James Kolenchery Rappel

Assessing suturing techniques using a virtual reality surgical simulator

Advantages of virtual‐reality simulators surgical skill assessment and training include more training time, no risk to patient, repeatable difficulty level, reliable feedback, without the resource demands, and ethical issues of animal‐based training. We tested this for a key subtask and showed a strong link between skill in the simulator and in reality. Suturing performance was assessed for four groups of participants, including experienced surgeons and naive subjects, on a custom‐made virtual‐reality simulator. Each subject tried the experiment 30 times using five different types of needles to perform a standardized suture placement task. Traditional metrics of performance as well as new metrics enabled by our system were proposed, and the data indicate difference between trained and untrained performance. In all traditional parameters such as time, number of attempts, and motion quantity, the medical surgeons outperformed the other three groups, though differences were not significant. However, motion smoothness, penetration and exit angles, tear size areas, and orientation change were statistically significant in the trained group when compared with untrained group. This suggests that these parameters can be used in virtual microsurgery training.

A Digital Stereo Microscope Platform for Microsurgery Training

We describe a software defined surgical microscope platform for developing AI applications in surgical training. The microscope has facility to merge and render multiple streams of live and/or stored video, and has the ability to enhance, annotate, and measure using a 3D position and orientation tracking forceps. A configuration mechanism controls the zoom, focus and disparity of the stereo view and stores surgeon and procedure specific configuration. The system tracks the surgical motion and analyses its quality in realtime. Several measures of quality of motion are described and can be used as a platform to develop AI applications in surgical training.

Surgical motion task performance in a hand eye colocated digital stereo microcsope

The effect of hand-eye colocation in performing dexterous fine movements such as microsurgical manipulation is studied under a novel digital stereo microscope. Hand motion data is captured under conditions of hand-eye colocation and separation. Both configurations are tested with monoscopic and stereoscopic vision. A set of microsurgical task abstractions are created to reduce the effect of prior expertise. Finally the captured motion data is analyzed to determine the effect of colocation and stereopsis for surgical motion tasks.

A system for analysing surgical motion under a surgical visual aid

A system for evaluating the hand motion under surgical visual aids is discussed. The system consists of a set of visually guided motor tasks, a method of tracking the hand motion and a set of techniques for analyzing the movement data. Visually guided motor tasks abstract the surgical motion to enable performance by non-surgeons. The movement data is analyzed to identify the motion elements such as motion induced by measurement system noise, motion due to physiological tremor, motion due to myoclonic jerk, drift and voluntary dexterous motion. Empirical mode decomposition is used as the method to analyze the motion data along with its frequency plot to verify that the decomposed signals indeed correspond to the respective processes. Early results of the complex multicomponent data is reported with the extraction of the voluntary movement and the involuntary movement data.