Development and evaluation of an autonomous camera control algorithm on the da Vinci Surgical System.

BACKGROUND: Manual control of the camera arm in telerobotic surgical systems requires the surgeon to repeatedly interrupt the flow of the surgery. During surgery, there are instances when one or even both tools can drift out of the field of view. These issues may lead to increased workload and potential errors. METHODS: We performed a 20-participant subject study (including four surgeons) to compare different methods of camera control on a customized da Vinci Surgical System. We tested (a) an autonomous camera algorithm, (b) standard clutched control, and (c) an experienced camera operator using a joystick. RESULTS: The automated algorithm surpassed the traditional method of clutched camera control in measures of userperceived workload, efficiency, and progress. Additionally, it was consistently able to generate more centered and appropriately zoomed viewpoints than the other methods while keeping both tools safely inside the camera's field of view. CONCLUSIONS: Clinical systems of the future should consider automating the camera control aspects of robotic surgery.

Towards the Implementation of an Autonomous Camera Algorithm on the da Vinci Platform.

Camera positioning is critical for all telerobotic surgical systems. Inadequate visualization of the remote site can lead to serious errors that can jeopardize the patient. An autonomous camera algorithm has been developed on a medical robot (da Vinci) simulator. It is found to be robust in key scenarios of operation. This system behaves with predictable and expected actions for the camera arm with respect to the tool positions. The implementation of this system is described herein. The simulation closely models the methodology needed to implement autonomous camera control in a real hardware system. The camera control algorithm follows three rules: (1) keep the view centered on the tools, (2) keep the zoom level optimized such that the tools never leave the field of view, and (3) avoid unnecessary movement of the camera that may distract/disorient the surgeon. Our future work will apply this algorithm to the real da Vinci hardware.

Task analysis of laparoscopic camera control schemes.

BACKGROUND: Minimally invasive surgeries rely on laparoscopic camera views to guide the procedure. Traditionally, an expert surgical assistant operates the camera. In some cases, a robotic system is used to help position the camera, but the surgeon is required to direct all movements of the system. Some prior research has focused on developing automated robotic camera control systems, but that work has been limited to rudimentary control schemes due to a lack of understanding of how the camera should be moved for different surgical tasks. METHODS: This research used task analysis with a sample of eight expert surgeons to discover and document several salient methods of camera control and their related task contexts. RESULTS: Desired camera placements and behaviours were established for two common surgical subtasks (suturing and knot tying). CONCLUSION: The results can be used to develop better robotic control algorithms that will be more responsive to surgeons' needs. Copyright © 2015 John Wiley & Sons, Ltd.

Towards an autonomous robot for camera control during laparoscopic surgery.

INTRODUCTION: During laparoscopic surgery, the surgeon currently must instruct a human camera operator or a robotic arm to move the camera. This process is distracting, and the camera is not always placed in an ideal location. To mitigate these problems, we have developed a test platform that tracks laparoscopic instruments and automatically moves a camera with no explicit human direction. MATERIALS AND METHODS: The test platform is designed to mimic a typical laparoscopic working environment, where two hand-operated tools are manipulated through small ports. A pan-tilt-zoom camera is positioned over the tools, which emulates the positioning capabilities of a straight (0°) scope placed through a trocar. A camera control algorithm automatically keeps the tools in the view. In addition, two test tasks that require camera movement have been developed to aid in future evaluation of the system. RESULTS: The system was found to successfully track the laparoscopic instruments in the camera view as intended. The camera is moved and zoomed to follow the instruments in a smooth and consistent fashion. CONCLUSIONS: This technology shows that it is possible to create an autonomous camera system that cooperates with a surgeon without requiring any explicit user input. However, the currently implemented camera control behaviors are not ideal or sufficient for many surgical tasks. Future work will be performed to develop, test, and refine more complex behaviors that are optimized for different kinds of surgical tasks. In addition, portions of the test platform will be redesigned to enable its use in actual laparoscopic procedures.

Human performance in the task of port placement for biosensor use.

BACKGROUND: We conducted a study of participants' abilities to place a laparoscopic port for in vivo biosensor use. Biosensors have physical limitations that make port placement crucial to proper data collection. A new port placement algorithm enabled evaluation of port locations, using segmented patient data in a virtual environment. METHODS: Port placement scoring algorithms were integrated into an image-guided surgery system. Virtual test scenes were created to evaluate various scenarios encountered during biosensor use. Participants were scored based on their ability to choose a port location from which points of interest could be scanned with a biosensor. Participants' scores were also compared to those of a port placement algorithm. RESULTS: The port placement algorithm consistently outscored participants by 10-25%. Participants were inconsistent from trial to trial and from participant to participant. CONCLUSION: Port placement for biosensor procedures could be improved through training or augmentation.

Optimized port placement for in vivo biosensors.

BACKGROUND: We discuss the implementation of an automated port placement system for use with laparoscopic in vivo biosensors. Biosensors have physical limitations that make port placement crucial to proper data collection. The port placement process is prohibitively complex to execute optimally by human estimation. METHODS: Port placement algorithms were integrated into an image-guided surgery system. Variables for optimization of ports include biosensor length, data collection restrictions, obstacles, and regions of interest. The port placement is applied to 3D virtual 'patients' created from medical imaging data. An example implementation for a Raman biosensor probe was created. RESULTS: The prototype system can correctly find ideal port locations on a virtual patient based on biosensor limitations and regions of interest. Conversely, the system can evaluate and score an individual port selected by a user. CONCLUSION: As biosensors become incorporated into laparoscopic surgical environments, an automated port placement system would enable their optimized integration.