Abstract
Artificial Intelligence, Machine Learning, and myriad related techniques are becoming ever more commonplace throughout industry and society, and radiology is by no means an exception. It is essential for every radiologists of every subspecialty to gain familiarity and confidence with these techniques as they become increasingly incorporated into the routine practice in both academic and private practice settings. In this article, we provide a brief review of several definitions and techniques that are commonly used in AI, and in particular machine vision, and examples of how they are currently being applied to the setting of clinical neuroradiology. We then review the unique challenges that the adoption and application of faces within the subspecialty of pediatric neuroradiology, and how these obstacles may be overcome. We conclude by presenting specific examples of how AI is currently being applied within the field of pediatric neuroradiology and the potential opportunities that are available for future applications.
Highlights
Advances in artificial intelligence (AI) could fundamentally change how millions of people will live and work
After the basic techniques have been introduced, we hope to show how deeply ingrained the technology has already become in some areas of neuroradiology practice as well as how much potential still exists in others, notably in pediatric neuroradiology
Pediatric neuroradiology presents a unique set of challenges to the radiologist that do not exist in adult neuroradiology which may, at least in part, explain why AI has not become as well established as it has in the adult world
Summary
Advances in artificial intelligence (AI) could fundamentally change how millions of people will live and work. Due to some exciting advances within the last decade within the field of machine vision, medical specialties that rely on image analysis are susceptible to such revolution; disciplines such as radiology, pathology, dermatology, and ophthalmology are among those that are actively preparing to incorporate AI as a part of daily workflows [1, 2]. We start with a general description of AI techniques commonly used in neuroradiology. This is followed by a few examples of AI in Pediatric Neuroradiology. Artificial Intelligence in Pediatric Neuroradiology highlight a number of factors that may contribute to the relative disparity between AI’s deployment in adult and pediatric radiology. After the basic techniques have been introduced, we hope to show how deeply ingrained the technology has already become in some areas of neuroradiology practice as well as how much potential still exists in others, notably in pediatric neuroradiology
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