Interventional Radiology ex-machina: impact of Artificial Intelligence on practice

Abstract

Artificial intelligence (AI) is a branch of Informatics that uses algorithms to tirelessly process data, understand its meaning and provide the desired outcome, continuously redefining its logic. AI was mainly introduced via artificial neural networks, developed in the early 1950s, and with its evolution into "computational learning models." Machine Learning analyzes and extracts features in larger data after exposure to examples; Deep Learning uses neural networks in order to extract meaningful patterns from imaging data, even deciphering that which would otherwise be beyond human perception. Thus, AI has the potential to revolutionize the healthcare systems and clinical practice of doctors all over the world. This is especially true for radiologists, who are integral to diagnostic medicine, helping to customize treatments and triage resources with maximum effectiveness. Related in spirit to Artificial intelligence are Augmented Reality, mixed reality, or Virtual Reality, which are able to enhance accuracy of minimally invasive treatments in image guided therapies by Interventional Radiologists. The potential applications of AI in IR go beyond computer vision and diagnosis, to include screening and modeling of patient selection, predictive tools for treatment planning and navigation, and training tools. Although no new technology is widely embraced, AI may provide opportunities to enhance radiology service and improve patient care, if studied, validated, and applied appropriately.

Keywords: Artificial intelligence (AI); Augmented reality (AR); Deep learning (DL); Interventional radiology (IR); Machine learning (ML); Virtual reality (VR).

Fig. 1

"Endobronchial navigation for lung tumor biopsy". a Pre-procedural CT planning; b 3D roadmap with tumor segmentation; c Real-time 3D fluoroscopic image

Fig. 2

“Automatic 3D detection of arterial bleeding of the leg in a traumatic patient”—a CTA reveals arterial bleeding of the leg in a traumatic patient. b Initial 2D angiography does not demonstrate bleeding. c Target segmentation was performed on the CTA dataset after CBCT-CT fusion; then 3D CBCT datasets were synchronized with the C-arm and overlaid on live fluoroscopy, during the intervention, to facilitate catheter navigation to the damaged vessel. d Once the target arterial culprit was engaged, a selective angiogram is performed to confirm correct targeting of the bleeding site

Fig. 3

“Automatic 3D detection of prostatic arteries using Cone-Beam CT during Prostatic Arterial Embolization”—a CBCT identification of prostatic arteries; b Realization of 3D roadmap; c Overlap on fluoroscopic images”

Fig. 4

“Endovascular navigation: virtual aortoscopy”—a Preoperative CT image; b, c 3D volume rendering of Aorta, d Creation of virtual angioscopy of the Aorta

Fig. 5

“Endovascular navigation: virtual aortic dissection angioscopy “—a, b Preoperative assessment on CT images; c Creation of virtual angioscopy to evaluate the approach for dissection treatment