What Is the Role of AI in Interventional Radiology?

Author: Rasit Dinc

Introduction

Interventional Radiology (IR) and Artificial Intelligence (AI) are two fields at the forefront of technological innovation in medicine. While AI has already made significant inroads into diagnostic radiology, its application in the procedural and highly complex environment of IR is now rapidly emerging, promising to revolutionize every aspect of the specialty [1, 2]. IR, with its reliance on advanced imaging techniques and precision-guided interventions, presents an ideal landscape for the integration of AI-powered solutions. However, the path to adoption is not without its challenges, including the need for vast, high-quality datasets, the non-standardizable human elements of procedures, and the need for robust clinical validation [1]. This article will explore the current and future roles of AI in interventional radiology, from patient selection and procedural guidance to robotics and outcome prediction.

Enhancing Patient Selection and Outcome Prediction

One of the most significant contributions of AI in IR is its ability to analyze vast and varied datasets—including clinical, imaging, biological, and genetic information—to support decision-making and predict treatment outcomes. Traditional clinical risk calculators are often based on linear models, but Machine Learning (ML) can uncover complex, nonlinear associations within the data, offering a more nuanced and personalized approach to patient management [2].

In interventional oncology, for instance, AI models are being developed to predict tumor response to treatments like transarterial chemoembolization (TACE) for hepatocellular carcinoma. Studies have shown that AI models, by analyzing features from pre-treatment CT or MRI scans, can distinguish between TACE-susceptible and TACE-refractory cases with greater accuracy than traditional staging systems like the Barcelona Clinic Liver Cancer (BCLC) system [2]. Similarly, AI has been used to predict local tumor progression and overall survival in patients with adrenal metastases treated with percutaneous thermal ablation, achieving high accuracy by combining clinical data with features from pretreatment imaging [2].

Revolutionizing Procedural Guidance and Execution

AI is poised to transform the intra-operative phase of interventional procedures by providing real-time decision support and enhancing imaging capabilities.

Advanced Image Analysis

During procedures, AI can assist radiologists with real-time image analysis, improving the efficiency, accuracy, and safety of interventions. This includes:

• Automated Segmentation: AI algorithms can automatically segment anatomical structures, tumors, and medical devices (like catheters and needles) from fluoroscopic or ultrasound images. This aids in precise navigation and ensures complete tumor coverage during ablation procedures [1, 2].
• Motion Artifact Reduction: In digital subtraction angiography (DSA), patient motion can create artifacts that obscure the view. AI-powered deep learning models can generate high-quality subtraction images without the need for a separate mask image, reducing artifacts and radiation dose [2].
• Needle and Catheter Tracking: AI-driven systems can accurately detect and track needles and catheters in real-time during ultrasound-guided procedures, improving placement accuracy even in challenging situations where the needle is not fully visible [2].

The Synergy of AI and Robotics

The integration of AI with robotics is paving the way for a new era of semi-automated and autonomous interventions. Robotic systems, guided by AI, can enhance a surgeon's precision and dexterity while also providing radiation protection through remote operation. AI helps these systems process multimodal data from sensors—such as visual data from endoscopic cameras and force-feedback sensors—to navigate complex anatomical environments like the cardiovascular system with superhuman stability [1, 2]. For example, a deep learning-driven robotic guidance system has been developed to obtain vascular access by creating a 3D map of the arm’s vasculature from ultrasound and near-infrared imaging, allowing for real-time tracking and precise needle insertion [2].

The Future: Simulated Reality and Virtual Biopsy

Looking ahead, AI will be a critical enabler for even more advanced applications.

• Simulated Reality: Augmented Reality (AR) and Virtual Reality (VR) can overlay 3D volumetric images (from CT or MRI) onto the patient or into the operator's field of view. AI automates the crucial steps of landmark recognition, motion compensation, and safe trajectory planning, allowing for highly accurate visual navigation during procedures like percutaneous vertebroplasty, potentially reducing the reliance on fluoroscopy [1, 2].

• Virtual Biopsy: This concept involves using radiomics—the extraction of quantitative data from medical images—to characterize tissue at a molecular level without an invasive procedure. AI models can analyze these radiomic features to classify lesions, such as distinguishing between benign and malignant tumors on a mammogram, offering a non-invasive diagnostic alternative [2].

Conclusion

The role of AI in interventional radiology is rapidly expanding from a theoretical concept to a practical reality. By enhancing decision-making, improving procedural accuracy, and enabling robotic and simulated reality applications, AI is set to drive precision medicine to new heights. While significant challenges related to data standardization, training, and ethical oversight remain, the potential for AI to revolutionize the interventional radiologist's workflow is undeniable. As these technologies mature and are validated through rigorous clinical trials, they will become indispensable tools in providing safer, more effective, and highly personalized care for patients.