The field of cardiology has adopted artificial intelligence into every subspecialty, and at the European Society of Cardiology’s 2022 Congress, cardiologists from around the globe have the opportunity to learn more about what's on the horizon for artificial intelligence in cardiology. What are some of the latest developments?
The field of cardiology has adopted artificial intelligence (AI) into every subspecialty, from imaging to electrophysiology. And at the European Society of Cardiology’s 2022 Congress, cardiologists from around the globe have the opportunity to learn more about what's on the horizon for artificial intelligence in cardiology. What are some of the latest developments?
AI has begun to play a significant role for this particular type of cardiology where clinicians use noninvasive techniques to measure myocardial blood flow, as well as the pumping function of the heart. Here are a few ways that has been possible:
- AI has been applied to image processing, which has allowed the ability to perform completely automatic single photon emission computed tomography (SPECT), myocardial perfusion, imaging motion correction, and more
- These tools have improved the prediction of the treatment and prognosis of several cardiovascular conditions by allowing for the development of a machine learning algorithm to predict revascularization events using imaging variables and clinical parameters
- AI driven algorithm has been incorporated into an FDA-approved nuclear imaging software tool, which has helped clinicians improve structured reporting and development of clinical decision support tools
What are the primary areas AI can improve in electrophysiology in data management, data interpretation, and real time integration of data? But one of the biggest roadblocks to this integration is the dilemma clinicians face when it comes to how to integrate ambulatory obtained EKG into clinical practice.
Several AI tools, such as smartphones or smartwatch enabled ECG devices, have allowed for more cost-effective screening. And while these tools continue to evolve and develop, conditions still face the risk of false-positive or false-negative results.
However, emerging data suggests that techniques using AI may improve the interpretation of ECGs and help facilitate triaging to those who need to see either a physician, a cardiologist, or an electrophysiologist.
Another opportunity for AI in this field is to improve the integration of multiple different complementary but separately obtained data, to help with facilitating correct interpretation and optimize therapy for patients. For example, for patients undergoing evaluation for cardiac ablation, the combination of preoperative imaging and intra operative imaging are used to optimize treatment. However, integrating this data can be difficult.
Luckily, recent data suggests that AI techniques can help facilitate data integration across several modalities, which could help the cardiologist efficiently and effectively identify and target relevant sites responsible for their patient’s condition.
From Research to Clinical Practice
Let's look beyond nuclear cardiology into clinical practice. There are several ways clinicians are combining AI with clinical practice to improve care. A few examples are:
- Patients come into the hospital with the stroke called an intracerebral hemorrhage and get a CT scan. The AI that makes that scan possible, cuts the time to diagnosis, and helps prevent brain damage
- Applying AI to ECGs can be used to detect the presence of a weak heart pump which, left untreated, could lead to heart failure
- AI-guided ECGs can also detect faulty heart rhythms, or atrial fibrillation, before patient experience symptoms
In clinical practice, effectively incorporating AI also means finding ways to incorporate it into several medical and surgical specialties. The rapidly developing field of AI has a role in not only cardiovascular medicine but also neurology, oncology, and radiology. And given that cardiology is often a field that requires multidisciplinary perspectives, incorporating AI in all related fields of medicine can improve multidisciplinary care within the field of cardiology.
Emerging data and AI tools can help cardiologists find more efficient ways to diagnose and treat complex cardiovascular conditions and diseases, and can help them improve quality of life for their patients.