Tremor Estimation and Removal in Robot-Assisted Surgery Using Improved Enhanced Band-Limited Multiple Fourier Linear Combiner

BACKGROUND

During a robot-assisted minimally invasive surgery, hand tremors in a surgeon's manipulation of the master manipulator can cause vibrations of the slave surgical instruments.

METHODS

This letter addresses this problem by proposing an improved Enhanced Band-Limited Multiple Linear Fourier Combiner (E-BMFLC) algorithm for filtering the physiological tremor signals of a surgeon's hand. The proposed method uses the amplitude of the input signal to adapt the learning rate and a dense division of the combiner bands for the higher amplitude bands of the tremor signals.

RESULTS

By using the proposed improved E-BMFLC algorithm, the compensation accuracy can be improved by 4.5%-8.9%, as well as a spatial position error of less than 1 mm.

CONCLUSION

The results show that among all filtering methods, the improved E-BMFLC filtering method has the highest number of successful experiments and the lowest experimental time.

Adding Safety Rules to Surgeon-Authored Virtual Reality Training

INTRODUCTION

The Toolkit for Illustration of Procedures in Surgery (TIPS) is an open source virtual reality (VR) laparoscopic simulation-based training environment with force feedback. The TIPS-author is a content creation interface that allows a surgeon educator (SE) to assemble new laparoscopic training modules. New technology enables safety rules to be specified by the SE, automatically tracks specified safety errors, and summarizes and communicates achievements and errors to the surgical trainee.

METHODS

The TIPS-author combines and initializes building blocks of anatomy with their physical properties, as selected by the SE from a database. The SE can add any safety rule that can be tested in terms of location, proximity, separation, clip count, and force. Errors are then automatically monitored during simulation and recorded as visual snapshots for feedback to the trainee. The TIPS was field tested at 2 surgical conferences, one before and one after adding the error snapshot feature.

RESULTS

Sixty-four respondents at 2 surgical conferences assessed the utility of TIPS on a Likert scale. While other ratings remained unchanged for an overall score of 5.24 of 7 (7 = very useful), the rating of the statement "The TIPS interface helps learners understand the force necessary to explore the anatomy" improved from 5.04 to 5.35 of 7 after the snapshot mechanism was added.

CONCLUSIONS

The ratings indicate the viability of the TIPS open source SE-authored surgical training units with safety rules. Presenting SE-determined procedural missteps via the snapshot mechanism at the end of the training increases perceived utility.

Performance and Capability Assessment in Surgical Subtask Automation

Robot-Assisted Minimally Invasive Surgery (RAMIS) has reshaped the standard clinical practice during the past two decades. Many believe that the next big step in the advancement of RAMIS will be partial autonomy, which may reduce the fatigue and the cognitive load on the surgeon by performing the monotonous, time-consuming subtasks of the surgical procedure autonomously. Although serious research efforts are paid to this area worldwide, standard evaluation methods, metrics, or benchmarking techniques are still not formed. This article aims to fill the void in the research domain of surgical subtask automation by proposing standard methodologies for performance evaluation. For that purpose, a novel characterization model is presented for surgical automation. The current metrics for performance evaluation and comparison are overviewed and analyzed, and a workflow model is presented that can help researchers to identify and apply their choice of metrics. Existing systems and setups that serve or could serve as benchmarks are also introduced and the need for standard benchmarks in the field is articulated. Finally, the matter of Human-Machine Interface (HMI) quality, robustness, and the related legal and ethical issues are presented.