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- W4386183287 abstract "Regenerating the failing organs for a complete or partial restoration of function is the most anticipated accomplishment to address several medical conditions. However, the feasibility of organ regeneration to a fully-functional solid organ is still in infancy considering the clinical realization due to the unmet challenges such as poor understanding of complex regeneration mechanism, delayed in vitro expansion of exogenous progenitor cells, lack of vasculature and technical limitations in evaluating the safety and efficacy of in-situ regeneration. Artificial intelligence (AI) -supported machine learning (ML) approaches are gaining promise in regenerative medicine to meet these challenges via trained predictive computer algorithms and implantable machine interfaces. Contributions of AI in developing structural blueprints for organ engineering, predicting and understanding endogenous regenerative pathways, assisting bioscaffold engineering, and enhancing the efficacy of induced pluripotent stem cells (iPSC) therapy are discussed in this chapter. Big data mining and ML algorithms can evolve the field of regenerative medicine and ease the clinical realization of approaches." @default.
- W4386183287 created "2023-08-26" @default.
- W4386183287 creator A5055604130 @default.
- W4386183287 creator A5075738966 @default.
- W4386183287 date "2023-01-01" @default.
- W4386183287 modified "2023-09-25" @default.
- W4386183287 title "Prospects of artificial intelligence in regeneration and repair of organs" @default.
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- W4386183287 doi "https://doi.org/10.1016/b978-0-443-18498-7.00013-2" @default.
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