360° 3D virtual reality operative video for the training of residents in neurosurgery.

OBJECTIVE: Training of residents is an essential but time-consuming and costly task in the surgical disciplines. During the coronavirus disease 2019 pandemic, surgical education became even more challenging because of the reduced caseload due to the increased shift to corona care. In this context, augmented 360° 3D virtual reality (VR) videos of surgical procedures enable effective off-site training through virtual participation in the surgery. The goal of this study was to establish and evaluate 360° 3D VR operative videos for neurosurgical training. METHODS: Using a 360° camera, the authors recorded three standard neurosurgical procedures: a lumbar discectomy, brain metastasis resection, and clipping of an aneurysm. Combined with the stereoscopic view of the surgical microscope, 7- to 10-minute 360° 3D VR videos augmented with annotations, overlays, and commentary were created. These videos were then presented to the neurosurgical residents at the authors' institution using a head-mounted display. Before viewing the videos, the residents were asked to fill out a questionnaire indicating their VR experience and self-assessment of surgical skills regarding the specific procedure. After watching the videos, the residents completed another questionnaire to evaluate their quality and usefulness. The parameters were scaled with a 5-point Likert scale. RESULTS: Twenty-two residents participated in this study. The mean years of experience of the participants in neurosurgery was 3.2 years, ranging from the 1st through the 7th year of training. Most participants (86.4%) had no or less than 15 minutes of VR experience. The overall quality of the videos was rated good to very good. Immersion, the feeling of being in the operating room, was high, and almost all participants (91%) stated that 360° VR videos provide a useful addition to the neurosurgical training. VR sickness was negligible in the cohort. CONCLUSIONS: In this study, the authors demonstrated the feasibility and high acceptance of augmented 360° 3D VR videos in neurosurgical training. Augmentation of 360° videos with complementary and interactive content has the potential to effectively support trainees in acquiring conceptual knowledge. Further studies are necessary to investigate the effectiveness of their use in improving surgical skills.

Stereoscopic Monitoring: A Promising Strategy to Advance Diagnostic and Prediction of Air Pollution

Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground and to support attribution of sources. We highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to reduce the uncertainties and to advance diagnostic understanding and prediction of air pollution. There are three reasons for the implementation of stereoscopic monitoring: 1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions;2) satellite retrievals of air pollutants are widely used in air pollution studies, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles;and 3) air quality modeling and forecasting require 3D observational constraints. We call on researchers and policymakers to establish stereoscopic monitoring networks and share monitoring data to better characterize the formation of air pollution, optimize air quality management, and protect human health. Future directions for advancing monitoring and modeling/predicting air pollution are also discussed.

Automatic Mobile Warning System against People with Elevated Body Temperature

The paper proposes a system that allows for the automatic detection of people with elevated body temperature and estimates distance from them using a smartphone-type device and a single mobile thermal camera. The algorithm automatically finds and selects humans with the highest temperature, and tracks changes in their position in an image sequence. On the basis of the change in the position of the human head in the image, in subsequent frames, the algorithm estimates the distance between camera and human. Owing to the use of fast machine-learning methods, the proposed system can immediately alert the user about the presence of a people with an elevated temperature at a distance of 1–3 m as soon as it appears in the field of view of the camera. The effectiveness of the algorithm was assessed as the ratio of correct distance classifications in the test image set to the total number of test images. Values ranging from 73% to 100% were obtained for over 4000 images of humans at different distances. The proposed method allows for the quick and completely automatic warning aboutt people with elevated temperature, and can be used in popular Android mobile devices.