scholarly journals Jigsaw puzzle to teach anatomy to first year MBBS students

2019 ◽  
Vol 7 (11) ◽  
pp. 4161
Author(s):  
Seema Gupta ◽  
Anshu Soni ◽  
Sonia Singh ◽  
Hitant Vohra
Keyword(s):  
Deep Learning ◽  
First Year ◽  
Jigsaw Puzzle ◽  
Rote Learning ◽  
Innovative Method ◽  
New Concepts ◽  
Hands On ◽  
Teaching Anatomy ◽  
Self Learning ◽  
The Brain

Background: Anatomy provides a platform of knowledge indispensable to all the branches of medicine. Students have to learn many new concepts and tongue-twisting terminologies, making this subject difficult to comprehend. It has been seen that a range of innovative, proactive, simple, hands-on approach strategies can achieve maximum student engagement and help them learn. Aim is to take students from the traditional view of anatomy as a subject that require surface learning (rote learning, memorization) to one that can lead to deep learning through understanding. Keeping all this in mind a study was planned to develop an innovative method of teaching anatomy to 1st year MBBS students.Methods: The diagrams of sagittal and horizontal sections of the brain were selected, marked and cut into jigsaw pieces. Students were given an incomplete jigsaw puzzle and a set of questions. The answer to these questions helped them complete the puzzle. Perception of students who consented to participate in the study was noted.Results: Out of 98 students who participated in the study 61.2% wanted to participate in similar activities in future in anatomy and 57.1% felt that it helped them in understanding the topic. For 52.1% it was a useful self-learning tool and for another 48.9% students solving the puzzle was a challenging experience.Conclusions: Jigsaw puzzle is an efficient way for students to become engaged in their learning. It maximizes interaction and establishes an atmosphere of co-operation and respect for other students and improves learning.

scholarly journals Using an Implementation Trial of an ePortfolio System to Promote Student Learning through Self-Reflection: Leveraging the Success

2021 ◽  
Vol 11 (6) ◽  
pp. 263
Author(s):  
Chris Campbell ◽  
Tran Le Nghi Tran
Keyword(s):  
Educational Technology ◽  
Pilot Study ◽  
Reflective Practice ◽  
First Year ◽  
Assessment Practices ◽  
Self Reflection ◽  
Large N ◽  
New Concepts ◽  
Hands On ◽  
Implementation Trial

This paper reports on a pilot study that was conducted during a technical trial of a new ePortfolio system at a large Australian university. Students from a large (n = 325) first-year educational technology course were given the opportunity to use the new ePortfolio system weekly as part of their reflective practice at the end of the hands-on tutorial classes and also through a blogging assignment that required six posts throughout the semester. Although the students reflecting on their work and ePortfolios themselves are not new concepts, this paper reports how assessment practices can be improved using ePortfolios and how students can improve their reflective practice through simple and regular use throughout the 12-week semester that the study was conducted. From the class, 208 students responded to the survey with the results being positive. The students were able to use the system easily and did not report many problems with crashing or freezing. The lessons learnt form an important part of this study for future iterations with these reported in the paper.

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

scholarly journals Deep learning for intelligent diagnosis in thyroid scintigraphy

2021 ◽  
Vol 49 (1) ◽  
pp. 030006052098284
Author(s):  
Tingting Qiao ◽  
Simin Liu ◽  
Zhijun Cui ◽  
Xiaqing Yu ◽  
Haidong Cai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Kappa Coefficient ◽  
Diagnostic Assessment ◽  
First Year ◽  
Thyroid Scintigraphy ◽  
Diagnostic Ability ◽  
The Third ◽  
Classification Time ◽  
Better Than

Objective To construct deep learning (DL) models to improve the accuracy and efficiency of thyroid disease diagnosis by thyroid scintigraphy. Methods We constructed DL models with AlexNet, VGGNet, and ResNet. The models were trained separately with transfer learning. We measured each model’s performance with six indicators: recall, precision, negative predictive value (NPV), specificity, accuracy, and F1-score. We also compared the diagnostic performances of first- and third-year nuclear medicine (NM) residents with assistance from the best-performing DL-based model. The Kappa coefficient and average classification time of each model were compared with those of two NM residents. Results The recall, precision, NPV, specificity, accuracy, and F1-score of the three models ranged from 73.33% to 97.00%. The Kappa coefficient of all three models was >0.710. All models performed better than the first-year NM resident but not as well as the third-year NM resident in terms of diagnostic ability. However, the ResNet model provided “diagnostic assistance” to the NM residents. The models provided results at speeds 400 to 600 times faster than the NM residents. Conclusion DL-based models perform well in diagnostic assessment by thyroid scintigraphy. These models may serve as tools for NM residents in the diagnosis of Graves’ disease and subacute thyroiditis.

scholarly journals Beneficial and harmful explanatory machine learning

2021 ◽  
Author(s):  
Lun Ai ◽  
Stephen H. Muggleton ◽  
Céline Hocquette ◽  
Mark Gromowski ◽  
Ute Schmid
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Beneficial Effect ◽  
Empirical Evidence ◽  
Task Performance ◽  
Human Performance ◽  
Human Learning ◽  
Science Literature ◽  
Definition Of ◽  
Self Learning

AbstractGiven the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong machine learning (USML). USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance. A recent paper demonstrates the beneficial effect of a machine learned logic theory for a classification task, yet no existing work to our knowledge has examined the potential harmfulness of machine’s involvement for human comprehension during learning. This paper investigates the explanatory effects of a machine learned theory in the context of simple two person games and proposes a framework for identifying the harmfulness of machine explanations based on the Cognitive Science literature. The approach involves a cognitive window consisting of two quantifiable bounds and it is supported by empirical evidence collected from human trials. Our quantitative and qualitative results indicate that human learning aided by a symbolic machine learned theory which satisfies a cognitive window has achieved significantly higher performance than human self learning. Results also demonstrate that human learning aided by a symbolic machine learned theory that fails to satisfy this window leads to significantly worse performance than unaided human learning.

scholarly journals fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task

2021 ◽  
Vol 11 (11) ◽  
pp. 4922
Author(s):  
Tengfei Ma ◽  
Wentian Chen ◽  
Xin Li ◽  
Yuting Xia ◽  
Xinhua Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Duration ◽  
Signal Classification ◽  
Imagery Task ◽  
Neural Activities ◽  
Computer Interfaces ◽  
Acquisition Device ◽  
The Time Domain ◽  
The Brain

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

An exploration of the factors that contribute to learning satisfaction of first-year anatomy and physiology students

2015 ◽  
Vol 39 (3) ◽  
pp. 158-166 ◽  
Author(s):  
Saramarie Eagleton
Keyword(s):  
Learning Community ◽  
Teaching And Learning ◽  
Community Support ◽  
First Year ◽  
Learning Effectiveness ◽  
Anatomy And Physiology ◽  
Learning Satisfaction ◽  
Use Of Technology ◽  
Teaching Anatomy ◽  
Physiology Students

Lecturers have reverted to using a “blended” approach when teaching anatomy and physiology. Student responses as to how this contributes to their learning satisfaction were investigated using a self-administered questionnaire. The questionnaire consisted of closed- and open-ended questions that were based on three determinants of learning satisfaction: perceived course learnability, learning community support, and perceived learning effectiveness. Regarding course learnability, students responded positively on questions regarding the relevance of the subject for their future careers. However, students identified a number of distractions that prevented them from paying full attention to their studies. As far as learning community support was concerned, respondents indicated that they were more comfortable asking a peer for support if they were unsure of concepts than approaching the lecturing staff. Most of the students study in their second language, and this was identified as a stumbling block for success. There was a difference in opinion among students regarding the use of technology for teaching and learning of anatomy and physiology. From students' perceptions regarding learning effectiveness, it became clear that students' expectations of anatomy and physiology were unrealistic; they did not expect the module to be so comprehensive. Many of the students were also “grade oriented” rather than “learning oriented” as they indicated that they were more concerned about results than “owning” the content of the module. Asking students to evaluate aspects of the teaching and learning process have provided valuable information to improve future offerings of anatomy and physiology.

scholarly journals Deep learning-based pupil model predicts time and spectral dependent light responses

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Babak Zandi ◽  
Tran Quoc Khanh
Keyword(s):  
Deep Learning ◽  
Time Course ◽  
Light Response ◽  
Absolute Error ◽  
Light Responses ◽  
Temporal Prediction ◽  
Feed Forward Neural Networks ◽  
Self Learning ◽  
Pupil Light Response ◽  
Planckian Locus

AbstractAlthough research has made significant findings in the neurophysiological process behind the pupillary light reflex, the temporal prediction of the pupil diameter triggered by polychromatic or chromatic stimulus spectra is still not possible. State of the art pupil models rested in estimating a static diameter at the equilibrium-state for spectra along the Planckian locus. Neither the temporal receptor-weighting nor the spectral-dependent adaptation behaviour of the afferent pupil control path is mapped in such functions. Here we propose a deep learning-driven concept of a pupil model, which reconstructs the pupil’s time course either from photometric and colourimetric or receptor-based stimulus quantities. By merging feed-forward neural networks with a biomechanical differential equation, we predict the temporal pupil light response with a mean absolute error below 0.1 mm from polychromatic (2007 $$\pm$$ ± 1 K, 4983 $$\pm$$ ± 3 K, 10,138 $$\pm$$ ± 22 K) and chromatic spectra (450 nm, 530 nm, 610 nm, 660 nm) at 100.01 ± 0.25 cd/m2. This non-parametric and self-learning concept could open the door to a generalized description of the pupil behaviour.

scholarly journals Qualitative Development of Students’ Digital Skills by Integrating a Spreadsheet Software in First Year Introduction to Engineering and Seminar Course

2021 ◽  
Vol 16 (18) ◽  
pp. 213
Author(s):  
Aziatul Niza Binti Sadikin ◽  
Azizul Azri Bin Mustaffa ◽  
Hasrinah Binti Hasbullah ◽  
Zaki Yamani Bin Zakaria ◽  
Mohd Kamaruddin Bin Abd Hamid ◽  
...  
Keyword(s):  
Problem Based Learning ◽  
First Year ◽  
Microsoft Excel ◽  
First Year Students ◽  
Learning Skill ◽  
Hands On ◽  
Digital Skills ◽  
Data Source ◽  
Spreadsheet Software ◽  
Development Of Students

The Introduction to Engineering (ITE) and Industrial Seminar and Profession (ISP) courses conducted at School of Chemical and Energy Engineering, Universiti Teknologi Malaysia, are integrated courses which implement the Cooperative Problem-based Learning (CPBL) methods in the same semester. Based on this integrated courses, the main aim of this paper is to investigate the qualitative impact of spreadsheet hands-on seminar on the first year students' digital skill. At the beginning of the semester, students are given sustainability-based project to work on, which requires them to collect and to report the data in a series of presentations and written reports. In order to present those data, they need to use analysis tools such as a spreadsheet software. The students are introduced with some in-depth applications of the Microsoft Excel software through the seminar sessions in the ISP course. With the knowledge that the students gain, they are expected to implement it in the CPBL project. A qualitative approach has been adopted to implement the study. Student’s reflections were used as the data source to identify common attributes that they have managed to gain from seminar sessions. This study has found that all students had primarily learned about digital skills. They perceived hand-on activity during the seminar as a good platform to acquire knowledge on basic calculation and developed learning skill on Excel. Moreover, students recognized the skills they are learning will be useful in other courses and future careers.

scholarly journals Vision based Patient Fall Detection using Deep Learning in Smart Hospitals

2019 ◽  
Vol 9 (2) ◽  
pp. 4826-4832
Keyword(s):  
Deep Learning ◽  
Video Surveillance ◽  
Fall Detection ◽  
Hospital Staff ◽  
Comparison Study ◽  
New Concepts ◽  
Surveillance Cameras ◽  
Time Fall ◽  
Smart Hospitals ◽  
Deep Learning Model

With the emergence of new concepts like smart hospitals, video surveillance cameras should be introduced in each room of the hospital for the purpose of safety and security. These surveillance cameras can also be used to provide assistance to patients and hospital staff. In particular, a real-time fall of a patient can be detected with the help of these cameras and accordingly, assistance can be provided to them. Different models have already been developed by researchers to detect a human fall using a camera. This paper proposes a vision based deep learning model to detect a human fall. Along with this model, two mathematical based models have also been proposed which uses pre-trained YOLO FCNN and Faster R-CNN architecture to detect the human fall. At the end of this paper, a comparison study has been done on these models to specify which method provides the most accurate results

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