Artificial Intelligence in the Cardiac Catheterization Lab: Transforming Care and Optimizing Workflows Post

By  Campion Quinn, MD

Introduction

The cardiac catheterization lab (cath lab) has long been a cornerstone of cardiovascular care, allowing for diagnosing and treating conditions like coronary artery disease, structural heart defects, and arrhythmias. Over the past two decades, imaging, robotics, and data analysis advancements have reshaped interventional cardiology. However, artificial intelligence (AI) aims to bring a fundamental transformation—enhancing real-time decision-making, improving procedural precision, and optimizing workflows.

Despite its promise, AI remains a mystifying concept for many physicians unfamiliar with its mechanics. This essay aims to clarify AI’s role in the cath lab, illustrating how it enhances diagnostics, guides interventions, and streamlines operations—all while improving patient safety and outcomes.

AI in Imaging and Diagnostics: Seeing Beyond the Human Eye

Enhancing Coronary Angiography and Stent Placement

Coronary angiography, the primary imaging modality in cath labs, has benefited tremendously from AI-powered image processing. Traditional angiography provides a two-dimensional view of coronary arteries. Still, AI can reconstruct three-dimensional (3D) and even four-dimensional (4D) models, offering a dynamic, real-time roadmap of a patient’s coronary circulation.

One example is AI-assisted quantitative coronary angiography (QCA), which automates the measurement of coronary artery stenosis. Instead of relying on a physician’s visual estimation, AI can more precisely analyze vessel diameter, lesion length, and plaque burden. This reduces subjectivity and ensures that stents are optimally sized and placed, decreasing the risk of restenosis and thrombosis​.

Fractional Flow Reserve (FFR) Without a Wire

Traditionally, fractional flow reserve (FFR) requires the cardiologist to pass an invasive pressure wire across the lesion to assess whether a coronary blockage is functionally significant. AI has revolutionized this by enabling angiography-derived FFR (AI-FFR), which analyzes blood flow and vessel pressure gradients from standard angiograms. Companies like HeartFlow and CathWorks have pioneered this approach, allowing faster and less invasive decision-making​.

AI-Guided Intravascular Imaging

Beyond angiography, cath labs employ advanced imaging modalities like intravascular ultrasound (IVUS) and optical coherence tomography (OCT). AI algorithms can now automatically segment and classify plaque types, detect unstable lesions, and assist in determining whether a stent should be placed. These tools help cardiologists avoid unnecessary stenting and reduce the risk of complications​.

AI in Procedural Assistance: Precision and Automation

Robotic-Assisted Interventions: A New Era of Precision

Robotic-assisted PCI (percutaneous coronary intervention) is one of the most exciting AI-driven advancements. For example, the Corindus CorPath GRX system enables interventional cardiologists to remotely control guidewires, balloons, and stents with millimeter-level accuracy. AI-powered robotic guidance ensures the following:

• Consistent wire navigation reduces the risk of perforation.

• More precise lesion targeting improves stent placement.

• Lower radiation exposure for physicians, as they can operate from a shielded workstation​.

A groundbreaking demonstration of AI’s potential was the first-ever remote PCI, performed 35 kilometers away in India using the Corindus system. This event suggests that in the future, AI-guided robotic interventions could allow specialists to perform procedures on patients in remote locations, addressing geographic disparities in cardiovascular care​.

AI-Guided Electrophysiology and Arrhythmia Mapping

In electrophysiology, AI is being used to map complex arrhythmias with unprecedented speed and accuracy. AI-enhanced algorithms can analyze millions of heartbeats, detecting atrial fibrillation or ventricular tachycardia patterns that may not be immediately apparent to the human eye. This has significantly improved success rates for catheter ablation procedures​.

AI in Workflow Optimization: Reducing Fatigue, Enhancing Efficiency

Automated Case Prioritization and Scheduling

AI-driven predictive analytics can analyze a hospital’s patient database and flag high-risk cases, helping administrators prioritize urgent interventions. This ensures that patients at the most significant risk of adverse events receive care sooner, improving efficiency and patient outcomes​.

Radiation Dose Optimization

Radiation exposure is a constant concern in the cath lab. AI now helps minimize fluoroscopy time by:

• Adjusting image quality in real time to reduce unnecessary radiation exposure.

• Eye-tracking technology, which directs X-ray beams only where the operator is looking.

• AI-driven collimation, which narrows the X-ray field to focus solely on the area of interest​.

A study on AI-assisted radiation reduction found that it decreased patient and operator radiation exposure by up to 50% without compromising image quality​.

Automated Report Generation

Natural language processing (NLP) is another AI application that streamlines cath lab workflows. AI-powered speech recognition allows physicians to dictate procedural notes that are automatically structured into patient reports. This reduces administrative burden, freeing up more time for direct patient care​.

Improving Patient Outcomes: AI’s Role in Risk Prediction and Long-Term Care

Beyond the cath lab, AI plays a crucial role in predicting long-term outcomes and personalizing post-procedural care. AI-driven algorithms can analyze patient data to:

• Predict which patients are at higher risk for restenosis after PCI, guiding follow-up strategies.

• Recommend personalized medication regimens based on genetic and physiological factors.

• Identify patterns in readmissions, helping hospitals reduce unnecessary returns to the ER​.

One real-world application is the IBM Watson AI, which integrates electronic health records, imaging studies, and clinical guidelines to suggest personalized post-PCI treatment plans​.

Challenges and Considerations in AI Adoption

While AI offers numerous benefits, its implementation is not without challenges:

• Data Integration Issues: Many AI models require large, standardized datasets, but hospitals often use different electronic health record (EHR) systems, creating compatibility issues.

• Physician Trust and “Black Box” Concerns: AI algorithms make complex decisions, but their reasoning is not always transparent. Physicians need clear explanations of AI-generated recommendations before widespread adoption is feasible.

• Regulatory and Ethical Considerations: The FDA and European regulators are still developing guidelines for AI-driven interventional cardiology tools​.

Actionable Takeaways for Physicians Interested in AI

1. Familiarize yourself with AI-Enhanced Imaging – AI-assisted FFR and IVUS/OCT tools can improve diagnostic precision.

2. Explore Robotic-Assisted PCI – AI-powered robotics can improve precision, safety, and procedural efficiency.

3. Leverage AI for Workflow Efficiency – Consider using AI for automated reporting, scheduling, and radiation dose reduction.

4. Advocate for AI Integration in Your Institution – Engage with hospital leadership to ensure seamless AI integration into existing systems.

Conclusion

AI is no longer a futuristic concept—it is actively reshaping the cardiac catheterization lab. From real-time imaging enhancement to robotic-assisted interventions and workflow automation, AI improves precision, efficiency, and patient safety. While challenges remain, integrating AI in interventional cardiology promises to reduce procedural risks, optimize decision-making, and extend life-saving care to more patients than ever before.

The future of the cath lab is intelligent, data-driven, and AI-powered—and for physicians willing to embrace these innovations, the possibilities are limitless.

References

1. Beyar R, Davies JE, Cook C, et al. Robotics, imaging, and artificial intelligence in the catheterization laboratory. EuroIntervention. 2021;17(5):537-549. doi:10.4244/EIJ-D-21-00145​

2. Sardar P, Abbott JD, Kundu A, et al. Impact of artificial intelligence on interventional cardiology: from decision-making aid to advanced interventional procedure assistance. JACC Cardiovasc Interv. 2019;12(14):1293-1303. doi:10.1016/j.jcin.2019.04.048​