How Artificial Intelligence is Changing Echocardiography
Does it not seem like an episode of Star Trek when you sit before a computer, and a small device captures the essence of what is normal or abnormal in your heart? To top it, it even predicts your condition. Echocardiograms with the use of AI have become smarter, faster, and more accurate.
AI is helping doctors diagnose heart diseases more reliably, spotting abnormalities, and even predicting a higher risk of complications for patients. Artificial intelligence (AI) has revolutionized our understanding of the heart.
In this article, we examine the impact of AI on cardiology, the ethical concerns it raises, and what the future may hold for this field.
Artificial Intelligence (AI) has been recently incorporated into heart ultrasound (echocardiography). It has benefits, but it also has significant challenges. Everything depends on the data they’re trained on; hence, AI models can behave differently. These tools are used widely in real-life medical settings. We must consider:
1. Can we trust the AI's decisions? Is it fair to all patients?
2. Is personal health data kept safe and confidential?
3. Do we know how the AI is making decisions?
4. Are there clear rules on how AI is developed and used?
How AI Learns to Detect Heart Problems
Initially, we must understand how artificial intelligence and machine learning work and how they transform our understanding of the heart. AI and machine learning enable computers to learn from experience, much like how we humans do. They follow the same pattern in learning that we humans do, viz.
- Supervised learning involves training the computer with multiple images labeled as either healthy or diseased hearts, just as we are taught.
- Unsupervised learning. The computer similarly learns on its own to identify patterns without explicit instructions, as we do when we explore on our own.
- Semi-supervised is a mix of both, being guided by a small set of labeled data and a large set of unlabeled data.
- Reinforced Learning: It uses trial and error to train itself.
Over time, machine learning has incorporated multiple layers of advanced and sophisticated artificial neural networks (ANNs). They function like the human brain. They communicate with each other and are interconnected like neurons in the body, processing information, images, sounds, and videos.
Deep learning (DL) is more capable. Think of it as the Einstein of AI. ML, the normal man can recognise normal patterns, but DL detects more complex and subtle features. DL in heart imaging identifies minute changes, such as early valve irregularities or wall motion abnormalities, that can be easily overlooked. It helps in faster, more precise, and earlier diagnosis. And this happens as deep neural networks are structured like layers of the brain interconnected by neurons.
Generative AI’s role in improving heart imaging?
AI generates texts, images, and sounds based on what it learns, as in ChatGPT. Echocardiography is a valuable application that uses Generative AI in medical imaging (heart ultrasounds).
GAN, or Generative Adversarial Network, is a generative AI.GAN is comprised of two smart systems that work like a creative team:
- One system creates synthetic images that look real.
- The other system detects whether it is real or fake.
They improve by challenging each other, so over time, the fake images become increasingly accurate, and it becomes almost impossible to distinguish the real one from the fake.
GAN and gains in Echocardiography
In heart imaging:
- GAN creates new heart ultrasound images as the available data is limited.
- These images train other AI models.
- They improve the clarity and quality, and help to detect heart problems.
Echocardiograms, ECGs, and MRI scans of the heart have become more advanced than ever due to the progress in AI technology, resulting in faster diagnoses, more effective treatment plans, and more personalized care.
AI’s Impact: From Data to Diagnosis
AI paves the way to bridge the gap between data science and patient-related outcomes in the following ways.
1. How AI Helps Capture Better Heart Images?
A heart scan is much easier today due to the availability of portable machines; however, the quality of these images remains a challenge. AI-powered tools help to take clear images in real-time.
AI ensures that the images captured by even new users are clear enough for doctors to make informed decisions. In emergency rooms, ICUs, and other critical care areas, the FDA permits the use of AI tools to take heart images when time or experts are unavailable.
AI helps non-doctors screen for conditions like rheumatic heart disease in remote areas. With AI guidance, they capture images to diagnose serious valve problems, especially issues with the mitral valve.
The use of AI
- Saves time and improves efficiency.
- Reduces the need for an expert.
- Expands access to heart care in remote or resource-limited areas.
- Improves diagnosis and patient care.
2. AI in action: Measuring heart structure and function
After the images are captured, the size of the chambers, the thickness of the heart walls, and the ejection fraction (which measures the heart's pumping efficiency) need to be measured. Using AI removes human errors and variations in analytical skills.
How AI Improves Heart Measurements
- AI measures heart structure, chamber sizes, and wall thickness with great accuracy, outperforming traditional methods.
- It tracks how well the heart functions, measures the amount of blood it pumps out with every beat (called ejection fraction), and assesses the stiffness or relaxation of the heart muscle during each beat.
AI makes measuring
- Faster and reliable, helping in quick diagnosis.
- Detects early signs of heart disease, even before symptoms start.
- Personalizes monitoring, treatment planning, and long-term care.
3. AI Finds Hidden Patterns in Heart Disease
AI groups patients based on their health data and heart scans, even though their symptoms, risks, and treatment needs are different. The process is called phenogrouping—sorting patients into subgroups (or “phenogroups”) based on patterns in how their disease looks and behaves.
Diagnosing Heart Failure
- AI models categorize patients who develop heart failure and recover, regaining normal heart function (called HFrecEF), versus those who don’t (called HFrEF).
- AI models analyze ultrasound measurements (such as heart pumping strength, heart size, and wall thickness). Accurately predicting which patients will recover enables doctors to predict outcomes better and tailor treatment to the specific type of heart failure.
Improving diagnosis in Valve Disease
AI has also been used to diagnose valve problems, such as aortic stenosis (AS) and mitral regurgitation (MR):
- AS is challenging to diagnose because it appears mild on scans but is serious (known as discordant AS).
- High-risk patient groups are identified by combining scan data with CT and MRI results, revealing that these high-risk patients exhibit more pronounced heart muscle thickening, scarring, and valve damage.
- These AI-detected groups need valve replacement surgery sooner and have higher mortality if left untreated.
In mitral regurgitation, AI categorizes patients into different severity levels.
Classifying Subtypes in Dilated Heart Disease
Dilated cardiomyopathy (DCM) occurs when the heart becomes enlarged and weakened, resulting in a decrease in its pumping ability. It is a complex disease that progresses differently in each patient.
AI helps us understand how diseases progress, respond to treatment, and predict their long-term outcomes, improving survival and quality of life through personalized care.
Using data, AI groups the patients with the disease into two subgroups:
- DCM-low: patients who have mild symptoms and better heart function.
- DCM-high: have patients with severe damage and poor outcomes.
AI predicts which patients need aggressive treatment and closer follow-up.
4. How AI Helps Doctors Predict Patient Outcomes
AI tools flag high-risk patients early. Prognostication( prophecising) using Artificial Intelligence (AI) helps doctors predict a patient’s risk of death over the next few years with an accuracy rate of 92% using images alone.
The PLAX view (parasternal long axis) results predict overall mortality. AI models often use it to assess who might be at higher risk and need closer follow-up or treatment. These predictions are often as good—or even better—than traditional scoring systems used today. With AI assistance, the ability to correctly identify high-risk patients improves by 13%.
5. How AI Can Help Doctors Make Better Decisions
AI tools, known as Clinical Decision Support Systems (CDSS), help doctors make smarter, faster, and more accurate decisions. These systems combine a patient’s personal health information (like scan results, symptoms, and medical history) with AI technology to offer evidence-based recommendations for:
- Diagnosing conditions
- Assessing risks
- Planning treatments
Compared with traditional scoring tools (such as MAGGIC, GRACE, and TIMI scores), AI predicts the risk of dying during a hospital stay with an accuracy of 91% to 96%. The AI focused on:
- How well the heart was pumping
- The size and shape of the heart chambers
- Valve problems
AI Can’t Replace Human Judgment
Researchers caution that just identifying high-risk patients doesn’t automatically save lives. If doctors only focus on high-risk patients without changing how care is delivered, outcomes may not improve.
Challenges in using AI
1. The Black Box Problem in AI?
Artificial intelligence (AI) and machine learning (ML) are powerful tools that help us.
- Understanding heart conditions
- Predicting risks
- Informing patients about treatment decisions.
The biggest challenge is that the decision-making process of AI isn’t clear and is often called the “black box” problem— AI gives you a result, but you don’t know how it got there.
2. Challenges in Making AI Work
1. AI Needs High-Quality Data
AI is trained on thousands (or millions) of clear heart images. If the image data is poor or incomplete, the AI might:
- Miss a diagnosis
- Make the wrong prediction
- Suggest the wrong treatment
2. Data Sharing Between Hospitals Is Needed
Hospitals and researchers must collaborate to obtain the large quantities of data. The sharing of data raises concerns about privacy, fairness, and consistency.
3. Not Enough Skilled Experts
We need more data scientists and AI experts who understand both healthcare and big data systems to build and manage models that effectively leverage these systems.
4. Risks with Generative AI
Newer tools, such as GANs (which can generate new images), are helpful; however, if they are trained on faulty data, they may produce fake or inaccurate images, leading to serious errors in diagnosis or treatment.
Promising but not perfect
For perfection, AI needs :
- Clear, reliable, and explanatory AI tools
- Bigger and better datasets
- Stronger clinical trials
- Better collaboration and transparency
Until then, AI remains a powerful assistant; doctors must be cautious when fully relying on AI for life-saving decisions.
Legal and Ethical Concerns
AI and machine learning (ML) tools are becoming more common in heart ultrasound (echocardiography), which also raises important ethical and legal concerns, including data privacy, responsibility, and transparency.
1. Data Privacy
When hospitals and researchers share data for training AI, personal patient information is often leaked. Keeping health records confidential is important for data protection.
2. Quality of data
Incorrect, incomplete, or biased data is a cause of
- Wrong diagnosis
- Unsafe treatments
- Legal issues (if a patient is harmed due to AI-based decisions)
The AI tool must be tested thoroughly before being used in patient care.
3. The “Black Box” Problem
The black box problem is a serious issue if patients do not trust AI because
- Patients need to understand their diagnosis and treatment
- Doctors also have to explain and justify decisions
4. Explainable AI (XAI)
Explainable AI (XAI) is a type of AI being developed to explain how and why AI systems reach their decisions. It will help to
- Build trust with patients and doctors
- Reveal hidden bias
- Ensure that AI is acting fairly and transparently
5. Everyone Has a Role to Play
As more AI-powered medical devices, such as those used in ultrasound and heart scans, are being approved by authorities like the FDA. Doctors, developers, data scientists, hospital administrators, and policymakers are responsible for ensuring AI tools are safe, ethical, and trustworthy.
Tomorrow’s Cardiology: The Road Ahead
AI aims for its next wave of innovation in personalized and predictive care after smarter, faster, and more accurate echocardiograms.
1. Digital Twins: A Virtual Copy of Your Heart
Imagine having a heart that has a digital twin, a version that mirrors how your real heart functions, responds to medication, or reacts to stress. In the future, digital twins will play a role in personalized cardiology.
Using the digital twin, it will be easy to
- Update using your medical records, lab tests, wearable devices, and even environmental factors.
- Simulate how your heart will behave in the future, say when you have a heart attack.
- Help doctors test treatments virtually before trying them on your real body.
2. AutoML: Making AI Easier for Everyone
AutoML (Automated Machine Learning) will make it easier, eliminating the need for skilled data scientists and large teams to code. It will increase reach to smaller clinics and hospitals by placing intelligent tools directly in the hands of healthcare workers everywhere.
3. Smarter Systems, Human Supervision
While challenges persist, humans and machines continue to collaborate. It holds potential for transforming heart care. AI is a sharper, faster assistant to those in the field of heart care.
AI tools will help to detect problems early, reduce errors, and tailor treatment. Artificial Intelligence in echocardiography is no longer the future—it’s the now.
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