Authors:
William Solórzano-Requejo
1
;
2
;
Carlos Aguilar
2
;
Rodrigo Zapata Martínez
2
;
Oscar Contreras-Almengor
3
;
Isabel Moscol
1
;
Carlos Ojeda
1
;
Jon Molina-Aldareguia
2
;
3
and
Andrés Diaz Lantada
2
Affiliations:
1
Department of Mechanical and Electrical Engineering, Universidad de Piura, Piura, Peru
;
2
ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
;
3
IMDEA Materials Institute, Getafe, Spain
Keyword(s):
Machine Learning, Computational Design, Personalized Medicine, Automated Design, Additive Manufacturing.
Abstract:
The design of personalized medical devices, which are adapted to the patient’s needs, starts from a digital model created from the advanced use of clinical imaging techniques such as magnetic resonance imaging or computed tomography. However, this methodology has several sources of error related to the medical imaging acquisition, segmentation and reverse engineering process, tessellation, and the selected additive manufacturing technique. Therefore, this paper proposes a new design strategy that avoids medical image segmentation. To demonstrate its feasibility, a patient-specific coronary stent was designed and manufactured based on slices similar to medical images. Using artificial intelligence algorithms and numerical methods, the ellipse that best fit the patient’s artery was obtained, and finally customized stent was generated from the parameterization of unit cells, demonstrating that it is possible to semi-automate the design of biodevices by removing some sources of error inh
erent to the conventional workflow.
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