13 research outputs found
The effect of 3D stereopsis and hand-tool alignment on learning effectiveness and skill transfer of a VR-based simulator for dental training
Dental simulators gained prevalence in recent years. Important aspects
distinguishing VR hardware configurations are 3D stereoscopic rendering and
visual alignment of the user's hands with the virtual tools. New dental
simulators are often evaluated without analysing the impact of these simulation
aspects. In this paper, we seek to determine the impact of 3D stereoscopic
rendering and of hand-tool alignment on the teaching effectiveness and skill
assessment accuracy of a VR dental simulator. We developed a bimanual simulator
using an HMD and two haptic devices that provides an immersive environment with
both 3D stereoscopic rendering and hand-tool alignment. We then independently
controlled for each of the two aspects of the simulation. We trained four
groups of students in root canal access opening using the simulator and
measured the virtual and real learning gains. We quantified the real learning
gains by pre- and post-testing using realistic plastic teeth and the virtual
learning gains by scoring the training outcomes inside the simulator. We
developed a scoring metric to automatically score the training outcomes that
strongly correlates with experts' scoring of those outcomes. We found that
hand-tool alignment has a positive impact on virtual and real learning gains,
and improves the accuracy of skill assessment. We found that stereoscopic 3D
had a negative impact on virtual and real learning gains, however it improves
the accuracy of skill assessment. This finding is counter-intuitive, and we
found eye-tooth distance to be a confounding variable of stereoscopic 3D, as it
was significantly lower for the monoscopic 3D condition and negatively
correlates with real learning gain. The results of our study provide valuable
information for the future design of dental simulators, as well as simulators
for other high-precision psycho-motor tasks.Comment: 26 pages, 15 figures, Accepted at online journal PLoS ON
Comparison of Neck Movement between Dentists with and without Work Related Musculoskeletal Pain
AbstractObjective: To compare neck movement between dentists with and without work related musculoskeletal pain. Method: By ways of purposive sampling, this case-control study recruited 19 dentists; 10 and 9 with and without work related musculoskeletal pain respectively. While performing scaling in gingivitis patients, the dentistsâ degrees of neck flexion and lateral flexion were measured by electrogoniometer and recorded by ultrasonic recorder every second until finishing work. Data from the two groups were analyzed by Datalog. Results: The 10th, 50th and 90th percentiles of neck flexion among dentists with work related musculoskeletal pain were 25.67, 39.52 and 50.16 degree, respectively; while those of dentists without pain were 20.58, 32.24, and 40.25 degree, respectively. The 10th, 50th and 90th percentiles of the right lateral flexion were 2.38, 15.06, and 23.45 degree, respectively among dentists with pain and 3.02, 8.68, and 18.0 degree, respectively, among those without pain. For neck lateral flexions to the left, there were 1.80, 9.0, and 27.81 degree and 1.44, 5.22, and 13.68 degree for dentists with and without musculoskeletal pain respectively. Dentists with pain had greater degree of neck flexion and right and left lateral flexions in 10th, 50th and 90th percentiles except the 10th percentile of right lateral flexion. The results showed that degree of neck flexion, right and left lateral flexion movement between dentists with and without musculoskeletal pain were significantly different in statistical output (p<0.05). Dentists with pain had static posture in neck flexion 26.70% and in lateral flexion 29.38% of working time; while those without pain had static posture in neck flexion 20.70% and in lateral flexion 51.35% of such time. Conclusion: Neck flexion and lateral flexion among dentists with work related musculoskeletal pain were significantly higher than in dentists without such pain.Keywords: electrogoniometer, musculoskeletal disorders (MSD), joint range of motion, neck range of motion, dentist āļāļāļāļąāļāļĒāđāļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđ: āđāļāļ·āđāļāđāļāļĢāļĩāļĒāļāđāļāļĩāļĒāļāļāļāļĻāļēāļāļēāļĢāđāļāļĨāļ·āđāļāļāđāļŦāļ§āļāļāļāļāļāļāļāļ°āļāļģāļāļēāļāļāļāļāļāļąāļāļāđāļāļāļĒāđāļāļĩāđāļĄāļĩāđāļĨāļ°āđāļĄāđāļĄāļĩāļāļēāļāļēāļĢāļāļ§āļāļāļĨāđāļēāļĄāđāļāļ·āđāļāļāļēāļāļāļēāļĢāļāļģāļāļēāļ āļ§āļīāļāļĩāļāļēāļĢāļĻāļķāļāļĐāļē: āļĢāļđāļāđāļāļāļāļēāļĢāļĻāļķāļāļĐāļēāđāļāđāļ case-control study āđāļāļĒāļāļąāļ§āļāļĒāđāļēāļāđāļāđāļāļāļąāļāļāđāļāļāļĒāđ 19 āļāļ āđāļāđāļāđāļāđāļāļāļđāđāļĄāļĩāļāļēāļāļēāļĢāļāļ§āļāļāļĨāđāļēāļĄāđāļāļ·āđāļ 10 āļāļ āđāļĨāļ°āđāļĄāđāļĄāļĩāļāļēāļāļēāļĢ 9 āļāļ āđāļŦāđāļāļąāļāļāđāļāļāļĒāđāļāļđāđāļĢāđāļ§āļĄāļ§āļīāļāļąāļĒāļāļđāļāļŦāļīāļāļāđāļģāļĨāļēāļĒāļāļļāļāļāļģāđāļŦāļāđāļāđāļāļāđāļāļāļāļēāļāļāļēāļŠāļēāļŠāļĄāļąāļāļĢ āđāļāđāļāļāđāļāļĄāļđāļĨāļāļāļĻāļēāļāļēāļĢāđāļāļĨāļ·āđāļāļāđāļŦāļ§āļāļāļāļāļāļāļąāļāđ āđāļāļāļāđāļĄ-āđāļāļĒ āđāļĨāļ°āđāļāļāļāđāļēāļĒ-āļāļ§āļē āđāļāļĒāđāļāļĢāļ·āđāļāļāļ§āļąāļāļāļāļĻāļēāļāļēāļĢāđāļāļĨāļ·āđāļāļāđāļŦāļ§āļāļāļāļāđāļāļāđāļāđāļāļāļāļīāđāļĨāļāđāļāļĢāļāļīāļ āļāļąāļāļāļķāļāļāļāļĻāļēāļāļēāļĢāđāļāļĨāļ·āđāļāļāđāļŦāļ§āđāļāļĒāđāļāļĢāļ·āđāļāļāļāļąāļĨāļāļĢāļēāđāļāļāļīāļāļāđāļāđāļāļ·āđāļāļāļāļļāļ 1āļ§āļīāļāļēāļāļĩ āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāļĢāļ°āļŦāļ§āđāļēāļāļŠāļāļāļāļĨāļļāđāļĄāđāļāļĒāđāļāļĢāđāļāļĢāļĄ Datalog āļāļĨāļāļēāļĢāļĻāļķāļāļĐāļē: āļāđāļēāļāļāļĻāļēāļāļēāļĢāđāļāļĨāļ·āđāļāļāđāļŦāļ§āļāļāļāļĄāļļāļĄāļāđāļĄāļāļāļāļĩāđāđāļāļāļĢāđāđāļāļāļāđāđāļāļĨāđāļāļĩāđ 10, 50 āđāļĨāļ° 90 āļāļāļāļāļąāļāļāđāļāļāļĒāđāļāļĩāđāļĄāļĩāļāļēāļāļēāļĢāļāļ§āļāđāļāđāļēāļāļąāļ 25.67, 39.52 āđāļĨāļ° 50.16 āļāļāļĻāļēāļāļēāļĄāļĨāļģāļāļąāļ āļŠāđāļ§āļāļāļĨāļļāđāļĄāļāļĩāđāđāļĄāđāļĄāļĩāļāļēāļāļēāļĢāđāļāđāļēāļāļąāļ 20.58, 32.24 āđāļĨāļ° 40.25 āļāļāļĻāļē āļāđāļēāļāļāļĻāļēāļāļēāļĢāđāļāļĨāļ·āđāļāļāđāļŦāļ§āđāļāļĩāļĒāļāļāļāļāļēāļāļāļ§āļēāļāļĩāđāđāļāļāļĢāđāđāļāļāļāđāđāļāļĨāđāļāļĩāđ 10, 50 āđāļĨāļ° 90 āļāļāļāļāļąāļāļāđāļāļāļĒāđāļāļĩāđāļĄāļĩāļāļēāļāļēāļĢāđāļāđāļēāļāļąāļ2.38, 15.06 āđāļĨāļ° 23.45 āļāļāļĻāļēāļāļēāļĄāļĨāļģāļāļąāļ āđāļāļāļĨāļļāđāļĄāļāļĩāđāđāļĄāđāļĄāļĩāļāļēāļāļēāļĢāđāļāđāļēāļāļąāļ 3.02, 8.68āđāļĨāļ° 18.0 āļŠāđāļ§āļāļāļēāļĢāđāļāļĩāļĒāļāļāļāļāļēāļāļāđāļēāļĒāļāļąāđāļ āļāļąāļāļāđāļāļāļĒāđāļāļĩāđāļĄāļĩāļāļēāļāļēāļĢāđāļāđāļēāļāļąāļ 1.80, 9.0āđāļĨāļ° 27.81 āļāļāļĻāļē āđāļĨāļ°āļāļĩāđāđāļĄāđāļĄāļĩāļāļēāļāļēāļĢāđāļāđāļēāļāļąāļ 1.44, 5.22 āđāļĨāļ° 13.68 āļāļāļĻāļēāļāļēāļĄāļĨāļģāļāļąāļāđāļāļĒāļāļĨāļļāđāļĄāļāļĩāđāļĄāļĩāļāļēāļāļēāļĢāļĄāļĩāļāđāļēāļāļāļĻāļēāļāļēāļĢāđāļāļĨāļ·āđāļāļāđāļŦāļ§āļāđāļĄāļāļāđāļĨāļ°āđāļāļĩāļĒāļāļāļāļāđāļēāļĒāļāļ§āļēāļāļāļ°āļāļāļīāļāļąāļāļīāļāļēāļāļĄāļēāļāļāļ§āđāļēāļāļĨāļļāđāļĄāļāļĩāđāđāļĄāđāļĄāļĩāļāļēāļāļēāļĢāļāļĩāđāđāļāļāļĢāđāđāļāļāļāđāđāļāļĨāđ 10, 50 āđāļĨāļ° 90 āļĒāļāđāļ§āđāļāđāļāļĩāļĒāļāļāļāļāļēāļāļāļ§āļēāļāļĩāđāđāļāļāļĢāđāđāļāđāļāļāđāđāļāļĨāđāļāļĩāđ 10 āđāļāļĒāļāđāļēāļāļāļĻāļēāļāļēāļĢāđāļāļĨāļ·āđāļāļāđāļŦāļ§āļāļāļāļāļāļĄāļļāļĄāļāđāļĄ-āđāļāļĒāđāļĨāļ°āđāļāļĩāļĒāļāļāļāļāđāļēāļĒ-āļāļ§āļēāļāļāļ°āļāļģāļāļēāļāļāļāļ 2 āļāļĨāļļāđāļĄāđāļāļāļāđāļēāļāļāļąāļ (P < 0.05) āļāļđāđāļĢāđāļ§āļĄāļāļēāļĢāļĻāļķāļāļĐāļēāļĄāļĩāļāđāļ§āļāļāļģāļāļēāļāļāđāļēāļāļāđāļēāđāļāļīāļĄāđāļāđāļāļ§āļāđāļĄāļāļāđāļāđāļāđāļ§āļĨāļēāļāļēāļ āđāļāļĒāđāļāļāļĨāļļāđāļĄāļāļĩāđāļĄāļĩāļāļēāļāļēāļĢāļāļīāļāđāļāđāļāļĢāđāļāļĒāļĨāļ° 26.70 āļāļāļāđāļ§āļĨāļēāļāļģāļāļēāļāļāļąāļāđ āļŦāļĄāļ āđāļĨāļ°āļāđāļēāļāļāđāļēāđāļāļīāļĄāđāļāļ§āđāļāļĩāļĒāļāļāļāļāđāļēāļĒ-āļāļ§āļēāđāļāđāļāļĢāđāļāļĒāļĨāļ° 29.38 āļāļāļāđāļ§āļĨāļēāļāļąāļāđ āļŦāļĄāļ āļŠāđāļ§āļāļāļĨāļļāđāļĄāļāļĩāđāđāļĄāđāļĄāļĩāļāļēāļāļēāļĢāļāļīāļāđāļāđāļāļĢāđāļāļĒāļĨāļ°20.70 āđāļĨāļ° 51.35 āļāļēāļĄāļĨāļģāļāļąāļāļāļāļāđāļ§āļĨāļēāļāļąāļāđ āļŦāļĄāļ āļŠāļĢāļļāļ: āļĄāļļāļĄāļāļĩāđāđāļāđāđāļāļāļēāļĢāļāļģāļāļēāļāļāđāļĄāđāļĨāļ°āđāļāļĩāļĒāļāļāđāļēāļĒāļāļ§āļēāļāļāļāļāļĨāļļāđāļĄāļāļąāļāļāđāļāļāļĒāđāļāļĩāđāļĄāļĩāļāļēāļāļēāļĢāļāļ§āļāļĄāļĩāļāđāļēāļĄāļēāļāļāļ§āđāļēāļāļĨāļļāđāļĄāđāļĄāđāļĄāļĩāļāļēāļāļēāļĢāļāļ§āļāļāļĨāđāļēāļĄāđāļāļ·āđāļāļāļēāļāļāļēāļĢāļāļģāļāļēāļāļāļģāļŠāļģāļāļąāļ: āđāļāļĢāļ·āđāļāļāļ§āļąāļāļāļāļĻāļēāļāļēāļĢāđāļāļĨāļ·āđāļāļāđāļŦāļ§āļāļāļāļāđāļāļāđāļāđāļāļāļāļīāđāļĨāļāđāļāļĢāļāļīāļ, āļāļ§āļēāļĄāļāļīāļāļāļāļāļīāļāļāļāļāļĢāļ°āļāļđāļāđāļĨāļ°āļāļĨāđāļēāļĄāđāļāļ·āđāļ, āļāđāļ§āļāļāļāļĻāļēāļāļēāļĢāđāļāļĨāļ·āđāļāļāđāļŦāļ§āļāļāļāļāđāļ,āļāđāļ§āļāļāļāļĻāļēāļāļēāļĢāđāļāļĨāļ·āđāļāļāđāļŦāļ§āļāļāļāļāļ, āļāļąāļāļāđāļāļāļĒ
Student Modeling for Collaborative Medical Problem-Based Leaning
Today a great many medical schools have turned to a problem-based learning (PBL) approach to teaching. PBL instructional models vary but the general approach is student-centered, small group, collaborative problem-based learning activities. While PBL has many strengths, effective PBL requires the tutor to provide a high degree of personal attention to the students. In the current academic environment where resources are becoming increasingly scarce and costs must be reduced, providing such attention becomes increasingly difficult. This is exacerbated by the fact that medical school faculty, in particular, often have limited time to devote to teaching. As a consequence, medical students often do not get as much facilitated PBL training as they might need or want. Our proposed work combines concepts from Intelligen
A collaborative intelligent tutoring system for medical problem-based learning
This paper describes COMET, a collaborative intelligent tutoring system for medical problem-based learning. The system uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. It incorporates a multi-modal interface that integrates text and graphics so as to provide a rich communication channel between the students and the system, as well as among students in the group. Students can sketch directly on medical images, search for medical concepts, and sketch hypotheses on a shared workspace. The prototype system incorporates substantial domain knowledge in the area of head injury diagnosis. A major challenge in building COMET has been to develop algorithms for generating tutoring hints. Tutoring in PBL is particularly challenging since the tutor should provide as little guidance as possible while at the same time not allowing the students to get lost. From studies of PBL sessions at a local medical school, we have identified and implemented eight commonly used hinting strategies. We compared the tutoring hints generated by COMET with those of experienced human tutors. Our results show that COMETâs hints agree with the hints of the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.652, Kappa = 0.773)
P.: Modeling Individual and Collaborative Problem Solving
Abstract. Since problem solving in group problem-based learning is a collaborative process, modeling individuals and the group is necessary if we wish to develop an intelligent tutoring system that can do things like focus the group discussion, promote collaboration, or suggest peer helpers. We have used Bayesian networks to model individual student knowledge and activity, as well as that of the group. The validity of the approach has been tested with student models in the areas of head injury, stroke and heart attack. Receiver operating characteristic (ROC) curve analysis shows that, the models are highly accurate in predicting individual student actions. Comparison with human tutors shows that group activity determined by the model agrees with that suggested by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.774, Kappa = 0.823).
Clinical-reasoning skill acquisition through intelligent group tutoring
This paper describes COMET, a collaborative intelligent tutoring system for medical problembased learning. COMET uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. Generic domainindependent tutoring algorithms use the models to generate tutoring hints. We present an overview of the system and then the results of two evaluation studies. The validity of the modeling approach is evaluated in the areas of head injury, stroke and heart attack. Receiver operating characteristic (ROC) curve analysis indicates that, the models are accurate in predicting individual student actions. Comparison of learning outcomes shows that studen
In Proc. Intâl Joint Conference on Artificial Intelligence (IJCAI05), Edinburgh, 2005. Clinical-Reasoning Skill Acquisition through Intelligent Group Tutoring
This paper describes COMET, a collaborative intelligent tutoring system for medical problembased learning. COMET uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. Generic domainindependent tutoring algorithms use the models to generate tutoring hints. We present an overview of the system and then the results of two evaluation studies. The validity of the modeling approach is evaluated in the areas of head injury, stroke and heart attack. Receiver operating characteristic (ROC) curve analysis indicates that, the models are accurate in predicting individual student actions. Comparison of learning outcomes shows that studen
Additional file 1 of Deep learning in oral cancer- a systematic review
Additional file 1: Table 1S. Modified leading questions of QUADAS-2 for critical appraisal. Table 2S. Quality assessment of included studies using QUADAS-2 (Classification studies). Table 3S. Quality assessment of included studies using QUADAS-2 (Object detection studies). Table 4S. Quality assessment of included studies using QUADAS-2 (Segmentation studies). Table 5S. Quality assessment of included studies using QUADAS-2 (Prognosis prediction studies)