[Development of a Head Radiography Simulator System Using Pose Estimation Techniques].

PURPOSE: This study proposes a system that can simulate head radiography by combining a technique for estimating human posture from moving images (hereafter referred to as "pose estimation technique") and use of two cameras capable of acquiring RGB images to determine body position during positioning. METHODS: The angles of the median sagittal plane (MS), axial plane (AX), and orbitomeatal baseline (OM) were obtained using the pose estimation technique from frontal and lateral images captured after positioning. The resulting radiographs were displayed according to the results. RESULTS: The head tilt during positioning could be determined based on the coordinate data of feature points acquired using the pose estimation technique. In an imaging experiment using a simulated human patient, errors increased as head tilt increased; however, the mean error values in each axis were 0.9° for MS, 0.8° for AX, and 1.5°for OM, when the patient was correctly positioned. CONCLUSION: The pose estimation technique can assist in evaluating positioning accuracy in radiography and is expected to be used as a potential simulator system.

Corrigendum: Tracheal intubation in patients with Pierre Robin sequence: development, application, and clinical value based on a 3-dimensional printed simulator.

[This corrects the article DOI: 10.3389/fphys.2023.1292523.].

A structured curriculum for the acquisition of basic surgical endoscopic skills for surgical residents and quantification of skills improvement.

Introduction: New strategies and methods are needed to ensure that new generations can train and acquire surgical skills in a safe environment. Materials and methods: From January 2020 to October 2020, we performed a single centre, prospective observational cohort study. 19 participants (15 students, 4 residents) enrolled and 16 participants (13 students, 3 residents) successfully completed the curriculum. We performed a quantitative data analysis to evaluate its effectiveness in gaining and improving basic surgical endoscopic skills. Results: The time for single knot tying pre-, mid-, and post-training was reduced significantly, the average time (sec) decreased by 79.5 % (p < 0.001), the total linear distance (cm) by 74.5 % (p < 0.001) and the total angular distance (rad) by 71.7 % (p < 0.001). The average acceleration (mm/s2) increased by 20 % (p = 0.041). Additionally, the average speed increased by 23.5 % (p < 0.001), while motion smoothness (m/s3) increased by 20.4 % (p = 0.02). Conclusion: The obtained performance scores showed a significant increase in participants improving their basic surgical performance skills on the endoscopic simulator. This curriculum can be easily implemented in any surgical specialty as part of the residency training curriculum before first exposure in the operation room. All 16 participants recommended the implementation of such simulator training in their surgical training curriculum.

An ovine knee simulator: description and proof of concept.

Aims: The ovine stifle is an established model for evaluation of knee treatments, such as meniscus replacement. This study introduces a novel ovine gait simulator for pre-testing of surgical treatments prior to in vivo animal trials. Furthermore, we describe a pilot study that assessed gait kinematics and contact pressures of native ovine stifle joints and those implanted with a novel fiber-matrix reinforced polyvinyl alcohol-polyethylene glycol (PVA-PEG) hydrogel meniscus to illustrate the efficacy of the simulator. Methods: The gait simulator controlled femoral flexion-extension and applied a 980N axial contact force to the distal tibia, whose movement was guided by the natural ligaments. Five right ovine stifle joints were implanted with a PVA-PEG total medial meniscus replacement, fixed to the tibia via transosseous tunnels and interference screws. Six intact and five implanted right ovine stifle joints were tested for 500 k gait cycles at 1.55 Hz. Implanted stifle joint contact pressures and kinematics in the simulator were compared to the intact group. Contact pressures were measured at 55° flexion using pressure sensitive film inserted sub-meniscally. 3D kinematics were measured optically across two 30-s captures. Results: Peak contact pressures in intact stifles were 3.6 ± 1.0 MPa and 6.0 ± 2.1 MPa in the medial and lateral condyles (p < 0.05) and did not differ significantly from previous studies (p > 0.4). Medial peak implanted pressures were 4.3 ± 2.2 MPa (p > 0.4 versus intact), while lateral peak pressures (9.4 ± 0.8 MPa) were raised post medial compartment implantation (p < 0.01). The range of motion for intact joints was flexion/extension 37° ± 1°, varus/valgus 1° ± 1°, external/internal rotation 5° ± 3°, lateral/medial translation 2 ± 1 mm, anterior/posterior translation 3 ± 1 mm and distraction/compression 1 ± 1 mm. Ovine joint kinematics in the simulator did not differ significantly from published in vivo data for the intact group, and the intact and implanted groups were comparable (p > 0.01), except for in distraction-compression (p < 0.01). Conclusion: These findings show correspondence of the ovine simulator kinematics with in vivo gait parameters. The efficacy of the simulator to evaluate novel treatments was demonstrated by implanting a PVA-PEG hydrogel medial meniscal replacement, which restored the medial peak contact pressures but not lateral. This novel simulator may enable future work on the development of surgical procedures, derisking subsequent work in live animals.

An Indigenous Suction-assisted Laryngoscopy and Airway Decontamination Simulation System.

Background: Suction-assisted laryngoscopy and airway decontamination (SALAD) is a new modality and training manikins are quite costly. Few modifications have been described with their pluses and minuses. We describe a low-cost simulator that replicates fluid contamination of the airway at various flow rates and allows the practice of SALAD in vitro. Materials and methods: We modified a standard Laerdal airway management trainer with locally available equipment to simulate varying rates of continuous vomiting or hemorrhage into the airway during intubation. The effectiveness of our SALAD simulator was tested during an advanced airway workshop of the Airway Management Foundation (AMF). The workshop had a brief common presentation on the learning objective of the SALAD technique followed by a demonstration to small groups of 5-6 participants at one time with necessary instructions. This was followed by a hands-on practical learning session on the simulator. Results: One hundred and five learners used the simulator including 15 faculties and 90 participants (48 on ICU and 42 on ENT workstations). At the end of the session, the workshop faculty and participants were asked to rate their level of confidence in managing similar situations in real practice on a four-point Likert scale. All 15 faculty members and 70 out of 90 participants felt very confident in managing similar situations in real practice. Fifteen participants felt fairly confident and 5 felt slightly confident. Conclusion: In resource-limited settings, our low-cost SALAD simulator is a good educational tool for training airway managers in the skills of managing continuously and rapidly soiling airways. How to cite this article: Kumar R, Kumar R. An Indigenous Suction-assisted Laryngoscopy and Airway Decontamination Simulation System. Indian J Crit Care Med 2024;28(7):702-705.

Robotic hepaticojejunostomy training in novices using robotic simulation and dry-lab suturing (ROSIM): randomized controlled crossover trial.

BACKGROUND: Robotic suturing training is in increasing demand and can be done using suture-pads or robotic simulation training. Robotic simulation is less cumbersome, whereas a robotic suture-pad approach could be more effective but is more costly. A training curriculum with crossover between both approaches may be a practical solution. However, studies assessing the impact of starting with robotic simulation or suture-pads in robotic suturing training are lacking. METHODS: This was a randomized controlled crossover trial conducted with 20 robotic novices from 3 countries who underwent robotic suturing training using an Intuitive Surgical® X and Xi system with the SimNow (robotic simulation) and suture-pads (dry-lab). Participants were randomized to start with robotic simulation (intervention group, n = 10) or suture-pads (control group, n = 10). After the first and second training, all participants completed a robotic hepaticojejunostomy (HJ) in biotissue. Primary endpoint was the objective structured assessment of technical skill (OSATS) score during HJ, scored by two blinded raters. Secondary endpoints were force measurements and a qualitative analysis. After training, participants were surveyed regarding their preferences. RESULTS: Overall, 20 robotic novices completed both training sessions and performed 40 robotic HJs. After both trainings, OSATS was scored higher in the robotic simulation-first group (3.3 ± 0.9 vs 2.5 ± 0.8; p = 0.049), whereas the median maximum force (N) (5.0 [3.2-8.0] vs 3.8 [2.3-12.8]; p = 0.739) did not differ significantly between the groups. In the survey, 17/20 (85%) participants recommended to include robotic simulation training, 14/20 (70%) participants preferred to start with robotic simulation, and 20/20 (100%) to include suture-pad training. CONCLUSION: Surgical performance during robotic HJ in robotic novices was significantly better after robotic simulation-first training followed by suture-pad training. A robotic suturing curriculum including both robotic simulation and dry-lab suturing should ideally start with robotic simulation.

Saliva stress biomarkers in ERCP trainees before and after familiarisation with ERCP on a virtual simulator.

Background: Stress during the early ERCP learning curve may interfere with acquisition of skills during training. The purpose of this study was to compare stress biomarkers in the saliva of trainees before and after familiarisation with ERCP exercises on a virtual simulator. Methods: Altogether 26 endoscopists under training, 14 women and 12 men, completed the three phases of this study: Phase 1. Three different ERCP procedures were performed on the simulator. Saliva for α-amylase (sAA), Chromogranin A (sCgA), and Cortisol (sC) were collected before (baseline), halfway through the exercise (ex.), and 10 min after completion of the exercise (comp.); Phase 2. A three-week familiarisation period where at least 30 different cases were performed on the virtual ERCP simulator; and Phase 3. Identical to Phase 1 where saliva samples were once again collected at baseline, during, and after the exercise. Percentage differences in biomarker levels between baseline and exercise (Diffex) and between baseline and completion (Diffcomp) during Phase 1 and Phase 3 were calculated for each stress marker. Results: Mean % changes, Diffex and Diffcomp, were significantly positive (p < 0.05) for all markers in both Phase 1 and Phase 3. Diffex in Phase 1 was significantly greater than Diffex in Phase 3 (p < 0.05) for sAA and sCgA. Diffcomp for sAA in Phase 1 was significantly greater than Diffcomp in Phase 3 (p < 0.05). No significant differences were found in sC concentration between Phases 1 and 3. Conclusion: This study shows that familiarisation with the ERCP simulator greatly reduced stress as measured by the three saliva stress biomarkers used with sAA being the best. It also suggests that familiarisation with an ERCP simulator might reduce stress in the clinical setting.

NoC simulation steered by NEST: McAERsim and a Noxim patch.

Introduction: Great knowledge was gained about the computational substrate of the brain, but the way in which components and entities interact to perform information processing still remains a secret. Complex and large-scale network models have been developed to unveil processes at the ensemble level taking place over a large range of timescales. They challenge any kind of simulation platform, so that efficient implementations need to be developed that gain from focusing on a set of relevant models. With increasing network sizes imposed by these models, low latency inter-node communication becomes a critical aspect. This situation is even accentuated, if slow processes like learning should be covered, that require faster than real-time simulation. Methods: Therefore, this article presents two simulation frameworks, in which network-on-chip simulators are interfaced with the neuroscientific development environment NEST. This combination yields network traffic that is directly defined by the relevant neural network models and used to steer the network-on-chip simulations. As one of the outcomes, instructive statistics on network latencies are obtained. Since time stamps of different granularity are used by the simulators, a conversion is required that can be exploited to emulate an intended acceleration factor. Results: By application of the frameworks to scaled versions of the cortical microcircuit model-selected because of its unique properties as well as challenging demands-performance curves, latency, and traffic distributions could be determined. Discussion: The distinct characteristic of the second framework is its tree-based source-address driven multicast support, which, in connection with the torus topology, always led to the best results. Although currently biased by some inherent assumptions of the network-on-chip simulators, the results suit well to those of previous work dealing with node internals and suggesting accelerated simulations to be in reach.

Movement-based cognitive training does not significantly shorten the learning curve for acquiring arthroscopic basic skills.

PURPOSE: Skilful arthroscopy requires an aboveaverage level of manual dexterity. It is evident that particular motor skills can be learned and trained before arthroscopic training. The aim of this prospective cohort study was to investigate the impact of movement-related cognitive training on the learning curve during arthroscopic basic training. METHODS: Fifty right-handed participants without arthroscopic experience were matched to an intervention group (n = 25) and a control group (n = 25). Prior to basic arthroscopic skill training with a simulator, the intervention group underwent 12 weeks of movement-related cognitive training. Cognitive and motor skills were assessed in both groups by using standardised tests (CogniFit test, angle reproduction test, two-arm coordination test) as a pretest and, for the intervention group, again before arthroscopic training as a posttest. For arthroscopic simulator training, three tasks ('Telescoping', 'Periscoping', 'Triangulation') from the Fundamentals of Arthroscopic Surgery Training module were selected and practiced 10 times with the camera in the right and left hands. The learning progress was quantified by exercise time, camera path length and hook path length. RESULTS: No significant differences in sex distribution, age distribution or the results of the pretests between the intervention group (n = 21) and the control group (n = 25) were found (n.s.). The intervention group improved significantly from the pretest to the posttest in the CogniFit (p = 0.003) and two-arm coordination test in terms of time (p < 0.001) and errors (p = 0.002) but not in the angle reproduction test. No significant differences were found between the groups for the three arthroscopic tasks. CONCLUSION: The hypothesis that movement-related cognitive training shortens the learning curve for acquiring arthroscopic basic skills cannot be confirmed. Other factors influencing the learning curve such as talent, teaching method and motivation have a greater impact on the acquisition of complex motor skills. LEVEL OF EVIDENCE: Level II.

Progress and challenges in designing dynamic in vitro gastric models to study food digestion.

Understanding the mechanisms involved in food breakdown in the human gastrointestinal (GI) tract is essential in food digestion research. Research to study food digestion in the human GI tract requires in vivo and in vitro approaches. In vivo methods involving human or animal subjects are often cost-prohibitive and raise ethical concerns. For these reasons, in vitro approaches are becoming more common. Several dynamic in vitro models that mimic one or more components of the GI tract have been developed at various research institutions and by commercial companies. While there is evidence of considerable novelty and innovation in the design of these models, there are many differences among them in how the mechanical breakdown of solid foods is accomplished. In some systems, modulating water pressure is used to achieve peristaltic contractions of the gastric antrum, whereas, in other models, the flexible walls of a gastric chamber are compressed by the movement of rollers or clamps outside the walls of the test chamber. Although much progress has been made in standardizing the biochemical environment appropriate to the food digestion process, there is a lack of standard protocols to measure mechanical forces that result in the breakdown of solid foods. Similarly, no standardized methods are available to evaluate the results obtained from in vitro trials for validation purposes. Due to the large variability in the design features of in vitro models used for food digestion studies, developing consensus-based standards for the mechanical aspects of food breakdown is needed.

Caching Policy in Low Earth Orbit Satellite Mega-Constellation Information-Centric Networking for Internet of Things.

Information-Centric Networking (ICN) is the emerging next-generation internet paradigm. The Low Earth Orbit (LEO) satellite mega-constellation based on ICN can achieve seamless global coverage and provide excellent support for Internet of Things (IoT) services. Additionally, in-network caching, typically characteristic of ICN, plays a paramount role in network performance. Therefore, the in-network caching policy is one of the hotspot problems. Especially, compared to caching traditional internet content, in-networking caching IoT content is more challenging, since the IoT content lifetime is small and transient. In this paper, firstly, the framework of the LEO satellite mega-constellation Information-Centric Networking for IoT (LEO-SMC-ICN-IoT) is proposed. Then, introducing the concept of "viscosity", the proposed Caching Algorithm based on the Random Forest (CARF) policy of satellite nodes combines both content popularity prediction and satellite nodes location prediction, for achieving good cache matching between the satellite nodes and content. And using the matching rule, the Random Forest (RF) algorithm is adopted to predict the matching relationship among satellite nodes and content for guiding the deployment of caches. Especially, the content is cached in advance at the future satellite to maintain communication with the current ground segment at the time of satellite switchover. Additionally, the policy considers both the IoT content lifetime and the freshness. Finally, a simulation platform with LEO satellite mega-constellation based on ICN is developed in Network Simulator 3 (NS-3). The simulation results show that the proposed caching policy compared with the Leave Copy Everywhere (LCE), the opportunistic (OPP), the Leave Copy down (LCD), and the probabilistic algorithm which caches each content with probability 0.5 (prob 0.5) yield a significant performance improvement, such as the average number of hops, i.e., delay, cache hit rate, and throughput.

Study of machine learning techniques for outcome assessment of leptospirosis patients.

Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death.

Experienced veterinary surgeons require multiple attempts but gain proficiency rapidly in robotic simulator tasks.

OBJECTIVE: To assess attempts to proficiency of experienced veterinary surgeons for 2 surgical tasks when using a robotic simulator (Mimic dV-Trainer; Surgical Sciences) and determine factors associated with the successful performance of these tasks. METHODS: Veterinary surgeons with rigid, minimally invasive surgery experience performed 2 tasks ("pick and place" and "knot the ring 1") using the simulator until they attained proficiency. Individual performance variables were recorded. The number of attempts to proficiency was recorded. Performance variables were also assessed for effect on proficiency by the Kendall tau correlation and hierarchical multiple linear regression. The study period was from July 25, 2022, through December 14, 2022. RESULTS: The 18 surgeons enrolled required a median of 8.5 attempts (95% CI, 7 to 12; range, 6 to 22) to reach proficiency for the basic task versus 27 attempts (95% CI, 21 to 38; range, 10 to 63) for the advanced task. Surgeons took a median of 6 minutes (range, 3 to 11 minutes) to complete training for the basic task and 12 minutes (range, 4 to 46 minutes) for the advanced task. The number of attempts to reach proficiency correlated strongly with economy of motion (tau = 0.72), instrument collisions (tau = 0.72), and time to completion (tau = 0.96). CLINICAL RELEVANCE: Although experienced surgeons required a high number of attempts to gain proficiency in robotic simulator tasks, they did achieve proficiency quickly, encouraging future investigations into their use for training. Specific motion metrics were identified which improved efficiency during training.

Multi-institutional beta testing of a novel cervical esophagogastric anastomosis simulator.

Objective: A novel simulator developed to offer hands-on practice for the stapled side-to-side cervical esophagogastric anastomosis was tested previously in a single-center study that supported its value in surgical education. This multi-institutional trial was undertaken to evaluate validity evidence from 6 independent thoracic surgery residency programs. Methods: After a virtual session for simulation leaders, learners viewed a narrated video of the procedure and then alternated as surgeon or first assistant. Using an online survey, perceived value was measured across fidelity domains: physical attributes, realism of materials, realism of experience, value, and relevance. Objective assessment included time, number of sutures tearing, bubble test, and direct inspection. Comparison across programs was performed using the Kruskal-Wallis test. Results: Surveys were completed by 63 participants as surgeons (17 junior and 20 senior residents, 18 fellows, and 8 faculty). For 3 of 5 tasks, mean ratings of 4.35 to 4.44 correlated with "somewhat easy" to "very easy" to perform. The interrupted outer layer of the anastomosis rated lowest, suggesting this task was the most difficult. The simulator was rated as a highly valuable training tool. For the objective measurements of performance, "direct inspection" rated highest followed by "time." A total of 90.5% of participants rated the simulator as ready for use with only minor improvements. Conclusions: Results from this multi-institutional study suggest the cervical esophagogastric anastomosis simulator is a useful adjunct for training and assessment. Further research is needed to determine its value in assessing competence for independent operating and associations between improved measured performance and clinical outcomes.

Improving medical students recognizing surgery of glioblastoma removal/decompressive craniectomy via physical lifelike brain simulator training.

BACKGROUND: This study aims to investigate the benefits of employing a Physical Lifelike Brain (PLB) simulator for training medical students in performing craniotomy for glioblastoma removal and decompressive craniectomy. METHODS: This prospective study included 30 medical clerks (fifth and sixth years in medical school) at a medical university. Before participating in the innovative lesson, all students had completed a standard gross anatomy course as part of their curriculum. The innovative lesson involved PLB Simulator training, after which participants completed the Learning Satisfaction/Confidence Perception Questionnaire and some received qualitative interviews. RESULTS: The average score of students' overall satisfaction with the innovative lesson was 4.71 out of a maximum of 5 (SD = 0.34). After the lesson, students' confidence perception level improved significantly (t = 9.38, p < 0.001, effect size = 1.48), and the average score improved from 2,15 (SD = 1.02) to 3.59 (SD = 0.93). 60% of the students thought that the innovative lesson extremely helped them understand the knowledge of surgical neuroanatomy more, 70% believed it extremely helped them improve their skills in burr hole, and 63% thought it was extremely helpful in improving the patient complications of craniotomy with the removal of glioblastoma and decompressive craniectomy after completing the gross anatomy course. CONCLUSION: This innovative lesson with the PLB simulator successfully improved students' craniotomy knowledge and skills.

Evaluation of the Oral Health Education Programme for Nurses using an Oral Lesion Simulator.

PURPOSE: This study aimed to investigate the usefulness of a newly developed oral simulator for nursing students' oral assessment education on oral diseases and symptoms. MATERIALS AND METHODS: The participants were first-year students (n=105) at a nursing school in Japan. Ten identical oral simulators with angular cheilitis, missing teeth, dental caries, calculus, periodontitis, hypoglossal induration, food debris, and crust formation were created by a team of dentists. After a 45-minute lecture programme for oral assessment performance with the Oral Health Assessment Tool (OHAT), the ability test with the simulators and the OHAT as well as test feedback were conducted in a 30-minute practical programme. To evaluate the effectiveness of the programmes, questionnaires and ability tests with slides of oral images were conducted at baseline and after the programme. RESULTS: Ninety-nine students (94.3%) participated in this study. The results of the ability test with the simulators and the OHAT in the practical programme showed that the correct answer rates of assessing tongue, gingiva, present teeth, and oral pain were less than 40%. Their levels of confidence, perception, and oral assessment performance were statistically significantly higher after the programmes than they were at baseline. Their level of confidence in assessing the need for dental referral had the largest increase in scores compared to the lowest scores at baseline in the nine post-programme assessment categories. CONCLUSIONS: This study identified several problems with nursing students' oral assessment skills and improvements of their oral assessment confidence, perceptions and performance.

PhyberSIM: a tool for the generation of ground truth to evaluate brain fiber clustering algorithms.

Diffusion Magnetic Resonance Imaging tractography is a non-invasive technique that produces a collection of streamlines representing the main white matter bundle trajectories. Methods, such as fiber clustering algorithms, are important in computational neuroscience and have been the basis of several white matter analysis methods and studies. Nevertheless, these clustering methods face the challenge of the absence of ground truth of white matter fibers, making their evaluation difficult. As an alternative solution, we present an innovative brain fiber bundle simulator that uses spline curves for fiber representation. The methodology uses a tubular model for the bundle simulation based on a bundle centroid and five radii along the bundle. The algorithm was tested by simulating 28 Deep White Matter atlas bundles, leading to low inter-bundle distances and high intersection percentages between the original and simulated bundles. To prove the utility of the simulator, we created three whole-brain datasets containing different numbers of fiber bundles to assess the quality performance of QuickBundles and Fast Fiber Clustering algorithms using five clustering metrics. Our results indicate that QuickBundles tends to split less and Fast Fiber Clustering tends to merge less, which is consistent with their expected behavior. The performance of both algorithms decreases when the number of bundles is increased due to higher bundle crossings. Additionally, the two algorithms exhibit robust behavior with input data permutation. To our knowledge, this is the first whole-brain fiber bundle simulator capable of assessing fiber clustering algorithms with realistic data.

Haptic based fundamentals of laparoscopic surgery simulation for training with objective assessments.

Force is crucial for learning psychomotor skills in laparoscopic tissue manipulation. Fundamental laparoscopic surgery (FLS), on the other hand, only measures time and position accuracy. FLS is a commonly used training program for basic laparoscopic training through part tasks. The FLS is employed in most of the laparoscopic training systems, including box trainers and virtual reality (VR) simulators. However, many laparoscopic VR simulators lack force feedback and measure tissue damage solely through visual feedback based on virtual collisions. Few VR simulators that provide force feedback have subjective force metrics. To provide an objective force assessment for haptic skills training in the VR simulators, we extend the FLS part tasks to haptic-based FLS (HFLS), focusing on controlled force exertion. We interface the simulated HFLS part tasks with a customized bi-manual haptic simulator that offers five degrees of freedom (DOF) for force feedback. The proposed tasks are evaluated through face and content validity among laparoscopic surgeons of varying experience levels. The results show that trainees perform better in HFLS tasks. The average Likert score observed for face and content validity is greater than 4.6 ± 0.3 and 4 ± 0.5 for all the part tasks, which indicates the acceptance of the simulator among subjects for its appearance and functionality. Face and content validations show the need to improve haptic realism, which is also observed in existing simulators. To enhance the accuracy of force rendering, we incorporated a laparoscopic tool force model into the simulation. We study the effectiveness of the model through a psychophysical study that measures just noticeable difference (JND) for the laparoscopic gripping task. The study reveals an insignificant decrease in gripping-force JND. A simple linear model could be sufficient for gripper force feedback, and a non-linear LapTool force model does not affect the force perception for the force range of 0.5-2.5 N. Further study is required to understand the usability of the force model in laparoscopic training at a higher force range. Additionally, the construct validity of HFLS will confirm the applicability of the developed simulator to train surgeons with different levels of experience.

Visual Function and Driving Performance Under Different Lighting Conditions in Older Drivers: Preliminary Results From an Observational Study.

BACKGROUND: Age-related vision changes significantly contribute to fatal crashes at night among older drivers. However, the effects of lighting conditions on age-related vision changes and associated driving performance remain unclear. OBJECTIVE: This pilot study examined the associations between visual function and driving performance assessed by a high-fidelity driving simulator among drivers 60 and older across 3 lighting conditions: daytime (photopic), nighttime (mesopic), and nighttime with glare. METHODS: Active drivers aged 60 years or older participated in visual function assessments and simulated driving on a high-fidelity driving simulator. Visual acuity (VA), contrast sensitivity function (CSF), and visual field map (VFM) were measured using quantitative VA, quantitative CSF, and quantitative VFM procedures under photopic and mesopic conditions. VA and CSF were also obtained in the presence of glare in the mesopic condition. Two summary metrics, the area under the log CSF (AULCSF) and volume under the surface of VFM (VUSVFM), quantified CSF and VFM. Driving performance measures (average speed, SD of speed [SDspeed], SD of lane position (SDLP), and reaction time) were assessed under daytime, nighttime, and nighttime with glare conditions. Pearson correlations determined the associations between visual function and driving performance across the 3 lighting conditions. RESULTS: Of the 20 drivers included, the average age was 70.3 years; 55% were male. Poor photopic VA was significantly correlated with greater SDspeed (r=0.26; P<.001) and greater SDLP (r=0.31; P<.001). Poor photopic AULCSF was correlated with greater SDLP (r=-0.22; P=.01). Poor mesopic VUSFVM was significantly correlated with slower average speed (r=-0.24; P=.007), larger SDspeed (r=-0.19; P=.04), greater SDLP (r=-0.22; P=.007), and longer reaction times (r=-0.22; P=.04) while driving at night. For functional vision in the mesopic condition with glare, poor VA was significantly correlated with longer reaction times (r=0.21; P=.046) while driving at night with glare; poor AULCSF was significantly correlated with slower speed (r=-0.32; P<.001), greater SDLP (r=-0.26; P=.001) and longer reaction times (r=-0.2; P=.04) while driving at night with glare. No other significant correlations were observed between visual function and driving performance under the same lighting conditions. CONCLUSIONS: Visual functions differentially affect driving performance in different lighting conditions among older drivers, with more substantial impacts on driving during nighttime, especially in glare. Additional research with larger sample sizes is needed to confirm these results.

Enhancing Autonomous Vehicle Decision-Making at Intersections in Mixed-Autonomy Traffic: A Comparative Study Using an Explainable Classifier.

The transition to fully autonomous roadways will include a long period of mixed-autonomy traffic. Mixed-autonomy roadways pose a challenge for autonomous vehicles (AVs) which use conservative driving behaviours to safely negotiate complex scenarios. This can lead to congestion and collisions with human drivers who are accustomed to more confident driving styles. In this work, an explainable multi-variate time series classifier, Time Series Forest (TSF), is compared to two state-of-the-art models in a priority-taking classification task. Responses to left-turning hazards at signalized and stop-sign-controlled intersections were collected using a full-vehicle driving simulator. The dataset was comprised of a combination of AV sensor-collected and V2V (vehicle-to-vehicle) transmitted features. Each scenario forced participants to either take ("go") or yield ("no go") priority at the intersection. TSF performed comparably for both the signalized and sign-controlled datasets, although all classifiers performed better on the signalized dataset. The inclusion of V2V data led to a slight increase in accuracy for all models and a substantial increase in the true positive rate of the stop-sign-controlled models. Additionally, incorporating the V2V data resulted in fewer chosen features, thereby decreasing the model complexity while maintaining accuracy. Including the selected features in an AV planning model is hypothesized to reduce the need for conservative AV driving behaviour without increasing the risk of collision.