Virtual reality-based simulation improves rotator cuff repair skill: A randomized transfer validity study.

Background: Although virtual reality (VR) simulators have demonstrated their efficiency for basic technical skill training of healthcare professionals, validation for more complex and sequential procedures, especially in arthroscopic surgery, is still warranted. We hypothesized that the VR-based training simulation improves arthroscopic cuff repair skills when transferred to realistic visual and haptic conditions.

Hypothesis: VR-based training simulation improves arthroscopic cuff repair skills when transferred to realistic visual and haptic conditions.

Comparison of the OpenPose system and the reference optoelectronic system for gait analysis of lower-limb angular parameters in children.

Introduction: Quantitative Gait Analysis (QGA) is the gold-standard for detailed study of lower-limb movement, angles and forces, especially in pediatrics, providing a decision aid for treatment and for assessment of results. However, widespread use of QGA is hindered by the need for specific equipment and trained personnel and high costs. Recently, the OpenPose system used algorithms for 2D video movement analysis, to determine joint points and angles without any supplementary equipment or great expertise.

Automated assessment of non-technical skills by heart-rate data.

Purpose: Observer-based scoring systems, or automatic methods, based on features or kinematic data analysis, are used to perform surgical skill assessments. These methods have several limitations, observer-based ones are subjective, and the automatic ones mainly focus on technical skills or use data strongly related to technical skills to assess non-technical skills. In this study, we are exploring the use of heart-rate data, a non-technical-related data, to predict values of an observer-based scoring system thanks to random forest regressors.

Generalisation capabilities of machine-learning algorithms for the detection of the subthalamic nucleus in micro-electrode recordings.

Purpose: Micro-electrode recordings (MERs) are a key intra-operative modality used during deep brain stimulation (DBS) electrode implantation, which allow for a trained neurophysiologist to infer the anatomy in which the electrode is placed. As DBS targets are small, such inference is necessary to confirm that the electrode is correctly positioned. Recently, machine learning techniques have been used to augment the neurophysiologist's capability.