Bioprinting is an emerging tissue engineering method used to generate cell-laden scaffolds with high spatial resolution. Bioprinted vascularized bone grafts are a potential application of this technology that would meet a critical clinical need, since current approaches to volumetric bone repair have significant limitations. However, generation of vascular networks suitable for bioprinting is challenging. Here, we propose a novel Q-learning approach to quickly generate 3D vascular networks within patient-specific bone geometry that are optimized for bioprinting. First, the inlet and outlet locations are specified and the scenario is modeled using a grid world for initial agent training. Next, the path planned in the grid world environment is converted to a Bezier curve, which is then used to generate the final 3D vascularized bone model. The vessels generated using this procedure have minimal tortuosity, which increases the likelihood of successful bioprinting. Furthermore, the ability to specify inlet and outlet position is necessary for both surgical feasibility as well as generation of more complex vascular networks. In total, this study demonstrates the reliability of our reinforcement learning method for automated generation of 3D vascular networks within patient-specific geometry that can be used for bioprinting vascularized bone grafts.
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, have a large influence on the quality of the bioprinted construct. Moreover, cell suspension density, cell culture period, and other critical biological parameters directly impact the biological function of the final product. Therefore, an approximation model that can be used to find the values of bioprinting parameters that will result in optimal bioprinted constructs is highly desired. Here, we propose type-1 and type-2 fuzzy systems to handle the uncertainty and imprecision in optimizing the input values. Specifically, we focus on the biological parameters, such as culture period, that can be used to maximize the output value (mineralization volume). To achieve a more accurate approximation, we have compared a type-2 fuzzy system with a type-1 fuzzy system using two levels of uncertainty. We hypothesized that type-2 fuzzy systems may e preferred in biological systems, due to the inherent vagueness and imprecision of the input data. Here, our results demonstrate that the type-2 fuzzy system with a high uncertainty boundary (30%) is superior to type-1 and type-2 with low uncertainty boundary fuzzy systems in the overall output approximation error for bone bioprinting inputs.
We used calibrated 2D images uploaded by patients to an online platform to generate a 3D digital model of the limb. This was used to 3D print a splint. This method of 3D printing of splints was used for two patients who were not able to visit the hospital in person due to restrictions placed by the COVID-19 pandemic. Both patients were satisfied with the splint. We feel that this technology could be used to offer additional options to conventional splinting that allows contactless splint fitting. Level of Evidence: Level V (Therapeutic)
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