Prime Time for Artificial Intelligence in Interventional Radiology

Jarrel Seah,Marc Sapoval,Gerard S Goh,Tom Boeken

doi:10.1007/s00270-021-03044-4

Abstract

Machine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR could lead the way to AI research and clinical applications for all interventional medical fields. This review will address an overview of machine learning, radiomics and AI in the field of interventional radiology, enumerating the possible applications of such techniques, while also describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology. Lastly, this review will address common errors in research in this field and suggest pathways for those interested in learning and becoming involved about AI.

Highlights

Artificial intelligence (AI) has been prominent in different fields including diagnostic radiology
This review will address an overview of machine learning, radiomics and artificial intelligence (AI) in the field of interventional radiology, enumerating the possible applications of such techniques, while describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology
AI breakthroughs are empowering the field of interventional radiology (IR) and recent surge in popularity was initially driven by the phenomenal success of deep neural networks in processing unstructured data such as images and audio through pattern recognition

Summary

Introduction

Artificial intelligence (AI) has been prominent in different fields including diagnostic radiology. Deep learning approaches may speed up corrected digital subtraction angiography, such as methods proposed by Gao et al [25] which use generative adversarial networks to generate subtraction images without the preliminary noncontrast acquisition, avoiding the issue of translational motion entirely This is achieved by acquiring a dataset of satisfactorily subtracted images paired with the unsubtracted images and training a neural network to predict the subtracted images from the unsubtracted image. While genetics and molecular pathology have played a large role in precision medicine, pre- and post-treatment imaging may identify additional disease phenotypes as well as quantify intervention success, which may help fine-tune management by prognosticating as well as determining the timing and need for follow-up imaging. While genetics and molecular pathology have played a large role in precision medicine, pre- and post-treatment imaging may identify additional disease phenotypes as well as quantify intervention success, which may help fine-tune management by prognosticating as well as determining the timing and need for follow-up imaging. [38]

Practical Challenges

How to Get Started in Ai

Findings

Conclusion