Automation Technology and Human Performance: Current Research and Trends

By Mark W. Scerbo | Go to book overview

AUTOMATION AND SURGICAL PERFORMANCE IN A BIOPSY PROCEDURE

Corinna E. Lathan

Biomedical Engineering, The Catholic University of America

Biopsy procedures are often needed to acquire tissue samples for pathological analysis so a treatment plan can be developed. Computed Tomography (CT) directed needle biopsies are routinely performed by interventional radiologists to gather tissue samples near the spine. As currently practiced, this procedure requires a great deal of spatial reasoning, skill, and training on the part of the radiologist. Image-guided systems that could assist the radiologist in needle placement and alignment would be a great improvement. Even further gains could be realized by the development of a semi-autonomous robotic biopsy system that could place the needle under the guidance of the radiologist.

Three critical task components of the spine biopsy procedure are as follows: Select the best CT slice for viewing the lesion; Plan the path between the skin entry point and the end-point; Move the needle to the biopsy location. This procedure can be time consuming and tedious since the biopsy needle must be advanced slowly and its position checked several times to ensure vital organs are not damaged. While this technique is generally effective, there is a time-accuracy tradeoff. The error tracking the planned path may be large, resulting in an increase in time, however, the error in end-point accuracy is usually low since the procedure continues until the desired endpoint is reached. The goal of an automated procedure would be to both decrease path tracking error and increase precision.

The current biopsy procedure, as represented by Figure 1(a), shows that the human operator carries most of the perceptual (select image), cognitive (path planning), and motor (move needle) workload. The radiologist controls the insertion of the needle directly and only uses occasional "snapshots" from the CT scanner for visual feedback. The system in Figure 1(b) is still manually controlled, but the visual feedback has been enhanced greatly to give the radiologist real-time image guidance during the insertion. Real-time 3-D image guidance would relieve some of the operator workload as well as reduce path planning error.

Figure 1(c)
depicts a semi-autonomous system in which a haptic master arm provides force feedback to help guide the radiologist and drive a robotic system that actually performs the task. In this scenario, the functions handled by the human are significantly reduced, allowing the radiologist to concentrate on the parts of the procedure where human judgment is required most. Shared control will reallocate some of the motor function from the human to the robot, reducing total error and improving accuracy. Finally, note that the progression from Figures 1(a) to 1(c) is downward compatible; it does not preclude the semi-autonomous system from being operated in manual mode.

Figure 1. Relative function allocation of system components: the human operator, imaging information, and needle insertion.

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