Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging

• URL: http://arxiv.org/abs/2503.16543v1
• Date: Wed, 19 Mar 2025 03:22:27 GMT
• Title: Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging
• Authors: Hanae Elmekki, Saidul Islam, Ahmed Alagha, Hani Sami, Amanda Spilkin, Ehsan Zakeri, Antonela Mariel Zanuttini, Jamal Bentahar, Lyes Kadem, Wen-Fang Xie, Philippe Pibarot, Rabeb Mizouni, Hadi Otrok, Shakti Singh, Azzam Mourad,
• Abstract summary: This work aims to offer a thorough review of current research efforts, highlighting the potential of RL in building autonomous US solutions.<n>Existing surveys on advancements in the US scanning domain predominantly focus on partially autonomous solutions leveraging AI.<n>To bridge this gap, this review proposes a comprehensive taxonomy that integrates the stages of the US process with the RL development pipeline.
• Score: 15.38820664844588
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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