Abstract
BACKGROUND: Basic skills training in laparoscopic high-fidelity simulators (LHFS) improves laparoscopic skills. However, since LHFS are expensive, their availability is limited. The aim of this study was to assess whether automated video analysis of low-cost BlackBox laparoscopic training could provide an alternative to LHFS in basic skills training. METHODS: Medical students volunteered to participate during their surgical semester at the Karolinska University Hospital. After written informed consent, they performed two laparoscopic tasks (PEG-transfer and precision-cutting) on a BlackBox trainer. All tasks were videotaped and sent to MPLSC for automated video analysis, generating two parameters (Pl and Prtcl_tot) that assess the total motion activity. The students then carried out final tests on the MIST-VR simulator. This study was a European collaboration among two simulation centers, located in Sweden and Greece, within the framework of ACS-AEI. RESULTS: 31 students (19 females and 12 males), mean age of 26.2 ± 0.8 years, participated in the study. However, since two of the students completed only one of the three MIST-VR tasks, they were excluded. The three MIST-VR scores showed significant positive correlations to both the Pl variable in the automated video analysis of the PEG-transfer (RSquare 0.48, P < 0.0001; 0.34, P = 0.0009; 0.45, P < 0.0001, respectively) as well as to the Prtcl_tot variable in that same exercise (RSquare 0.42, P = 0.0002; 0.29, P = 0.0024; 0.45, P < 0.0001). However, the correlations were exclusively shown in the group with less PC gaming experience as well as in the female group. CONCLUSIONS: Automated video analysis provides accurate results in line with those of the validated MIST-VR. We believe that a more frequent use of automated video analysis could provide an extended value to cost-efficient laparoscopic BlackBox training. However, since there are gender-specific as well as PC gaming experience differences, this should be taken in account regarding the value of automated video analysis.