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Review of Machine Learning for Bioimpedance Tomography in Regenerative Medicine

Review of Machine Learning for Bioimpedance Tomography in Regenerative Medicine

Zhe Liu, Zhou Chen, Yunjie Yang
Copyright: © 2023 |Pages: 22
ISBN13: 9781668450925|ISBN10: 1668450925|ISBN13 Softcover: 9781668450932|EISBN13: 9781668450949
DOI: 10.4018/978-1-6684-5092-5.ch013
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MLA

Liu, Zhe, et al. "Review of Machine Learning for Bioimpedance Tomography in Regenerative Medicine." Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems, edited by Thomas M. Connolly, et al., IGI Global, 2023, pp. 271-292. https://doi.org/10.4018/978-1-6684-5092-5.ch013

APA

Liu, Z., Chen, Z., & Yang, Y. (2023). Review of Machine Learning for Bioimpedance Tomography in Regenerative Medicine. In T. Connolly, P. Papadopoulos, & M. Soflano (Eds.), Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems (pp. 271-292). IGI Global. https://doi.org/10.4018/978-1-6684-5092-5.ch013

Chicago

Liu, Zhe, Zhou Chen, and Yunjie Yang. "Review of Machine Learning for Bioimpedance Tomography in Regenerative Medicine." In Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems, edited by Thomas M. Connolly, Petros Papadopoulos, and Mario Soflano, 271-292. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-5092-5.ch013

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Abstract

Monitoring cell growth and activities is crucial for regenerative medicine. Although optical imaging can provide high resolution, such methods are limited by the penetration depth. Bioimpedance tomography is an alternative way as it can overcome the penetration problem and possess the advantages of non-radiative, non-destructive, and high temporal resolution. In addition, with the rapid development of machine leaning, learning-based bioimpedance tomography is gradually introduced into regenerative medicine and demonstrates powerful potential. This chapter aims to provide an overview of the state-of-the-art machine learning methods of bioimpedance tomography in regenerative medicine while offering perspectives for future research directions. This chapter first summarizes the electrical properties of tissues and the principle of electrical impedance tomography (EIT) then extensively reviews the recent progress on learning-based single-modal and multi-modal imaging methods of EIT for regenerative medicine. Finally, promising future research directions are discussed.

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