Ahmed Ghazi

INTRODUCTION: Robot-assisted surgery is becoming increasingly adopted by multiple surgical specialties. There is evidence of inherent risks of utilising new technologies that are unfamiliar early in the learning curve. The development of standardised and validated training programmes is crucial to deliver safe introduction. In this review, we aim to evaluate the current evidence and opportunities to integrate novel technologies into modern digitalised robotic training curricula. METHODS: A systematic literature review of the current evidence for novel technologies in surgical training was conducted online and relevant publications and information were identified. Evaluation was made on how these technologies could further enable digitalisation of training. RESULTS: Overall, the quality of available studies was found to be low with current available evidence consisting largely of expert opinion, consensus statements and small qualitative studies. The review identified that there are several novel technologies already being utilised in robotic surgery training. There is also a trend towards standardised validated robotic training curricula. Currently, the majority of the validated curricula do not incorporate novel technologies and training is delivered with more traditional methods that includes centralisation of training services with wet laboratories that have access to cadavers and dedicated training robots. CONCLUSIONS: Improvements to training standards and understanding performance data have good potential to significantly lower complications in patients. Digitalisation automates data collection and brings data together for analysis. Machine learning has potential to develop automated performance feedback for trainees. Digitalised training aims to build on the current gold standards and to further improve the 'continuum of training' by integrating PBP training, 3D-printed models, telementoring, telemetry and machine learning.

OBJECTIVE: To validate robot-assisted surgery skills acquisition using an augmented reality (AR)-based module for urethrovesical anastomosis (UVA). METHODS: Participants at three institutions were randomised to a Hands-on Surgical Training (HoST) technology group or a control group. The HoST group was given procedure-based training for UVA within the haptic-enabled AR-based HoST environment. The control group did not receive any training. After completing the task, the control group was offered to cross over to the HoST group (cross-over group). A questionnaire administered after HoST determined the feasibility and acceptability of the technology. Performance of UVA using an inanimate model on the daVinci Surgical System (Intuitive Surgical Inc., Sunnyvale, CA, USA) was assessed using a UVA evaluation score and a Global Evaluative Assessment of Robotic Skills (GEARS) score. Participants completed the National Aeronautics and Space Administration Task Load Index (NASA TLX) questionnaire for cognitive assessment, as outcome measures. A Wilcoxon rank-sum test was used to compare outcomes among the groups (HoST group vs control group and control group vs cross-over group). RESULTS: A total of 52 individuals participated in the study. UVA evaluation scores showed significant differences in needle driving (3.0 vs 2.3; P = 0.042), needle positioning (3.0 vs 2.4; P = 0.033) and suture placement (3.4 vs 2.6; P = 0.014) in the HoST vs the control group. The HoST group obtained significantly higher scores (14.4 vs 11.9; P 0.012) on the GEARS. The NASA TLX indicated lower temporal demand and effort in the HoST group (5.9 vs 9.3; P = 0.001 and 5.8 vs 11.9; P = 0.035, respectively). In all, 70% of participants found that HoST was similar to the real surgical procedure, and 75% believed that HoST could improve confidence for carrying out the real intervention. CONCLUSION: Training in UVA in an AR environment improves technical skill acquisition with minimal cognitive demand.

Google Earth provides an open source, easy to access and cost free image data that support map interest community. Therefore, depending of this community on Google Earth, grows up day by day. More than simply providing locational information, Google Earth allows users to add their own content such as photos or descriptions of areas or landmarks. They can also extrapolate information from the satellite imagery obtained by digitizing areas of interest and exporting them for use elsewhere. As such, the application has found a strong following not only in explorers and navigators but also in classrooms all over the world. However, this popularity of Google Earth is not an indication of its accuracy. The aim of this research is to estimate the Google Earth horizontal and vertical accuracy in Khartoum State so as to evaluate this free source of data. This was carried out by comparing Google Earth measured coordinates of points with geodetic Global Positional System (GPS) receiver coordinates over sample of 16 check points located in Khartoum State. Since GPS provide accurate measurement of coordinates on the same ellipsoid as Google Earth, it was used to check the accuracy of Google Earth. Root Mean Square Error (RMSE) was computed for horizontal coordinates and was found to be 1.59m. For height measurement RMSE was computed to be 1.7m. For the research purposes and to pursue the changes occurred while Google Earth images updated, it was noted that the positional accuracy was changed and improved, but the elevation is still as it were before update.