Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills

Abstract

The best method of training for laparoscopic surgical skills is controversial. Some advocate observation in the operating room, while others promote animal and simulated models or a combination of surgery-related tasks. A crucial process in surgical education is to evaluate the level of surgical skills. For laparoscopic surgery, skill evaluation is traditionally performed subjectively by experts grading a video of a procedure performed by a student. By its nature, this process uses fuzzy criteria. The objective of the current study was to develop and assess a skill scale using Markov models (MMs). Ten surgeons [five novice surgeons (NS); five expert surgeons (ES)] performed a cholecystectomy and Nissen fundoplication in a porcine model. An instrumented laparoscopic grasper equipped with a three-axis force/torque (F/T) sensor was used to measure the forces/torques at the hand/tool interface synchronized with a video of the tool operative maneuvers. A synthesis of frame-by-frame video analysis and a vector quantization algorithm, allowed to define F/T signatures associated with 14 different types of tool/tissue interactions. The magnitude of F/T applied by NS and ES were significantly different (p < 0.05) and varied based on the task being performed. High F/T magnitudes were applied by NS compared with ES while performing tissue manipulation and vise versa in tasks involved tissue dissection. From each step of the surgical procedures, two MMs were developed representing the performance of three surgeons out of the five in the ES and NS groups. The data obtained by the remaining two surgeons in each group were used for evaluating the performance scale. The final result was a surgical performance index which represented a ratio of statistical similarity between the examined surgeon's MM and the MM of NS and ES. The difference between the performance index value, for a surgeon under study, and the NS/ES boundary, indicated the level of expertise in the surgeon's own group. Using this index, 87.5% of the surgical procedures were correctly classified into the NS and ES groups. The 12.5% of the procedures that were misclassified were performed by the ES and classified as NS. However in these cases the performance index values were very close to the NS/ES boundary. Preliminary data suggest that a performance index based on MM and F/T signatures provides an objective means of distinguishing NS from ES. In addition, this methodology can be further applied to evaluate haptic virtual reality surgical simulators for improving realism in surgical education.

Towards automatic skill evaluation: Detection and segmentation of robot-assisted surgical motions

“…In the field of high-level surgical modeling, Rosen et al [14][15][16] demonstrated that statistical models derived from recorded force and motion data can be used to objectively classify surgical skill level as either novice or expert. The results show that the statistical distances between Hidden Markov Models (HMMs) representing varying levels of surgical skill were significantly different (a , 0.05).…”

Surgical Gesture Segmentation and Recognition

“…The temporal evolution of such observations is typically modeled using a Hidden Markov Model (HMM), where each gesture corresponds to one or more states of the HMM and the transitions among consecutive gestures are modeled by the HMM transition probabilities. Different papers [6,7,8,9,10,11,12] use different models for the observations associated with each gesture, including discrete HMMs, Gaussian HMMs, factor analyzed HMMs and Sparse HMMs. While generally successful, these methods rely mostly on local cues from a few frames, thus failing to capture global cues about the whole execution of a gesture.…”

Sparse Hidden Markov Models for Surgical Gesture Classification and Skill Evaluation

“…Hidden Markov models (HMMs) provide an excellent framework for doing this. The simplest approach is to model each surgeme as the state of an HMM and to vector-quantize the observations from each surgeme into discrete symbols [6,7]. Alternatively, one can model the observations from each surgeme using a Gaussian [8].…”

“…These methods are generally easy to implement, but lack a detailed description of the surgical procedure. Another approach is to use statistical models to decompose a surgical task into a series of pre-defined surgical gestures or surgemes [6][7][8][9][10][11]. For example, in a suturing task, the surgemes can be 'insert a needle', 'grab a needle', 'position a needle', etc.…”