Evaluation of an Autonomous Navigation Method for Vascular Interventional Surgery in Virtual Environment

Meng, Fanxu; Guo, Shuxiang; Zhou, Wei; Chen, Zhengyang

doi:10.1109/icma54519.2022.9856107

Cited by 7 publications

(22 citation statements)

References 20 publications

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“…Figure 2 shows the anatomical regions where each study focuses. Experiments within or around the blood vessels of the heart were reported by the majority of studies (10/14, 71%; Rafii-Tari et al, 2013Chi et al, 2018aChi et al, ,b, 2020You et al, 2019;Kweon et al, 2021;Meng et al, 2021Meng et al, , 2022Wang et al, 2022), with the study with the longest path length starting at the femoral artery and finishing at the coronary artery (Wang et al, 2022). Nonanatomical vessel platforms "idealized" for simple navigation were used in three studies (3/14, 21%; Behr et al, 2019;Zhao et al, 2019;Cho et al, 2022), and the porcine liver venous system in one study (Karstensen et al, 2022).…”

Section: Experimental Designmentioning

confidence: 99%

“…As shown in Figure 1, 462 studies met the search criteria, and 21 full-text studies were assessed against the eligibility criteria. A total of 14 were identified for review (Rafii-Tari et al, 2013Chi et al, 2018aChi et al, ,b, 2020Behr et al, 2019;You et al, 2019;Zhao et al, 2019;Kweon et al, 2021;Meng et al, 2021Meng et al, , 2022Cho et al, 2022;Karstensen et al, 2022;Wang et al, 2022). The characteristics of the fourteen studies are listed in Table 2.…”

Section: Studiesmentioning

confidence: 99%

“…. RL methods RL was used in nine studies (9/14, 64%) with algorithms including A3C, DDPG, DQN, Dueling DQN, HER, PI 2 , PPO, and Rainbow (Chi et al, 2018a(Chi et al, , 2020Behr et al, 2019;You et al, 2019;Kweon et al, 2021;Meng et al, 2021Meng et al, , 2022Cho et al, 2022;Karstensen et al, 2022). Demonstrator data in some form (GAIL, Behavior Cloning, or HD) was used as a precursor in four of the studies (4/14, 29%) during training (LfD), in conjunction with other RL algorithms (Chi et al, 2018a;Behr et al, 2019;Kweon et al, 2021;Cho et al, 2022).…”

Section: Yolo Supervised Learningmentioning

confidence: 99%

“…Demonstrator data in some form (GAIL, Behavior Cloning, or HD) was used as a precursor in four of the studies (4/14, 29%) during training (LfD), in conjunction with other RL algorithms (Chi et al, 2018a;Behr et al, 2019;Kweon et al, 2021;Cho et al, 2022). The SOFA framework (Inria, Strasbourg, France; Faure et al, 2012) was used for training RL models in four studies (4/14, 29%; Behr et al, 2019;Cho et al, 2022;Karstensen et al, 2022;Meng et al, 2022), the Unity engine (Unity Technologies, San Francisco, USA) was used in two studies (2/14, 14%; You et al, 2019;Meng et al, 2021), while the platform used for training was not specified in three…”

Section: Yolo Supervised Learningmentioning

confidence: 99%

“…Two studies (2/14, 14%) were performed entirely in silico, and hence no tracking method was required (Meng et al, 2021(Meng et al, , 2022.…”

Section: Evaluationmentioning

confidence: 99%

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Artificial intelligence in the autonomous navigation of endovascular interventions: a systematic review

Robertshaw

Karstensen

Jackson

et al. 2023

Front. Hum. Neurosci.

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BackgroundAutonomous navigation of catheters and guidewires in endovascular interventional surgery can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure while increasing access to treatment.ObjectiveTo determine from recent literature, through a systematic review, the impact, challenges, and opportunities artificial intelligence (AI) has for the autonomous navigation of catheters and guidewires for endovascular interventions.MethodsPubMed and IEEEXplore databases were searched to identify reports of AI applied to autonomous navigation methods in endovascular interventional surgery. Eligibility criteria included studies investigating the use of AI in enabling the autonomous navigation of catheters/guidewires in endovascular interventions. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). PROSPERO: CRD42023392259.ResultsFour hundred and sixty-two studies fulfilled the search criteria, of which 14 studies were included for analysis. Reinforcement learning (RL) (9/14, 64%) and learning from expert demonstration (7/14, 50%) were used as data-driven models for autonomous navigation. These studies evaluated models on physical phantoms (10/14, 71%) and in-silico (4/14, 29%) models. Experiments within or around the blood vessels of the heart were reported by the majority of studies (10/14, 71%), while non-anatomical vessel platforms “idealized” for simple navigation were used in three studies (3/14, 21%), and the porcine liver venous system in one study. We observed that risk of bias and poor generalizability were present across studies. No procedures were performed on patients in any of the studies reviewed. Moreover, all studies were limited due to the lack of patient selection criteria, reference standards, and reproducibility, which resulted in a low level of evidence for clinical translation.ConclusionDespite the potential benefits of AI applied to autonomous navigation of endovascular interventions, the field is in an experimental proof-of-concept stage, with a technology readiness level of 3. We highlight that reference standards with well-identified performance metrics are crucial to allow for comparisons of data-driven algorithms proposed in the years to come.Systematic review registrationidentifier: CRD42023392259.

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Section: Experimental Designmentioning

confidence: 99%

Section: Studiesmentioning

confidence: 99%

Section: Yolo Supervised Learningmentioning

confidence: 99%

Section: Yolo Supervised Learningmentioning

confidence: 99%

“…Two studies (2/14, 14%) were performed entirely in silico, and hence no tracking method was required (Meng et al, 2021(Meng et al, , 2022.…”

Section: Evaluationmentioning

confidence: 99%

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Artificial intelligence in the autonomous navigation of endovascular interventions: a systematic review

Robertshaw

Karstensen

Jackson

et al. 2023

Front. Hum. Neurosci.

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Recurrent neural networks for generalization towards the vessel geometry in autonomous endovascular guidewire navigation in the aortic arch

et al. 2023

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Purpose Endovascular intervention is the state-of-the-art treatment for common cardiovascular diseases, such as heart attack and stroke. Automation of the procedure may improve the working conditions of physicians and provide high-quality care to patients in remote areas, posing a major impact on overall treatment quality. However, this requires the adaption to individual patient anatomies, which currently poses an unsolved challenge. Methods This work investigates an endovascular guidewire controller architecture based on recurrent neural networks. The controller is evaluated in-silico on its ability to adapt to new vessel geometries when navigating through the aortic arch. The controller’s generalization capabilities are examined by reducing the number of variations seen during training. For this purpose, an endovascular simulation environment is introduced, which allows guidewire navigation in a parametrizable aortic arch. Results The recurrent controller achieves a higher navigation success rate of 75.0% after 29,200 interventions compared to 71.6% after 156,800 interventions for a feedforward controller. Furthermore, the recurrent controller generalizes to previously unseen aortic arches and is robust towards size changes of the aortic arch. Being trained on 2048 aortic arch geometries gives the same results as being trained with full variation when evaluated on 1000 different geometries. For interpolation a gap of 30% of the scaling range and for extrapolation additional 10% of the scaling range can be navigated successfully. Conclusion Adaption to new vessel geometries is essential in the navigation of endovascular instruments. Therefore, the intrinsic generalization to new vessel geometries poses an essential step towards autonomous endovascular robotics.

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A zero-shot reinforcement learning strategy for autonomous guidewire navigation

Scarponi,

Duprez,

Nageotte

et al. 2024

Int J CARS

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Evaluation of an Autonomous Navigation Method for Vascular Interventional Surgery in Virtual Environment

Cited by 7 publications

References 20 publications

Artificial intelligence in the autonomous navigation of endovascular interventions: a systematic review

Artificial intelligence in the autonomous navigation of endovascular interventions: a systematic review

Recurrent neural networks for generalization towards the vessel geometry in autonomous endovascular guidewire navigation in the aortic arch

A zero-shot reinforcement learning strategy for autonomous guidewire navigation

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