Abstract
The expense of treating cardiovascular diseases is significant. For example, sediments on the coronary arteries’ inner walls are among the most common risks of a heart attack. One possible treatment includes cardiovascular implants or stents. Stents are manufactured by a braiding process and afterward inspected for defects by human visual inspection. To reduce production costs, an automated inspection system is, therefore, the subject of this work. First, we propose a formalized problem description for camera-based automated visual inspection. Next, a machine learning based divide-and-conquer algorithm is presented. The CNN based algorithm can be used both to supervise the braiding process and to correct braiding errors.
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Acknowledgments
Parts of this work has been developed in the project Stents4Tomorrow. Stents4Tomorrow (reference number: 02P18C022) is partly funded by the German ministry of education and research BMBF) within the research program ProMed. Additionally the authors want to thank Dr. Marc Braeuner (Admedes GmbH) and Kevin Lehmann (Admedes GmbH) for their continuous support and advice.
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Haas, B., Stang, M., Khan-Blouki, V., Sax, E. (2021). Introduction of an Algorithm Based on Convolutional Neural Networks for an Automated Online Correction of Braided Cardiovascular Implants. In: Ahram, T., Taiar, R., Groff, F. (eds) Human Interaction, Emerging Technologies and Future Applications IV. IHIET-AI 2021. Advances in Intelligent Systems and Computing, vol 1378. Springer, Cham. https://doi.org/10.1007/978-3-030-74009-2_4
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DOI: https://doi.org/10.1007/978-3-030-74009-2_4
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