A CNN-based prototype method of unstructured surgical state perception and navigation for an endovascular surgery robot

Zhao, Yan; Guo, Shuxiang; Wang, Yuxin; Cui, Jinxin; Ma, Youchun; Zeng, Yuwen; Liu, X.; Jiang, Yuhua; Li, Youxinag; Shi, Liwei; Xiao, Nan

doi:10.1007/s11517-019-02002-0

Cited by 69 publications

(35 citation statements)

References 53 publications

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“…If the goal of robotic surgery is full automation, and all surgeons who use the complete surgical systems operating today are able to perform surgery without haptics, relying on visual cues alone, one has to wonder if the development of haptics is truly necessary. A CNN-based framework to autonomously control guide wires in vessels during angiography is currently being developed that will use haptics recorded during robotic angiography performed by a clinician to develop algorithms that can enable an autonomously functioning robot to successfully navigate vessels intra-luminally [ 109 ]. As with humans, the current models involve two-dimensional image interpretation with fluoroscopy being converted into three-dimensional robotic movements [ 110 , 111 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?

Gumbs

Frigerio

Spolverato

et al. 2021

Sensors

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Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?

Gumbs

Frigerio

Spolverato

et al. 2021

Sensors

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“…Considering that simple control operations are repeatedly performed to utilize a robot-assisted intervention system, deep RL may be a solution that effectively alleviates the burden of human operators. Recent applications for autonomous control of interventional devices in phantom simulation supported the potential applicability of deep RL (You et al, 2019;Behr et al, 2019;Karstensen et al, 2020;Chi et al, 2020;Zhao et al, 2019). In this study, we propose a deep RL framework for autonomous guidewire navigation in robot-assisted coronary interventions.…”

mentioning

confidence: 88%

Deep reinforcement learning for guidewire navigation in coronary artery phantom

Kweon,

Kim,

Lee

et al. 2021

Preprint

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In percutaneous intervention for treatment of coronary plaques, guidewire navigation is a primary procedure for stent delivery. Steering a flexible guidewire within coronary arteries requires considerable training, and the non-linearity between the control operation and the movement of the guidewire makes precise manipulation difficult. Here, we introduce a deep reinforcement learning (RL) framework for autonomous guidewire navigation in a robot-assisted coronary intervention. Using Rainbow, a segment-wise learning approach is applied to determine how best to accelerate training using human demonstrations with deep Q-learning from demonstrations (DQfD), transfer learning, and weight initialization. 'State' for RL is customized as a focus window near the guidewire tip, and subgoals are placed to mitigate a sparse reward problem. The RL agent improves performance, eventually enabling the guidewire to reach all valid targets in 'stable' phase. Our framework opens a new direction in the automation of robot-assisted intervention, providing guidance on RL in physical spaces involving mechanical fatigue.

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Section: Introductionmentioning

confidence: 98%