@article{paperid:1094455,
author = {Ashkan Sedigh and Akbarzadeh Totonchi, Mohammad Reza and Ryan Tomlison},
title = {Optimizing Mineralization of Bioprinted Bone Utilizing Type-2 Fuzzy Systems},
journal = {Biophysica},
year = {2022},
volume = {2},
number = {4},
month = {October},
issn = {2673-4125},
pages = {400--411},
numpages = {11},
keywords = {Bioprinting is an emerging tissue engineering method used to generate cell-laden scaffolds with high spatial resolution. Bioprinting parameters; such as pressure; nozzle size; and speed; highly influence the quality of the bioprinted construct. Moreover; cell suspension density and other critical biological parameters directly impact the biological function. Therefore; an approximation model that can be used to find the optimal bioprinting parameter settings for bioprinted constructs is highly desirable. Here; we propose a type-2 fuzzy model to handle the uncertainty and imprecision in the approximation model. Specifically; we focus on the biological parameters; such as the culture period; that can be used to maximize the output value (mineralization volume 21.8 mm3 with the same culture period of 21 days). We have also implemented a type-1 fuzzy model and compared the results with the proposed type-2 fuzzy model using two levels of uncertainty. We hypothesize that the type-2 fuzzy model may be preferred in biological systems due to the inherent vagueness and imprecision of the input data. Our numerical results confirm this hypothesis. More specifically; the type-2 fuzzy model with a high uncertainty boundary (30%) is superior to type-1 and type-2 fuzzy systems with low uncertainty boundaries in the overall output approximation error for bone bioprinting inputs.},
}

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