk of recommending intervention at a certain time for complex cases, such as those with low-flow, low-gradient reads. They built a neural network of the largest dataset of echocardiograms linked to mortality in the world.

Many publications and clinical trials later, the technology not only flags patients who meet guideline criteria in real time but also serves as a decision support tool by displaying mortality risk progression—the only technology in the world that can do so. The machine learning methodology was also developed uniquely, excluding key biases such as gender and race that skew real-world diagnoses. Its algorithms have been trained millions of times to provide consistently accurate results even when input measurements may reflect minor inaccuracies. From its user interface down to its underlying data, it demonstrates how a very straightforward product can answer extremely nuanced questions in a manner that simply would have been implausible without artificial intelligence.

Ultimately, AI helps cardiac care teams identify patients who need additional attention without creating additional work. A cardiologist using the technology said:

"Clinical work is sometimes a bit of a challenge. Every clinical day my secretary comes to me with a pile of referrals of new patients to see me and we try to work out where I can see them in my schedule. Whenever I am told that I have more patients to see, I let out a subconscious "groan" because of my clinical workload. The flip side of this is as follows: I do my job in order to help patients and save lives. To make my job really count, I need to see the people for whom I can make the biggest difference. In reality, these are the patients that, if left untreated, are likely to have a bad outcome. Because of my workload, I need these people to ‘bubble to the surface’ rather than relying on generic, unfiltered and often inappropriate referrals. The AI system automatically finds those very people at risk. Even if they don't end up having (or needing) a valve replacement, everyone is at risk and will benefit from seeing me and having a comprehensive review of their medical situation. This in my mind is the real value."

The Future of Healthcare: AI as a Partner, not a Replacement

While AI has made significant advancements in healthcare, it is unlikely to completely replace healthcare providers. AI is designed to assist and augment the capabilities of healthcare professionals rather than replace them. While AI algorithms can greatly enhance the accuracy and efficiency of diagnosis and treatment, healthcare providers possess essential skills like empathy, critical thinking and expertise that are necessary for patient care. They are responsible for interpreting AI-generated results, considering the broader clinical context, and making informed decisions based on their knowledge and experience. AI can be a valuable tool for healthcare providers but the human touch and expertise remain critical in delivering personalized and compassionate care to patients.

Conclusion

As technology continues to advance, AI is poised to assume a pivotal role in shaping the future of cardiovascular imaging and the broader field of medicine. It is vital to address key challenges associated with data privacy, bias and human oversight, ensuring that AI is used responsibly and ethically in the healthcare field. But by harnessing its potential in an ethical way, healthcare providers have the opportunity to enhance patient outcomes, elevate the precision of diagnoses and ultimately save lives. This fascinating expedition into the realm of AI in diagnostic cardiovascular imaging is merely the beginning, holding the promise of a brighter future for the healthcare industry.

Are you ready to learn how use AI to facilitate cardiovascular imaging diagnosis and improve outcomes?

• Join us for CV Transforum Fall'23 for a general session on "Advancing Cardiovascular Care: Harnessing the Power of AI Technology."
• Watch John S. Rumsfeld, MD, PhD, FACC and other experts review AI’s strengths and applications that, when partnered with clinical practice, can result in better care.