Abstract:Introduction
Short response time is critical for future military medical operations in austere settings or remote areas. Such effective patient care at the point of injury can greatly benefit from the integration of semi-autonomous robotic systems. To achieve autonomy, robots would require massive libraries of maneuvers collected with the goal of training machine learning algorithms. Although this is attainable in controlled settings, obtaining surgical data in austere settings can be difficu… Show more
“…[83]. A training data set for automated surgical operations by robots and methods for its propagation and sharing to other robotic platforms were discussed in [84] in order to develop the capability to perform emergency medical/surgical procedures using AI to tackle the highly emergent situations where availability of healthcare professionals risks lives. Those surgical maneuvers were compiled as a library for sharing for continuous learning and improvements.…”
“…[83]. A training data set for automated surgical operations by robots and methods for its propagation and sharing to other robotic platforms were discussed in [84] in order to develop the capability to perform emergency medical/surgical procedures using AI to tackle the highly emergent situations where availability of healthcare professionals risks lives. Those surgical maneuvers were compiled as a library for sharing for continuous learning and improvements.…”
“…It is important to know that every time a procedure is carried out with the da Vinci robotic system, the motion data of the robotic arms are being recorded and transmitted to the manufacturer. Engineers and computer scientists hope that the sheer quantity of these data will permit the generation of functioning algorithms that will ultimately result in more autonomous actions by surgical robots [71]. Although it is tempting to wonder whether or not this is possible, perhaps this is not the most important question.…”
Section: Robotic-assisted Surgery and Autonomous Actionsmentioning
This is a review focused on advances and current limitations of computer vision (CV) and how CV can help us obtain to more autonomous actions in surgery. It is a follow-up article to one that we previously published in Sensors entitled, “Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?” As opposed to that article that also discussed issues of machine learning, deep learning and natural language processing, this review will delve deeper into the field of CV. Additionally, non-visual forms of data that can aid computerized robots in the performance of more autonomous actions, such as instrument priors and audio haptics, will also be highlighted. Furthermore, the current existential crisis for surgeons, endoscopists and interventional radiologists regarding more autonomy during procedures will be discussed. In summary, this paper will discuss how to harness the power of CV to keep doctors who do interventions in the loop.
“…In the no-transfer scenario, the training and testing data were obtained from the same domain, compared to a domain-transfer scenario where training data is a blend of simulated and real robot data but tested on real robot data only. Authors in [19] tested this on several types of robots and found that the transfer model showed an accuracy of 81%, 97.5%, and 93% for the YuMi, Taurus II, and the da Vinci robot, respectively. While, in the YuMi case, the ratio of real-to-simulated data was 22% to 78%, later were trained only with simulation data.…”
Robotic surgery has increased the domain of surgeries possible. Several examples of partial surgical automation have been seen in the past decade. We break down the path of automation tasks into features required and provide a checklist that can help reach higher levels of surgical automation. Finally, we discuss the current challenges and advances required to make this happen.
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