The influence of virtual reality simulation on surgical residents' heart rate during an assessment of arthroscopic technical skills: A prospective, paired observational study.

HYPOTHESIS: To demonstrate that a virtual reality (VR) simulation training program reduces heart rate variability during an assessment of surgical trainees' technical skills in arthroscopy. STUDY DESIGN: Prospective observational matched study. MATERIALS & METHODS: Thirty-six orthopaedic surgery residents, new to arthroscopy, received standard training in arthroscopic knee surgery, supplemented by additional monthly training for 6months on a VR simulator for 16 of them. At inclusion, the 2 groups (VR and NON-VR) answered a questionnaire and performed a meniscectomy on a VR simulator. After 6months of training, two independent trainers blinded to the inclusion arms evaluated the technical skills of the two groups during meniscectomies on a model and on an anatomical subject. Heart rate variability (HRV) was measured using a wireless heart rate monitor during baseline, VR training, and assessment. RESULTS: After removing incomplete data, the analysis focused on 10 VR residents matched at inclusion with 10 NON-VR residents. The VR group had a significantly lower heart rate at the final assessment (p=0.02) and lower overall HRV (p=0.05). The low/high frequency ratio (LF/HF) was not significantly different between the groups (1.84 vs 2.05, p=0.66) but the before-after training comparison showed a greater decrease in this ratio in the VR group compared to the NON-VR group -0.76 (-41%) vs -0.08 (-4%). CONCLUSION: This study demonstrates a significant difference in heart rate variability between trained residents versus untrained residents during the final assessment of their technical skills at 6months. It appears that improving stress management should be an integral part of training programs in arthroscopic surgery. CLINICAL INTEREST: VR simulators in arthroscopy could improve non-technical skills such as heart rate variability, from the perspective of accountability. LEVEL OF EVIDENCE: III.

Virtual reality simulation training improve diagnostic knee arthroscopy and meniscectomy skills: a prospective transfer validity study.

PURPOSE: Limited data exist on the actual transfer of skills learned using a virtual reality (VR) simulator for arthroscopy training because studies mainly focused on VR performance improvement and not on transfer to real word (transfer validity). The purpose of this single-blinded, controlled trial was to objectively investigate transfer validity in the context of initial knee arthroscopy training. METHODS: For this study, 36 junior resident orthopaedic surgeons (postgraduate year one and year two) without prior experience in arthroscopic surgery were enrolled to receive standard knee arthroscopy surgery training (NON-VR group) or standard training plus training on a hybrid virtual reality knee arthroscopy simulator (1 h/month) (VR group). At inclusion, all participants completed a questionnaire on their current arthroscopic technical skills. After 6 months of training, both groups performed three exercises that were evaluated independently by two blinded trainers: i) arthroscopic partial meniscectomy on a bench-top knee simulator; ii) supervised diagnostic knee arthroscopy on a cadaveric knee; and iii) supervised knee partial meniscectomy on a cadaveric knee. Training level was determined with the Arthroscopic Surgical Skill Evaluation Tool (ASSET) score. RESULTS: Overall, performance (ASSET scores) was better in the VR group than NON-VR group (difference in the global scores: p < 0.001, in bench-top meniscectomy scores: p = 0.03, in diagnostic knee arthroscopy on a cadaveric knee scores: p = 0.04, and in partial meniscectomy on a cadaveric knee scores: p = 0.02). Subgroup analysis by postgraduate year showed that the year-one NON-VR subgroup performed worse than the other subgroups, regardless of the exercise. CONCLUSION: This study showed the transferability of the technical skills acquired by novice residents on a hybrid virtual reality simulator to the bench-top and cadaveric models. Surgical skill acquired with a VR arthroscopy surgical simulator might safely improve arthroscopy competences in the operating room, also helping to standardise resident training and follow their progress.

"Stand-up straight!": human pose estimation to evaluate postural skills during orthopedic surgery simulations.

PURPOSE: Surgery simulators can be used to learn technical and non-technical skills and, to analyse posture. Ergonomic skill can be automatically detected with a Human Pose Estimation algorithm to help improve the surgeon's work quality. The objective of this study was to analyse the postural behaviour of surgeons and identify expertise-dependent movements. Our hypothesis was that hesitation and the occurrence of surgical instruments interfering with movement (defined as interfering movements) decrease with expertise. MATERIAL AND METHODS: Sixty surgeons with three expertise levels (novice, intermediate, and expert) were recruited. During a training session using an arthroscopic simulator, each participant's movements were video-recorded with an RGB camera. A modified OpenPose algorithm was used to detect the surgeon's joints. The detection frequency of each joint in a specific area was visualized with a heatmap-like approach and used to calculate a mobility score. RESULTS: This analysis allowed quantifying surgical movements. Overall, the mean mobility score was 0.823, 0.816, and 0.820 for novice, intermediate and expert surgeons, respectively. The mobility score alone was not enough to identify postural behaviour differences. A visual analysis of each participants' movements highlighted expertise-dependent interfering movements. CONCLUSION: Video-recording and analysis of surgeon's movements are a non-invasive approach to obtain quantitative and qualitative ergonomic information in order to provide feedback during training. Our findings suggest that the interfering movements do not decrease with expertise but differ in function of the surgeon's level.

Assessing the robustness of clinical trials by estimating Jadad's score using artificial intelligence approaches.

BACKGROUND: Clinical trials are essential in medical science and are currently the most robust strategy for evaluating the effectiveness of a treatment. However, some of these studies are less reliable than others due to flaws in their design. Assessing the robustness of a clinical trial can be a very complex and time-consuming task, with factors such as randomization, masking and the description of withdrawals needing to be considered. METHOD: We built a program based on artificial intelligence (AI) approaches, designed to assess the robustness of a clinical trial by estimating its Jadad's score. The program is composed of five Recursive Neural Networks (RNN), each of them trained to spot one specific item constituting the Jadad's scale. After training, the algorithm was tested on two different validation sets (one from the original database: 35% of this database was used for validation and 65% for training; one composed of 10 articles, out of the original database, for which the Jadad's score has been computed by each contributor of this study). RESULT: After training, the algorithm achieved a mean accuracy of 96,2% (ranging from 93% to 98%) and a mean area under the curve (AUC) of 96% (ranging from 95% to 97%) on the first validation dataset. These results indicate good feature detection capacity for each of the five RNN. On the second validation dataset the algorithm extracted 100% of the item to retrieve for 70% of the articles and between 66% and 75% for 30% of the articles. Overall 85% of the items present in the second validation dataset were correctly extracted. None of the extracted items was misclassified. CONCLUSION: We developed a program that can automatically estimate the Jadad's score of a clinical trial with a good accuracy. Automating the assessment of this metric could be very useful in a systematic review of the literature and will probably save clinicians time.