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- All Subjects: Genetics
- All Subjects: Education
- Creators: School of Life Sciences
Colorimetric assays are an important tool in point-of-care testing that offers several advantages to traditional testing methods such as rapid response times and inexpensive costs. A factor that currently limits the portability and accessibility of these assays are methods that can objectively determine the results of these assays. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before being measured in some way. However, this increases the cost and decreases the portability of these assays. The focus of this study is to create a machine learning algorithm that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of a model to several types of colorimetric assays, three models were trained on the same convolutional neural network with different datasets. The images these models are trained on consist of positive and negative images of ETG, fentanyl, and HPV Antibodies test strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types. The results from these models show it is able to predict positive and negative results to a high level of accuracy.
This thesis aimed to create a curriculum for college students to increase their health insurance literacy and to evaluate the impact of the curriculum on participants' confidence. The curriculum for college students consisted of pre-recorded presentation slides covering six health insurance topics, pre- and post-tests, and evaluation questions. Canvas was used to house the curriculum. At the time of evaluation, a total of 12 participants had completed all aspects of the curriculum. The curriculum was evaluated through questions provided at the end of each module. It was found that participants felt the curriculum to be clear and helpful. Moreover, participants reported an increase in confidence, decreased confusion, and were interested in learning more about health insurance such as enrollment. Both the creation of a curriculum and the impact on participants' confidence was successful. At a later point in time, an analysis of the pre- and post-tests will be assessed to determine if the curriculum was effective at increasing health insurance literacy.