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Background Initial learning curves are potentially shorter in robotic-assisted surgery (RAS) than in conventional laparoscopic surgery (LS). There is little evidence to support this claim. Furthermore, there is limited evidence how skills from LS transfer to RAS. Methods A randomized controlled, assessor blinded crossover study to compare how RAS naïve surgeons (n = 40) performed linear-stapled side-to-side bowel anastomoses in an in vivo porcine model with LS and RAS. Technique was rated using the validated anastomosis objective structured assessment of skills (A-OSATS) score and the conventional OSATS score. Skill transfer from LS to RAS was measured by comparing the RAS performance of LS novices and LS experienced surgeons. Mental and physical workload was measured with the NASA-task load index (NASA-Tlx) and the Borg-scale. Outcomes In the overall cohort, there were no differences between RAS and LS for surgical performance (A-OSATS, time, OSATS). Surgeons that were naïve in both LS and RAS had significantly higher A-OSATS scores in RAS (Mean (Standard deviation (SD)): LS: 48.0 ± 12.1; RAS: 52.0 ± 7.5); p = 0.044) mainly deriving from better bowel positioning (LS: 8.7 ± 1.4; RAS: 9.3 ± 1.0; p = 0.045) and closure of enterotomy (LS: 12.8 ± 5.5; RAS: 15.6 ± 4.7; p = 0.010). There was no statistically significant difference in how LS novices and LS experienced surgeons performed in RAS [Mean (SD): novices: 48.9 ± 9.0; experienced surgeons: 55.9 ± 11.0; p = 0.540]. Mental and physical demand was significantly higher after LS. Conclusion The initial performance was improved for RAS versus LS for linear stapled bowel anastomosis, whereas workload was higher for LS. There was limited transfer of skills from LS to RAS.

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Minimally Invasive Surgery (MIS) techniques have gained rapid popularity among surgeons since they offer significant clinical benefits including reduced recovery time and diminished post-operative adverse effects. However, conventional endoscopic systems output monocular video which compromises depth perception, spatial orientation and field of view. Suturing is one of the most complex tasks performed under these circumstances. Key components of this tasks are the interplay between needle holder and the surgical needle. Reliable 3D localization of needle and instruments in real time could be used to augment the scene with additional parameters that describe their quantitative geometric relation, e.g. the relation between the estimated needle plane and its rotation center and the instrument. This could contribute towards standardization and training of basic skills and operative techniques, enhance overall surgical performance, and reduce the risk of complications. The paper proposes an Augmented Reality environment with quantitative and qualitative visual representations to enhance laparoscopic training outcomes performed on a silicone pad. This is enabled by a multi-task supervised deep neural network which performs multi-class segmentation and depth map prediction. Scarcity of labels has been conquered by creating a virtual environment which resembles the surgical training scenario to generate dense depth maps and segmentation maps. The proposed convolutional neural network was tested on real surgical training scenarios and showed to be robust to occlusion of the needle. The network achieves a dice score of 0.67 for surgical needle segmentation, 0.81 for needle holder instrument segmentation and a mean absolute error of 6.5 mm for depth estimation.