Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging
Journal:
arXiv
Published Date:
Mar 19, 2025
Abstract
Medical Ultrasound (US) imaging has seen increasing demands over the past
years, becoming one of the most preferred imaging modalities in clinical
practice due to its affordability, portability, and real-time capabilities.
However, it faces several challenges that limit its applicability, such as
operator dependency, variability in interpretation, and limited resolution,
which are amplified by the low availability of trained experts. This calls for
the need of autonomous systems that are capable of reducing the dependency on
humans for increased efficiency and throughput. Reinforcement Learning (RL)
comes as a rapidly advancing field under Artificial Intelligence (AI) that
allows the development of autonomous and intelligent agents that are capable of
executing complex tasks through rewarded interactions with their environments.
Existing surveys on advancements in the US scanning domain predominantly focus
on partially autonomous solutions leveraging AI for scanning guidance, organ
identification, plane recognition, and diagnosis. However, none of these
surveys explore the intersection between the stages of the US process and the
recent advancements in RL solutions. To bridge this gap, this review proposes a
comprehensive taxonomy that integrates the stages of the US process with the RL
development pipeline. This taxonomy not only highlights recent RL advancements
in the US domain but also identifies unresolved challenges crucial for
achieving fully autonomous US systems. This work aims to offer a thorough
review of current research efforts, highlighting the potential of RL in
building autonomous US solutions while identifying limitations and
opportunities for further advancements in this field.
