The virtual reality classroom: a randomized control trial of medical student knowledge of postpartum hemorrhage emergency management.

Objective: To investigate the use of a virtual reality learning environment (VRLE) to enhance medical student knowledge of postpartum hemorrhage (PPH) emergency management and insertion of a postpartum balloon. Methods: A randomized control trial involving medical students from University College Dublin, Ireland. Participants were randomly allocated to the intervention group (VRLE tutorial) or control group (PowerPoint tutorial on the same topic). All participants completed pre-learning experience and post-learning experience surveys. Both groups were timed and assessed on postpartum balloon insertion technique on a model pelvis. The primary outcome was assessment of student knowledge. Secondary outcomes included confidence levels, time taken to complete the task, technique assessment, satisfaction with the learning environment, and side effects of VR. Results: Both learning experiences significantly (p < 0.001) enhanced student performance on the post-learning experience multiple choice questionnaire, with no difference between the intervention and control groups. In the intervention group, time for task completion was significantly less compared to the control group (1-2 min vs. 2-3 min, p = 0.039). Both learning experiences significantly (p < 0.001) enhanced student confidence, with no significant difference between intervention and control groups. 100% of the students using the VRLE enjoyed the experience, and 82.4% were very likely to recommend use of VRLE in medical education. 94.1% of the students felt the VRLE was beneficial over didactic teaching. Conclusion: Receiving formal instruction, regardless of format, enhances students' knowledge and confidence of the topic covered. Students who received instruction via the VRLE assembled the postpartum balloon faster than students who received didactic teaching. VR may be beneficial in teaching hands-on procedural skills in obstetrics and gynecology education.

Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review.

Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.

Dataset of user interactions across four large pilots on the use of augmented reality in learning experiences.

Augmented Reality in education can support students in a wide range of cognitive tasks-fostering understanding, remembering, applying, analysing, evaluating, and creating learning-relevant information more easily. It can help keep up engagement, and it can render learning more fun. Within the framework of a multi-year investigation encompassing primary and secondary schools across Europe, the ARETE project developed several Augmented Reality applications, providing tools for user interaction and data collection in the education sector. The project developed innovative AR learning technology and methodology, validating these in four comprehensive pilot studies, in total involving more than 2,900 students and teachers. Each pilot made use of a different Augmented Reality application covering specific subjects (English literacy skills, Mathematics and Geography, Positive Behaviour, plus, additionally, an Augmented Reality authoring tool applied in a wide range of subjects). In this paper, we introduce the datasets collected during the pilots, describe how the data enabled the validation of the technology, and how the approach chosen could enhance existing augmented reality applications in data exploration and modelling.

Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation.

Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection.

Virtual reality learning: A randomized controlled trial assessing medical student knowledge of fetal development.

OBJECTIVE: To investigate whether a virtual reality learning environment (VRLE) enhanced student understanding and knowledge compared with a traditional tutorial. METHOD: A randomized controlled trial involving medical students from University College Dublin, Ireland. Participants were assigned to an intervention (VRLE involving a 15-min learning experience on the stages of fetal development) or control (PowerPoint tutorial on the same topic) group. Multiple choice questionnaires (MCQs) assessed knowledge at three time points: preintervention, immediately postintervention, and 1 week postintervention. Primary outcomes were differences in MCQ knowledge scores postintervention between groups. Secondary outcomes included attitudes on the learning experience assessed using the Student Satisfaction and Self-Confidence in Learning Scale (SCLS) and the Virtual Reality Design Scale (VRDS). RESULTS: No statistically significant between-group differences were found in the primary outcome assessing postintervention knowledge scores. Within-group differences in knowledge scores were significant among the three time points for both the intervention (P < 0.01 [95% confidence interval, 5.33-6.19]) and control (P = 0.02 [95% confidence interval, 5.74-6.49]) groups. Mean levels of satisfaction and self-confidence in learning were higher in the intervention group compared with the control group: 54.2 (standard deviation, 7.5) and 50.5 (standard deviation, 7.2), respectively (P = 0.21). CONCLUSION: VRLEs are a learning tool that can support knowledge development.

Virtual reality in midwifery education: A mixed methods study to assess learning and understanding.

BACKGROUND: Virtual reality learning environments (VRLEs) are a potentially valuable learning tool that have recently increased in popularity due to widespread availability and decreased cost. VRLEs can provide an immersive learning environment that increases the understanding of three-dimensional relationships between anatomical structures. However, there is a paucity of evidence in the literature supporting its use within Midwifery education. OBJECTIVES: To explore the effectiveness of a VRLE as an educational tool in midwifery education. SETTING: A large University in Ireland, with institutional ethical approval. PARTICIPANTS: Undergraduate and graduate degree midwifery students. DESIGN: A descriptive qualitative and quantitative study was carried out. Data collection was carried out between September 2020 and March 2021. METHODS: Participants underwent a VRLE lesson based on the topic of fetal lie, position, and presentation in pregnancy. A multiple-choice questionnaire was used to quantitatively evaluate knowledge before and immediately after the intervention, and knowledge retention after one week. Qualitative data was collected using open-ended questions in the questionnaire. The primary outcome was a difference in pre- and post-intervention knowledge scores. Data was analysed using repeated measures one-way ANOVA. Qualitative data was analysed using thematic analysis and simple content analysis. All students participated in the quantitative and qualitative components of the study. Secondary outcomes included participant satisfaction and self-confidence in learning which were analysed using thematic analysis. The side effect profile of the virtual reality device was also explored using open-ended questions in the questionnaire. RESULTS: Forty-one midwifery students participated in the study, with a 100 % participation and response rate. Repeated measures one-way ANOVA revealed no statistically significant differences in knowledge scores pre- and post-intervention. Participants rated high satisfaction and self-confidence scores with regard to the VRLE as a learning modality. Side effects most commonly experienced by participants included dizziness (49 %), disorientation (30 %) and symptoms similar to motion sickness (32 %). The following themes were identified: "Learning in 3D", "The Power of Visual Learning", "The value of Educational Technology", "Learning can be fun and enjoyable". CONCLUSIONS: This study showed that the VRLE had no impact on knowledge gain, though high levels of satisfaction and self-confidence indicate a positive response to the VRLE. VRLEs are a potentially valuable learning tool to help enhance the student learning experience, promoting increased engagement, satisfaction, and self-confidence with the learning material.

Phenotyping for waterlogging tolerance in crops: current trends and future prospects.

Yield losses to waterlogging are expected to become an increasingly costly and frequent issue in some regions of the world. Despite the extensive work that has been carried out examining the molecular and physiological responses to waterlogging, phenotyping for waterlogging tolerance has proven difficult. This difficulty is largely due to the high variability of waterlogging conditions such as duration, temperature, soil type, and growth stage of the crop. In this review, we highlight use of phenotyping to assess and improve waterlogging tolerance in temperate crop species. We start by outlining the experimental methods that have been utilized to impose waterlogging stress, ranging from highly controlled conditions of hydroponic systems to large-scale screenings in the field. We also describe the phenotyping traits used to assess tolerance ranging from survival rates and visual scoring to precise photosynthetic measurements. Finally, we present an overview of the challenges faced in attempting to improve waterlogging tolerance, the trade-offs associated with phenotyping in controlled conditions, limitations of classic phenotyping methods, and future trends using plant-imaging methods. If effectively utilized to increase crop resilience to changing climates, crop phenotyping has a major role to play in global food security.

Capturing crop adaptation to abiotic stress using image-based technologies.

Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.

Learning Outcomes of Immersive Technologies in Health Care Student Education: Systematic Review of the Literature.

BACKGROUND: There is a lack of evidence in the literature regarding the learning outcomes of immersive technologies as educational tools for teaching university-level health care students. OBJECTIVE: The aim of this review is to assess the learning outcomes of immersive technologies compared with traditional learning modalities with regard to knowledge and the participants' learning experience in medical, midwifery, and nursing preclinical university education. METHODS: A systematic review was conducted according to the Cochrane Collaboration guidelines. Randomized controlled trials comparing traditional learning methods with virtual, augmented, or mixed reality for the education of medicine, nursing, or midwifery students were evaluated. The identified studies were screened by 2 authors independently. Disagreements were discussed with a third reviewer. The quality of evidence was assessed using the Medical Education Research Study Quality Instrument (MERSQI). The review protocol was registered with PROSPERO (International Prospective Register of Systematic Reviews) in April 2020. RESULTS: Of 15,627 studies, 29 (0.19%) randomized controlled trials (N=2722 students) were included and evaluated using the MERSQI tool. Knowledge gain was found to be equal when immersive technologies were compared with traditional learning modalities; however, the learning experience increased with immersive technologies. The mean MERSQI score was 12.64 (SD 1.6), the median was 12.50, and the mode was 13.50. Immersive technology was predominantly used to teach clinical skills (15/29, 52%), and virtual reality (22/29, 76%) was the most commonly used form of immersive technology. Knowledge was the primary outcome in 97% (28/29) of studies. Approximately 66% (19/29) of studies used validated instruments and scales to assess secondary learning outcomes, including satisfaction, self-efficacy, engagement, and perceptions of the learning experience. Of the 29 studies, 19 (66%) included medical students (1706/2722, 62.67%), 8 (28%) included nursing students (727/2722, 26.71%), and 2 (7%) included both medical and nursing students (289/2722, 10.62%). There were no studies involving midwifery students. The studies were based on the following disciplines: anatomy, basic clinical skills and history-taking skills, neurology, respiratory medicine, acute medicine, dermatology, communication skills, internal medicine, and emergency medicine. CONCLUSIONS: Virtual, augmented, and mixed reality play an important role in the education of preclinical medical and nursing university students. When compared with traditional educational modalities, the learning gain is equal with immersive technologies. Learning outcomes such as student satisfaction, self-efficacy, and engagement all increase with the use of immersive technology, suggesting that it is an optimal tool for education.

Fully Automated Breast Density Segmentation and Classification Using Deep Learning.

Breast density estimation with visual evaluation is still challenging due to low contrast and significant fluctuations in the mammograms' fatty tissue background. The primary key to breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; nevertheless, most of them are not fully automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. This study intends to develop a fully automated and digitalized breast tissue segmentation and classification using advanced deep learning techniques. The conditional Generative Adversarial Networks (cGAN) network is applied to segment the dense tissues in mammograms. To have a complete system for breast density classification, we propose a Convolutional Neural Network (CNN) to classify mammograms based on the standardization of Breast Imaging-Reporting and Data System (BI-RADS). The classification network is fed by the segmented masks of dense tissues generated by the cGAN network. For screening mammography, 410 images of 115 patients from the INbreast dataset were used. The proposed framework can segment the dense regions with an accuracy, Dice coefficient, Jaccard index of 98%, 88%, and 78%, respectively. Furthermore, we obtained precision, sensitivity, and specificity of 97.85%, 97.85%, and 99.28%, respectively, for breast density classification. This study's findings are promising and show that the proposed deep learning-based techniques can produce a clinically useful computer-aided tool for breast density analysis by digital mammography.

Identification of the Students Learning Process During Education Robotics Activities.

This paper presents the design of an assessment process and its outcomes to investigate the impact of Educational Robotics activities on students' learning. Through data analytics techniques, the authors will explore the activities' output from a pedagogical and quantitative point of view. Sensors are utilized in the context of an Educational Robotics activity to obtain a more effective robot-environment interaction. Pupils work on specific exercises to make their robot smarter and to carry out more complex and inspirational projects: the integration of sensors on a robotic prototype is crucial, and learners have to comprehend how to use them. In the presented study, the potential of Educational Data Mining is used to investigate how a group of primary and secondary school students, using visual programming (Lego Mindstorms EV3 Education software), design programming sequences while they are solving an exercise related to an ultrasonic sensor mounted on their robotic artifact. For this purpose, a tracking system has been designed so that every programming attempt performed by students' teams is registered on a log file and stored in an SD card installed in the Lego Mindstorms EV3 brick. These log files are then analyzed using machine learning techniques (k-means clustering) in order to extract different patterns in the creation of the sequences and extract various problem-solving pathways performed by students. The difference between problem-solving pathways with respect to an indicator of early achievement is studied.