ArticleInternational journal of computer assisted radiology and surgery2025
Robotic navigation with deep reinforcement learning in transthoracic echocardiography.
Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Automated image acquisition of apical four-chamber view with robotic echocardiography.International journal of computer assisted radiology and surgery · 2026Article
- A Review and Perspective of Techniques for Autonomous Robotic Ultrasound Acquisitions.Sensors (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- Comprehensive review of reinforcement learning for medical ultrasound imaging.Artificial intelligence review · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
purposeThe search for heart components in robotic transthoracic echocardiography is a time-consuming process. This paper proposes an optimized robotic navigation system for heart components using deep reinforcement learning to achieve an efficient and effective search technique for heart components.
methodThe proposed method introduces (i) an optimized search behavior generation algorithm that avoids multiple local solutions and searches for the optimal solution and (ii) an optimized path generation algorithm that minimizes the search path, thereby realizing short search times.
resultsThe mitral valve search with the proposed method reaches the optimal solution with a probability of 74.4%, the mitral valve confidence loss rate when the local solution stops is 16.3% on average, and the inspection time with the generated path is 48.6 s on average, which is 56.6% of the time cost of the conventional method.
conclusionThe results indicate that the proposed method improves the search efficiency, and the optimal location can be searched in many cases with the proposed method, and the loss rate of the confidence in the mitral valve was low even when a local solution rather than the optimal solution was reached. It is suggested that the proposed method enables accurate and quick robotic navigation to find heart components.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.