An educational environment based on digital image processing to support the learning process of biomaterials degradation in stem cells
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An educational environment based on digital image processing to support the learning process of biomaterials degradation in stem cellsAuthor(s)
Date
2018-11-08Citation
Robles-Bykbaev Y, Naya S, Tarrio-Saavedra J, et al. An educational environment based on digital image processing to support the learning process of biomaterials degradation in stem cells. In: Proceedings of the 2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON); 2018 Ago 8-10; Lima, Perú. Piscataway, NJ: Institute of Electrical and Electronic Engineering; 2018.
Abstract
[Abstract] The Poly(DL-lactide-co-glycolide) copolymers (PDLGA) have designed and performed as biomaterials, taking into account their biodegradability and biocompatibility properties. These materials have a wide range of application in medicine such as orthopedic implants, general surgical implants (suture materials), osteosynthesis, bone cement, among many others. For these reasons, in this paper, we present an intelligent educational environment that can be used for both, researchers and students interested in the analysis of the biomaterial behavior under certain conditions. Our platform includes a Learning Objects (LOs) for MOODLE, and in the same way, implements several digital image processing techniques as well as a decision support module based on a random forest algorithm and statistical modeling. With the aim of determining the real feasibility of this proposal, we have presented the system to 34 Ecuadorian engineering students. After testing the platform, the students answered a survey aimed at determining their perception of the system. The results provide several guidelines to continue with the developing of the platform.
Keywords
Image segmentation
Biomedical imaging
Degradation
Engineering students
Analytical models
Electronic learning
Biomedical imaging
Degradation
Engineering students
Analytical models
Electronic learning