Experimental Study 3
Surgeon cognitive workload correlates, during simulated laparoscopic cholecystectomy and simultaneous cognitive tasks
3.3 Study 3: Surgeon cognitive workload correlates during simulated laparoscopic cholecystectomy and simultaneous cognitive tasks (in progress)
The latest study carried out with MAESTRO platform is a surgical training task in which novice and experience surgeons performed the extraction of the gallbladder (known as laparoscopic Cholecystectomy). Such operation is a surgical procedure to remove the gallbladder - a pear-shaped organ that sits just below your liver on the upper right side of the human abdomen. The main function of Gallbladder is to collect and stores bile a digestive fluid produced in your liver. In this study, a porcine live and gallbladder are used for practical reasons.
The aim of this study is to determine the effect of increasing cognitive workload levels have on junior or experienced surgeons during a simulated operation (laparoscopic cholecystectomy). Data from different sensing sources such as EEG, ECG, EMG, fNIRS and body temperature is collected throughout the operation.
Surgical trainees undertake a high fidelity simulated operation (laparoscopic cholecystectomy) on a porcine model whilst wearing the MAESTRO sensors identified from the pilot study. Additionally, Kinect cameras record motion and environment data. The procedure data is also recorded via a screen recorder. These are all synchronised onto the bespoke MAESTRO setup.
Figure 3.7.1. Surgical Operation: laparoscopic cholecystectomy performed at St. Mary’s Hospital and data collected by MAESTRO on real-time conditions.
Until today, three participants have completed the laparoscopic cholecystectomy and data has also been collected. A number of 12 further operations are expected in the next couple of months based on the availability of the invited surgeons.
This study is currently in progress. At this stage, data collected from the camera system and the different sensing sources is being curated and processed. This step will facilitate the analysis of data and used to feed the suggested machine learning algorithms.
Even though this study is still in process, significant conclusions can be already drawn. First, collected data has been analysed and preliminary results show its consistency. Secondly, the suggested task has demonstrated to be of significant meaning to measure and evaluate the different factors that might contribute to an increase in CWL under different distractors. This will contribute to the statistical analysis of data and implementation of near-to-real-time machine learning.