Towards an optimization of catheter guidance in vascular surgery: A comparative analysis of the contribution of reinforcement learning
Background: Precision in catheter guidance is essential for the success of vascular surgeries, yet current methods often need more accuracy due to the complex anatomy and dynamics of blood vessels. Methods: This study evaluates the efficacy of advanced reinforcement learning (RL) techniques to enhan...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-01-01
|
| Series: | Intelligent Surgery |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666676624000103 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832595699355615232 |
|---|---|
| author | Cheima Bouden |
| author_facet | Cheima Bouden |
| author_sort | Cheima Bouden |
| collection | DOAJ |
| description | Background: Precision in catheter guidance is essential for the success of vascular surgeries, yet current methods often need more accuracy due to the complex anatomy and dynamics of blood vessels. Methods: This study evaluates the efficacy of advanced reinforcement learning (RL) techniques to enhance catheter navigation. We compare different RL approaches within simulated vascular environments, focusing on their success rates, operational efficiency, and adaptability to varied clinical scenarios. Results: Advanced reinforcement learning techniques display exceptional performance, yielding high success rates and improved precision in catheter guidance. Integrating specific enhancements has notably increased learning speeds and strengthened operational robustness. Conclusion: The study indicates that reinforcement learning could significantly improve the precision and safety of catheter navigation in vascular surgery. By adopting these techniques, medical practices could see more accurate and less invasive procedures, enhancing patient outcomes. Future research should aim to refine these algorithms for wider clinical use and integration. |
| format | Article |
| id | doaj-art-98bac87e160240758597979174ed6f74 |
| institution | Kabale University |
| issn | 2666-6766 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Intelligent Surgery |
| spelling | doaj-art-98bac87e160240758597979174ed6f742025-01-18T05:05:22ZengKeAi Communications Co., Ltd.Intelligent Surgery2666-67662024-01-0175361Towards an optimization of catheter guidance in vascular surgery: A comparative analysis of the contribution of reinforcement learningCheima Bouden0LISIA Laboratory, University of Constantine 2 - Abdelhamid Mehri, New City - Ali Mendjeli, Constantine, AlgeriaBackground: Precision in catheter guidance is essential for the success of vascular surgeries, yet current methods often need more accuracy due to the complex anatomy and dynamics of blood vessels. Methods: This study evaluates the efficacy of advanced reinforcement learning (RL) techniques to enhance catheter navigation. We compare different RL approaches within simulated vascular environments, focusing on their success rates, operational efficiency, and adaptability to varied clinical scenarios. Results: Advanced reinforcement learning techniques display exceptional performance, yielding high success rates and improved precision in catheter guidance. Integrating specific enhancements has notably increased learning speeds and strengthened operational robustness. Conclusion: The study indicates that reinforcement learning could significantly improve the precision and safety of catheter navigation in vascular surgery. By adopting these techniques, medical practices could see more accurate and less invasive procedures, enhancing patient outcomes. Future research should aim to refine these algorithms for wider clinical use and integration.http://www.sciencedirect.com/science/article/pii/S2666676624000103Artificial intelligenceReinforcement learningVascular surgeryCatheter guidanceSimulation |
| spellingShingle | Cheima Bouden Towards an optimization of catheter guidance in vascular surgery: A comparative analysis of the contribution of reinforcement learning Intelligent Surgery Artificial intelligence Reinforcement learning Vascular surgery Catheter guidance Simulation |
| title | Towards an optimization of catheter guidance in vascular surgery: A comparative analysis of the contribution of reinforcement learning |
| title_full | Towards an optimization of catheter guidance in vascular surgery: A comparative analysis of the contribution of reinforcement learning |
| title_fullStr | Towards an optimization of catheter guidance in vascular surgery: A comparative analysis of the contribution of reinforcement learning |
| title_full_unstemmed | Towards an optimization of catheter guidance in vascular surgery: A comparative analysis of the contribution of reinforcement learning |
| title_short | Towards an optimization of catheter guidance in vascular surgery: A comparative analysis of the contribution of reinforcement learning |
| title_sort | towards an optimization of catheter guidance in vascular surgery a comparative analysis of the contribution of reinforcement learning |
| topic | Artificial intelligence Reinforcement learning Vascular surgery Catheter guidance Simulation |
| url | http://www.sciencedirect.com/science/article/pii/S2666676624000103 |
| work_keys_str_mv | AT cheimabouden towardsanoptimizationofcatheterguidanceinvascularsurgeryacomparativeanalysisofthecontributionofreinforcementlearning |