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End-to-End Framework Integrating Generative AI and Deep Reinforcement Learning for Autonomous Ultrasound Scanning

arXiv 2511.00114 ↗First posted 31 Oct 2025cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

Cardiac ultrasound (US) is among the most widely used diagnostic tools in cardiology for assessing heart health, but its effectiveness is limited by operator dependence, time constraints, and human error. The shortage of trained professionals, especially in remote areas, further restricts access. These issues underscore the need for automated solutions that can ensure consistent, and accessible cardiac imaging regardless of operator skill or location. Recent progress in artificial intelligence (AI), especially in deep reinforcement learning (DRL), has gained attention for enabling autonomous decision-making. However, existing DRL-based approaches to cardiac US scanning lack reproducibility, rely on proprietary data, and use simplified models. Motivated by these gaps, we present the first end-to-end framework that integrates generative AI and DRL to enable autonomous and reproducible cardiac US scanning. The framework comprises two components: (i) a conditional generative simulator combining Generative Adversarial Networks (GANs) with Variational Autoencoders (VAEs), that models the cardiac US environment producing realistic action-conditioned images; and (ii) a DRL module that leverages this simulator to learn autonomous, accurate scanning policies. The proposed framework delivers AI-driven guidance through expert-validated models that classify image type and assess quality, supports conditional generation of realistic US images, and establishes a reproducible foundation extendable to other organs. To ensure reproducibility, a publicly available dataset of real cardiac US scans is released. The solution is validated through several experiments. The VAE-GAN is benchmarked against existing GAN variants, with performance assessed using qualitative and quantitative approaches, while the DRL-based scanning system is evaluated under varying configurations to demonstrate effectiveness.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 73 references that are in the map. First posted on arXiv 31 Oct 2025.

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23 of 73 references.

References · 23

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  12. 2021Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learningcited by 21
11 more
  1. 2020Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learningcited by 17
  2. 2022VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigationcited by 12
  3. 2024Goal-conditioned reinforcement learning for ultrasound navigation guidancecited by 6
  4. 2025CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learningcited by 6
  5. 2022Reward Shaping Using Convolutional Neural Networkcited by 3
  6. 2024Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging Using Reinforcement Learningcited by 3
  7. 2024Imitation Learning for Robotic Assisted Ultrasound Examination of Deep Venous Thrombosis using Kernelized Movement Primitivescited by 3
  8. 2025Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfercited by 3
  9. 2025UAV-assisted Internet of Vehicles: A Framework Empowered by Reinforcement Learning and Blockchaincited by 3
  10. 2024Robot-Assisted Deep Venous Thrombosis Ultrasound Examination using Virtual Fixturecited by 2
  11. 2025Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imagingcited by 2

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  1. 2025Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation
  2. 2026Anatomical Prior-Driven Framework for Autonomous Robotic Cardiac Ultrasound Standard View Acquisition
  3. 2026US-VLA: An Ultrasound Vision-Language-Action Model for Embodied Abdomina
  4. 2024GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging
  5. 2025EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation
  6. 2025SonoGym: High Performance Simulation for Challenging Surgical Tasks with Robotic Ultrasound
  7. 2024A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images
  8. 2024Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach